Merge remote-tracking branch 'origin/main' into feat/user-onboarding

.release-please-manifest.json

@@ -1,3 +1,3 @@
 {
-    ".": "1.33.0"
+    ".": "1.34.0"
 }

CHANGELOG.md

@@ -1,5 +1,33 @@
 # Changelog
 
+## 1.34.0 (2026-06-03)
+
+<!-- Release notes generated using configuration in .github/release.yml at main -->
+
+## What's Changed
+### Features
+* feat: add mcp guides for various topic and stages for bot building by @a6kme in https://github.com/dograh-hq/dograh/pull/380
+* feat: allow overriding base URL of OpenAI STT and TTS by @developer603 in https://github.com/dograh-hq/dograh/pull/377
+* feat: add Azure AI multi-provider support (TTS, STT, Embeddings, Realtime) by @vishaldhateria in https://github.com/dograh-hq/dograh/pull/381
+### Bug Fixes
+* fix: support object and array parameters in custom HTTP tools by @mvanhorn in https://github.com/dograh-hq/dograh/pull/373
+* fix(telephony): resolve transfer context via call-sid index instead of KEYS scan by @shiminshen in https://github.com/dograh-hq/dograh/pull/387
+* fix(webrtc): enforce embed allowed-domain policy on public signaling websocket by @shiminshen in https://github.com/dograh-hq/dograh/pull/388
+* fix: use runtime BACKEND_URL for proxying by @a6kme in https://github.com/dograh-hq/dograh/pull/411
+* fix: add CORS preflight handler and ACAO header for embed config endpoint by @nuthalapativarun in https://github.com/dograh-hq/dograh/pull/403
+### Other Changes
+* Add Sarvam LLM, update Sarvam STT models, expose usage_info on run detail by @abhaybabbar in https://github.com/dograh-hq/dograh/pull/351
+* fix: make email lookup case-insensitive in get_user_by_email by @developer603 in https://github.com/dograh-hq/dograh/pull/397
+
+## New Contributors
+* @abhaybabbar made their first contribution in https://github.com/dograh-hq/dograh/pull/351
+* @mvanhorn made their first contribution in https://github.com/dograh-hq/dograh/pull/373
+* @developer603 made their first contribution in https://github.com/dograh-hq/dograh/pull/377
+* @vishaldhateria made their first contribution in https://github.com/dograh-hq/dograh/pull/381
+* @shiminshen made their first contribution in https://github.com/dograh-hq/dograh/pull/387
+
+**Full Changelog**: https://github.com/dograh-hq/dograh/compare/dograh-v1.33.0...dograh-v1.34.0
+
 ## [1.33.0](https://github.com/dograh-hq/dograh/compare/dograh-v1.32.0...dograh-v1.33.0) (2026-05-31)
 
 

README.md

@@ -75,13 +75,13 @@ An honest comparison on the axes that matter most to teams evaluating voice AI p
 ##### Download and setup Dograh on your Local Machine
 
 > **Note**
-> We collect anonymous usage data to improve the product. You can opt out by setting the `ENABLE_TELEMETRY` to `false` in the below command.
+> We collect anonymous usage data to improve the product. You can opt out by setting `ENABLE_TELEMETRY=false` before running the startup script.
 
 > **Note**
 > If you wish to run the platform on a remote server instead, checkout our [Documentation](https://docs.dograh.com/deployment/docker#option-2:-remote-server-deployment)
 
 ```bash
-curl -o docker-compose.yaml https://raw.githubusercontent.com/dograh-hq/dograh/main/docker-compose.yaml && REGISTRY=ghcr.io/dograh-hq ENABLE_TELEMETRY=true docker compose up --pull always
+curl -o docker-compose.yaml https://raw.githubusercontent.com/dograh-hq/dograh/main/docker-compose.yaml && curl -o start_docker.sh https://raw.githubusercontent.com/dograh-hq/dograh/main/scripts/start_docker.sh && chmod +x start_docker.sh && ./start_docker.sh
 ```
 
 > **Note**

api/alembic/versions/2159d4ac431a_added_quota_tables.py

@@ -18,6 +18,9 @@ branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 
 
+DEPRECATED_QUOTA_COMMENT = "Deprecated. MPS owns quota and credit ledger state."
+
+
 def upgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     # 1) Create the `quota_type` enum *before* we add the column that references it.
@@ -34,7 +37,12 @@ def upgrade() -> None:
         sa.Column("organization_id", sa.Integer(), nullable=False),
         sa.Column("period_start", sa.DateTime(), nullable=False),
         sa.Column("period_end", sa.DateTime(), nullable=False),
-        sa.Column("quota_dograh_tokens", sa.Integer(), nullable=False),
+        sa.Column(
+            "quota_dograh_tokens",
+            sa.Integer(),
+            nullable=False,
+            comment=DEPRECATED_QUOTA_COMMENT,
+        ),
         sa.Column("used_dograh_tokens", sa.Integer(), nullable=False),
         sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
         sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True),
@@ -63,7 +71,11 @@ def upgrade() -> None:
     op.add_column(
         "organizations",
         sa.Column(
-            "quota_type", quota_type_enum, nullable=False, server_default="monthly"
+            "quota_type",
+            quota_type_enum,
+            nullable=False,
+            server_default="monthly",
+            comment=DEPRECATED_QUOTA_COMMENT,
         ),
     )
     op.add_column(
@@ -73,6 +85,7 @@ def upgrade() -> None:
             sa.Integer(),
             nullable=False,
             server_default=sa.text("0"),
+            comment=DEPRECATED_QUOTA_COMMENT,
         ),
     )
     op.add_column(
@@ -82,10 +95,17 @@ def upgrade() -> None:
             sa.Integer(),
             nullable=False,
             server_default=sa.text("LEAST(EXTRACT(DAY FROM CURRENT_DATE)::int, 28)"),
+            comment=DEPRECATED_QUOTA_COMMENT,
         ),
     )
     op.add_column(
-        "organizations", sa.Column("quota_start_date", sa.DateTime(), nullable=True)
+        "organizations",
+        sa.Column(
+            "quota_start_date",
+            sa.DateTime(),
+            nullable=True,
+            comment=DEPRECATED_QUOTA_COMMENT,
+        ),
     )
     op.add_column(
         "organizations",
@@ -94,6 +114,7 @@ def upgrade() -> None:
             sa.Boolean(),
             nullable=False,
             server_default=sa.text("false"),
+            comment=DEPRECATED_QUOTA_COMMENT,
         ),
     )
     # ### end Alembic commands ###

api/alembic/versions/384be6596b36_make_email_case_insensitive.py

@@ -5,28 +5,38 @@ Revises: 6bd9f67ec994
 Create Date: 2026-06-02 07:58:00.002359
 
 """
+
 from typing import Sequence, Union
 
-from alembic import op
 import sqlalchemy as sa
-
+from alembic import op
 
 # revision identifiers, used by Alembic.
-revision: str = '384be6596b36'
-down_revision: Union[str, None] = '6bd9f67ec994'
+revision: str = "384be6596b36"
+down_revision: Union[str, None] = "6bd9f67ec994"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 
 
 def upgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index(op.f('ix_users_email'), table_name='users')
-    op.create_index('ix_users_email_lower', 'users', [sa.literal_column('lower(email)')], unique=True, postgresql_where=sa.text('email IS NOT NULL'))
+    op.drop_index(op.f("ix_users_email"), table_name="users")
+    op.create_index(
+        "ix_users_email_lower",
+        "users",
+        [sa.literal_column("lower(email)")],
+        unique=True,
+        postgresql_where=sa.text("email IS NOT NULL"),
+    )
     # ### end Alembic commands ###
 
 
 def downgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index('ix_users_email_lower', table_name='users', postgresql_where=sa.text('email IS NOT NULL'))
-    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.drop_index(
+        "ix_users_email_lower",
+        table_name="users",
+        postgresql_where=sa.text("email IS NOT NULL"),
+    )
+    op.create_index(op.f("ix_users_email"), "users", ["email"], unique=True)
     # ### end Alembic commands ###

api/alembic/versions/c425d3445750_add_columns_in_usage_table.py

@@ -18,6 +18,9 @@ branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 
 
+DEPRECATED_QUOTA_COMMENT = "Deprecated. MPS owns quota and credit ledger state."
+
+
 def upgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     op.add_column(
@@ -26,7 +29,12 @@ def upgrade() -> None:
     )
     op.add_column(
         "organization_usage_cycles",
-        sa.Column("quota_amount_usd", sa.Float(), nullable=True),
+        sa.Column(
+            "quota_amount_usd",
+            sa.Float(),
+            nullable=True,
+            comment=DEPRECATED_QUOTA_COMMENT,
+        ),
     )
     # ### end Alembic commands ###
 

api/app.py

@@ -117,6 +117,15 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+
+def _add_public_embed_cors_middleware() -> None:
+    from api.routes.public_embed import PublicEmbedCORSMiddleware
+
+    app.add_middleware(PublicEmbedCORSMiddleware, api_prefix=API_PREFIX)
+
+
+_add_public_embed_cors_middleware()
+
 api_router = APIRouter()
 
 # include subrouters here

api/db/campaign_client.py

@@ -9,6 +9,7 @@ from api.db.base_client import BaseDBClient
 from api.db.filters import apply_workflow_run_filters, get_workflow_run_order_clause
 from api.db.models import CampaignModel, QueuedRunModel, WorkflowRunModel
 from api.schemas.workflow import WorkflowRunResponseSchema
+from api.services.workflow.run_usage_response import format_public_cost_info
 
 
 class CampaignClient(BaseDBClient):
@@ -215,26 +216,9 @@ class CampaignClient(BaseDBClient):
                         "is_completed": run.is_completed,
                         "recording_url": run.recording_url,
                         "transcript_url": run.transcript_url,
-                        "cost_info": {
-                            "dograh_token_usage": (
-                                run.cost_info.get("dograh_token_usage")
-                                if run.cost_info
-                                and "dograh_token_usage" in run.cost_info
-                                else round(
-                                    float(run.cost_info.get("total_cost_usd", 0)) * 100,
-                                    2,
-                                )
-                                if run.cost_info and "total_cost_usd" in run.cost_info
-                                else 0
-                            ),
-                            "call_duration_seconds": int(
-                                round(run.cost_info.get("call_duration_seconds") or 0)
-                            )
-                            if run.cost_info
-                            else None,
-                        }
-                        if run.cost_info
-                        else None,
+                        "cost_info": format_public_cost_info(
+                            run.cost_info, run.usage_info
+                        ),
                         "definition_id": run.definition_id,
                         "initial_context": run.initial_context,
                         "gathered_context": run.gathered_context,
@@ -662,7 +646,7 @@ class CampaignClient(BaseDBClient):
         async with self.async_session() as session:
             conditions = [
                 WorkflowRunModel.is_completed.is_(True),
-                WorkflowRunModel.cost_info["call_duration_seconds"]
+                WorkflowRunModel.usage_info["call_duration_seconds"]
                 .as_string()
                 .isnot(None),
             ]
@@ -685,6 +669,7 @@ class CampaignClient(BaseDBClient):
                     WorkflowRunModel.initial_context,
                     WorkflowRunModel.gathered_context,
                     WorkflowRunModel.cost_info,
+                    WorkflowRunModel.usage_info,
                     WorkflowRunModel.public_access_token,
                 )
                 .where(*conditions)

api/db/db_client.py

@@ -53,7 +53,7 @@ class DBClient(
     - UserClient: handles user and user configuration operations
     - OrganizationClient: handles organization operations
     - OrganizationConfigurationClient: handles organization configuration operations
-    - OrganizationUsageClient: handles organization usage and quota operations
+    - OrganizationUsageClient: handles organization usage reporting aggregates
     - IntegrationClient: handles integration operations
     - WorkflowTemplateClient: handles workflow template operations
     - CampaignClient: handles campaign operations

api/db/filters.py

@@ -25,7 +25,7 @@ def get_workflow_run_order_clause(
     """
     # Determine sort column
     if sort_by == "duration":
-        sort_column = WorkflowRunModel.cost_info.op("->>")(
+        sort_column = WorkflowRunModel.usage_info.op("->>")(
             "call_duration_seconds"
         ).cast(Float)
     else:
@@ -43,7 +43,7 @@ def get_workflow_run_order_clause(
 ATTRIBUTE_FIELD_MAPPING = {
     "dateRange": "created_at",
     "dispositionCode": "gathered_context.mapped_call_disposition",
-    "duration": "cost_info.call_duration_seconds",
+    "duration": "usage_info.call_duration_seconds",
     "status": "is_completed",
     "tokenUsage": "cost_info.total_cost_usd",
     "runId": "id",
@@ -208,7 +208,7 @@ def apply_workflow_run_filters(
                 min_val = value.get("min")
                 max_val = value.get("max")
 
-                if field == "cost_info.call_duration_seconds":
+                if field == "usage_info.call_duration_seconds":
                     # Use ->> operator for compatibility with all PostgreSQL versions
                     # (subscript [] only works in PostgreSQL 14+)
                     duration_text = cast(WorkflowRunModel.usage_info, JSONB).op("->>")(

api/db/models.py

@@ -97,22 +97,44 @@ class OrganizationModel(Base):
     provider_id = Column(String, unique=True, index=True, nullable=False)
     created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(UTC))
 
-    # Quota fields
+    # Deprecated: MPS owns quota and credit ledger state.
     quota_type = Column(
         Enum("monthly", "annual", name="quota_type"),
         nullable=False,
         default="monthly",
         server_default=text("'monthly'::quota_type"),
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
     )
     quota_dograh_tokens = Column(
-        Integer, nullable=False, default=0, server_default=text("0")
+        Integer,
+        nullable=False,
+        default=0,
+        server_default=text("0"),
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
     )
     quota_reset_day = Column(
-        Integer, nullable=False, default=1, server_default=text("1")
-    )  # 1-28, only for monthly
-    quota_start_date = Column(DateTime(timezone=True), nullable=True)  # Only for annual
+        Integer,
+        nullable=False,
+        default=1,
+        server_default=text("1"),
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
+    )
+    quota_start_date = Column(
+        DateTime(timezone=True),
+        nullable=True,
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
+    )
     quota_enabled = Column(
-        Boolean, nullable=False, default=False, server_default=text("false")
+        Boolean,
+        nullable=False,
+        default=False,
+        server_default=text("false"),
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
     )
 
     price_per_second_usd = Column(Float, nullable=True)
@@ -593,8 +615,9 @@ class WorkflowRunTextSessionModel(Base):
 
 class OrganizationUsageCycleModel(Base):
     """
-    This model is used to track the usage of Dograh tokens for an organization for a given usage
-    cycle.
+    This model is used to track reporting aggregates for an organization for a given
+    usage cycle. Quota fields on this model are deprecated; MPS owns quota and
+    credit ledger state.
     """
 
     __tablename__ = "organization_usage_cycles"
@@ -603,14 +626,24 @@ class OrganizationUsageCycleModel(Base):
     organization_id = Column(Integer, ForeignKey("organizations.id"), nullable=False)
     period_start = Column(DateTime(timezone=True), nullable=False)
     period_end = Column(DateTime(timezone=True), nullable=False)
-    quota_dograh_tokens = Column(Integer, nullable=False)
+    quota_dograh_tokens = Column(
+        Integer,
+        nullable=False,
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
+    )
     used_dograh_tokens = Column(Float, nullable=False, default=0)
     total_duration_seconds = Column(
         Integer, nullable=False, default=0, server_default=text("0")
     )
     # New USD tracking fields
     used_amount_usd = Column(Float, nullable=True, default=0)
-    quota_amount_usd = Column(Float, nullable=True)
+    quota_amount_usd = Column(
+        Float,
+        nullable=True,
+        comment="Deprecated. MPS owns quota and credit ledger state.",
+        info={"deprecated": True},
+    )
     created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(UTC))
     updated_at = Column(
         DateTime(timezone=True),

api/db/organization_usage_client.py

@@ -10,6 +10,7 @@ from sqlalchemy.orm import joinedload
 from api.db.base_client import BaseDBClient
 from api.db.filters import apply_workflow_run_filters
 from api.db.models import (
+    OrganizationConfigurationModel,
     OrganizationModel,
     OrganizationUsageCycleModel,
     UserConfigurationModel,
@@ -17,11 +18,12 @@ from api.db.models import (
     WorkflowModel,
     WorkflowRunModel,
 )
-from api.schemas.user_configuration import UserConfiguration
+from api.enums import OrganizationConfigurationKey
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 
 
 class OrganizationUsageClient(BaseDBClient):
-    """Client for managing organization usage and quota operations."""
+    """Client for managing organization usage reporting aggregates."""
 
     async def get_or_create_current_cycle(
         self, organization_id: int, session=None
@@ -47,14 +49,7 @@ class OrganizationUsageClient(BaseDBClient):
         self, organization_id: int, session, commit: bool
     ) -> OrganizationUsageCycleModel:
         """Internal implementation for get_or_create_current_cycle."""
-        # Get organization to determine quota type
-        org_result = await session.execute(
-            select(OrganizationModel).where(OrganizationModel.id == organization_id)
-        )
-        org = org_result.scalar_one()
-
-        # Calculate current period
-        period_start, period_end = self._calculate_current_period(org)
+        period_start, period_end = self._calculate_current_period()
 
         # Try to get existing cycle
         cycle_result = await session.execute(
@@ -76,7 +71,8 @@ class OrganizationUsageClient(BaseDBClient):
             organization_id=organization_id,
             period_start=period_start,
             period_end=period_end,
-            quota_dograh_tokens=org.quota_dograh_tokens,
+            # Deprecated non-null column retained for historical schema compatibility.
+            quota_dograh_tokens=0,
         )
         # Handle concurrent inserts gracefully
         stmt = stmt.on_conflict_do_nothing(
@@ -100,95 +96,9 @@ class OrganizationUsageClient(BaseDBClient):
         )
         return cycle_result.scalar_one()
 
-    async def check_and_reserve_quota(
-        self, organization_id: int, estimated_tokens: int = 0
-    ) -> bool:
-        """
-        Check if organization has sufficient quota and optionally reserve tokens.
-        Returns True if quota is available, False otherwise.
-
-        This method is fully atomic and safe for concurrent access from multiple processes.
-        """
-        async with self.async_session() as session:
-            # Get organization
-            org_result = await session.execute(
-                select(OrganizationModel).where(OrganizationModel.id == organization_id)
-            )
-            org = org_result.scalar_one_or_none()
-
-            if not org or not org.quota_enabled:
-                # No quota enforcement if not enabled
-                return True
-
-            # Get or create current cycle within the same session/transaction
-            cycle = await self._get_or_create_current_cycle_impl(
-                organization_id, session, commit=False
-            )
-
-            # Atomic check and update with row-level lock
-            result = await session.execute(
-                select(OrganizationUsageCycleModel)
-                .where(
-                    and_(
-                        OrganizationUsageCycleModel.id == cycle.id,
-                        OrganizationUsageCycleModel.used_dograh_tokens
-                        + estimated_tokens
-                        <= OrganizationUsageCycleModel.quota_dograh_tokens,
-                    )
-                )
-                .with_for_update(skip_locked=False)
-            )
-
-            cycle_locked = result.scalar_one_or_none()
-            if cycle_locked:
-                # Update the usage atomically
-                cycle_locked.used_dograh_tokens += estimated_tokens
-                await session.commit()
-                return True
-
-            return False
-
-    async def update_usage_after_run(
-        self,
-        organization_id: int,
-        actual_tokens: float,
-        duration_seconds: float = 0,
-        charge_usd: float | None = None,
-    ) -> None:
-        """Update usage after a workflow run completes with actual token count and duration.
-
-        This method is fully atomic and safe for concurrent access from multiple processes.
-        """
-        async with self.async_session() as session:
-            # Get or create current cycle within the same session/transaction
-            cycle = await self._get_or_create_current_cycle_impl(
-                organization_id, session, commit=False
-            )
-
-            # Acquire a row-level lock for atomic update
-            result = await session.execute(
-                select(OrganizationUsageCycleModel)
-                .where(OrganizationUsageCycleModel.id == cycle.id)
-                .with_for_update(skip_locked=False)
-            )
-            cycle_locked = result.scalar_one()
-
-            # Update usage atomically
-            cycle_locked.used_dograh_tokens += actual_tokens
-            cycle_locked.total_duration_seconds += int(round(duration_seconds))
-
-            # Update USD amount if provided
-            if charge_usd is not None:
-                if cycle_locked.used_amount_usd is None:
-                    cycle_locked.used_amount_usd = 0
-                cycle_locked.used_amount_usd += charge_usd
-
-            await session.commit()
-
     async def get_current_usage(self, organization_id: int) -> dict:
-        """Get current period usage information."""
+        """Get current reporting-period usage information."""
         async with self.async_session() as session:
-            # Get organization
             org_result = await session.execute(
                 select(OrganizationModel).where(OrganizationModel.id == organization_id)
             )
@@ -199,42 +109,19 @@ class OrganizationUsageClient(BaseDBClient):
                 organization_id, session, commit=False
             )
 
-            # Calculate next refresh date
-            if org.quota_type == "monthly":
-                next_refresh = cycle.period_end + relativedelta(days=1)
-            else:  # annual
-                next_refresh = cycle.period_end + relativedelta(days=1)
-
             result = {
                 "period_start": cycle.period_start.isoformat(),
                 "period_end": cycle.period_end.isoformat(),
                 "used_dograh_tokens": cycle.used_dograh_tokens,
-                "quota_dograh_tokens": cycle.quota_dograh_tokens,
-                "percentage_used": (
-                    round(
-                        (cycle.used_dograh_tokens / cycle.quota_dograh_tokens) * 100, 2
-                    )
-                    if cycle.quota_dograh_tokens > 0
-                    else 0
-                ),
-                "next_refresh_date": next_refresh.date().isoformat(),
-                "quota_enabled": org.quota_enabled,
                 "total_duration_seconds": cycle.total_duration_seconds,
             }
 
             # Add USD fields if organization has pricing
             if org.price_per_second_usd is not None:
                 result["used_amount_usd"] = cycle.used_amount_usd or 0
-                result["quota_amount_usd"] = cycle.quota_amount_usd
                 result["currency"] = "USD"
                 result["price_per_second_usd"] = org.price_per_second_usd
 
-                # Calculate percentage based on USD if available
-                if cycle.quota_amount_usd and cycle.quota_amount_usd > 0:
-                    result["percentage_used"] = round(
-                        ((cycle.used_amount_usd or 0) / cycle.quota_amount_usd) * 100, 2
-                    )
-
             return result
 
     async def get_usage_history(
@@ -254,7 +141,7 @@ class OrganizationUsageClient(BaseDBClient):
                 .join(UserModel, WorkflowModel.user_id == UserModel.id)
                 .where(
                     UserModel.selected_organization_id == organization_id,
-                    WorkflowRunModel.cost_info.isnot(None),
+                    WorkflowRunModel.usage_info.isnot(None),
                 )
                 .order_by(WorkflowRunModel.created_at.desc())
             )
@@ -307,19 +194,8 @@ class OrganizationUsageClient(BaseDBClient):
             total_tokens = 0
             total_duration_seconds = 0
             for run in runs:
-                if run.cost_info:
-                    # Try to get dograh_token_usage first (new format)
-                    dograh_tokens = run.cost_info.get("dograh_token_usage", 0)
-                    # If not present, calculate from total_cost_usd (old format)
-                    if dograh_tokens == 0 and "total_cost_usd" in run.cost_info:
-                        dograh_tokens = round(
-                            float(run.cost_info["total_cost_usd"]) * 100, 2
-                        )
-                    # Get call duration
-                    call_duration = run.cost_info.get("call_duration_seconds", 0)
-                else:
-                    dograh_tokens = 0
-                    call_duration = 0
+                dograh_tokens = 0
+                call_duration = (run.usage_info or {}).get("call_duration_seconds", 0)
                 total_tokens += dograh_tokens
                 total_duration_seconds += int(round(call_duration))
 
@@ -393,13 +269,14 @@ class OrganizationUsageClient(BaseDBClient):
                     WorkflowRunModel.initial_context,
                     WorkflowRunModel.gathered_context,
                     WorkflowRunModel.cost_info,
+                    WorkflowRunModel.usage_info,
                     WorkflowRunModel.public_access_token,
                 )
                 .join(WorkflowModel, WorkflowRunModel.workflow_id == WorkflowModel.id)
                 .join(UserModel, WorkflowModel.user_id == UserModel.id)
                 .where(
                     UserModel.selected_organization_id == organization_id,
-                    WorkflowRunModel.cost_info.isnot(None),
+                    WorkflowRunModel.usage_info.isnot(None),
                 )
                 .order_by(WorkflowRunModel.created_at.desc())
             )
@@ -440,8 +317,29 @@ class OrganizationUsageClient(BaseDBClient):
         """Get daily usage breakdown for an organization with pricing."""
 
         async with self.async_session() as session:
-            # Get user timezone if user_id is provided
+            # Get org timezone preference first, then fall back to legacy user config.
             user_timezone = "UTC"  # Default timezone
+            pref_result = await session.execute(
+                select(OrganizationConfigurationModel).where(
+                    OrganizationConfigurationModel.organization_id == organization_id,
+                    OrganizationConfigurationModel.key.in_(
+                        [
+                            OrganizationConfigurationKey.ORGANIZATION_PREFERENCES.value,
+                            OrganizationConfigurationKey.MODEL_CONFIGURATION_PREFERENCES.value,
+                        ]
+                    ),
+                )
+            )
+            pref_rows = pref_result.scalars().all()
+            pref_by_key = {pref.key: pref for pref in pref_rows}
+            pref_obj = pref_by_key.get(
+                OrganizationConfigurationKey.ORGANIZATION_PREFERENCES.value
+            ) or pref_by_key.get(
+                OrganizationConfigurationKey.MODEL_CONFIGURATION_PREFERENCES.value
+            )
+            if pref_obj and pref_obj.value:
+                user_timezone = pref_obj.value.get("timezone") or user_timezone
+
             if user_id:
                 config_result = await session.execute(
                     select(UserConfigurationModel).where(
@@ -450,11 +348,11 @@ class OrganizationUsageClient(BaseDBClient):
                 )
                 config_obj = config_result.scalar_one_or_none()
                 if config_obj and config_obj.configuration:
-                    user_config = UserConfiguration.model_validate(
+                    effective_config = EffectiveAIModelConfiguration.model_validate(
                         config_obj.configuration
                     )
-                    if user_config.timezone:
-                        user_timezone = user_config.timezone
+                    if effective_config.timezone and user_timezone == "UTC":
+                        user_timezone = effective_config.timezone
 
             # Validate timezone string
             try:
@@ -473,7 +371,7 @@ class OrganizationUsageClient(BaseDBClient):
                 select(
                     date_expr.label("date"),
                     func.sum(
-                        WorkflowRunModel.cost_info["call_duration_seconds"].as_float()
+                        WorkflowRunModel.usage_info["call_duration_seconds"].as_float()
                     ).label("total_seconds"),
                     func.count(WorkflowRunModel.id).label("call_count"),
                 )
@@ -522,83 +420,11 @@ class OrganizationUsageClient(BaseDBClient):
                 "currency": "USD",
             }
 
-    async def update_organization_quota(
-        self,
-        organization_id: int,
-        quota_type: str,
-        quota_dograh_tokens: int,
-        quota_reset_day: Optional[int] = None,
-        quota_start_date: Optional[datetime] = None,
-    ) -> OrganizationModel:
-        """Update organization quota settings."""
-        async with self.async_session() as session:
-            result = await session.execute(
-                select(OrganizationModel).where(OrganizationModel.id == organization_id)
-            )
-            org = result.scalar_one()
-
-            org.quota_type = quota_type
-            org.quota_dograh_tokens = quota_dograh_tokens
-            org.quota_enabled = True
-
-            if quota_type == "monthly" and quota_reset_day:
-                org.quota_reset_day = quota_reset_day
-            elif quota_type == "annual" and quota_start_date:
-                org.quota_start_date = quota_start_date
-
-            await session.commit()
-            await session.refresh(org)
-            return org
-
-    def _calculate_current_period(
-        self, org: OrganizationModel
-    ) -> tuple[datetime, datetime]:
-        """Calculate the current billing period based on organization settings."""
+    def _calculate_current_period(self) -> tuple[datetime, datetime]:
+        """Calculate the current calendar-month reporting period."""
         now = datetime.now(timezone.utc)
 
-        if org.quota_type == "monthly":
-            # Find the start of the current billing month
-            reset_day = org.quota_reset_day
-
-            # Handle month boundaries
-            if now.day >= reset_day:
-                period_start = now.replace(
-                    day=reset_day, hour=0, minute=0, second=0, microsecond=0
-                )
-            else:
-                # Previous month
-                period_start = (now - relativedelta(months=1)).replace(
-                    day=reset_day, hour=0, minute=0, second=0, microsecond=0
-                )
-
-            # End is one month later minus 1 second
-            period_end = (
-                period_start + relativedelta(months=1) - relativedelta(seconds=1)
-            )
-
-        else:  # annual
-            if not org.quota_start_date:
-                # Default to calendar year
-                period_start = now.replace(
-                    month=1, day=1, hour=0, minute=0, second=0, microsecond=0
-                )
-                period_end = (
-                    period_start + relativedelta(years=1) - relativedelta(seconds=1)
-                )
-            else:
-                # Find current annual period
-                start_date = org.quota_start_date.replace(tzinfo=timezone.utc)
-                years_diff = now.year - start_date.year
-
-                # Adjust for whether we've passed the anniversary
-                if now.month < start_date.month or (
-                    now.month == start_date.month and now.day < start_date.day
-                ):
-                    years_diff -= 1
-
-                period_start = start_date + relativedelta(years=years_diff)
-                period_end = (
-                    period_start + relativedelta(years=1) - relativedelta(seconds=1)
-                )
+        period_start = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
+        period_end = period_start + relativedelta(months=1) - relativedelta(seconds=1)
 
         return period_start, period_end

api/db/user_client.py

@@ -8,7 +8,7 @@ from sqlalchemy.future import select
 
 from api.db.base_client import BaseDBClient
 from api.db.models import UserConfigurationModel, UserModel
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 
 
 class UserClient(BaseDBClient):
@@ -65,7 +65,9 @@ class UserClient(BaseDBClient):
             )
             return result.scalars().first()
 
-    async def get_user_configurations(self, user_id: int) -> UserConfiguration:
+    async def get_user_configurations(
+        self, user_id: int
+    ) -> EffectiveAIModelConfiguration:
         async with self.async_session() as session:
             result = await session.execute(
                 select(UserConfigurationModel).where(
@@ -74,10 +76,10 @@ class UserClient(BaseDBClient):
             )
             configuration_obj = result.scalars().first()
             if not configuration_obj:
-                return UserConfiguration()
+                return EffectiveAIModelConfiguration()
 
             try:
-                return UserConfiguration.model_validate(
+                return EffectiveAIModelConfiguration.model_validate(
                     {
                         **configuration_obj.configuration,
                         "last_validated_at": configuration_obj.last_validated_at,
@@ -90,11 +92,11 @@ class UserClient(BaseDBClient):
                     f"Failed to validate user configuration for user {user_id}: {e}. "
                     "Returning default configuration."
                 )
-                return UserConfiguration()
+                return EffectiveAIModelConfiguration()
 
     async def update_user_configuration(
-        self, user_id: int, configuration: UserConfiguration
-    ) -> UserConfiguration:
+        self, user_id: int, configuration: EffectiveAIModelConfiguration
+    ) -> EffectiveAIModelConfiguration:
         async with self.async_session() as session:
             result = await session.execute(
                 select(UserConfigurationModel).where(
@@ -115,7 +117,9 @@ class UserClient(BaseDBClient):
                 await session.rollback()
                 raise e
             await session.refresh(configuration_obj)
-        return UserConfiguration.model_validate(configuration_obj.configuration)
+        return EffectiveAIModelConfiguration.model_validate(
+            configuration_obj.configuration
+        )
 
     async def update_user_configuration_last_validated_at(self, user_id: int) -> None:
         async with self.async_session() as session:

api/db/workflow_run_client.py

@@ -16,6 +16,7 @@ from api.db.models import (
 )
 from api.enums import CallType, StorageBackend
 from api.schemas.workflow import WorkflowRunResponseSchema
+from api.services.workflow.run_usage_response import format_public_cost_info
 
 
 class WorkflowRunClient(BaseDBClient):
@@ -312,26 +313,9 @@ class WorkflowRunClient(BaseDBClient):
                         "is_completed": run.is_completed,
                         "recording_url": run.recording_url,
                         "transcript_url": run.transcript_url,
-                        "cost_info": {
-                            "dograh_token_usage": (
-                                run.cost_info.get("dograh_token_usage")
-                                if run.cost_info
-                                and "dograh_token_usage" in run.cost_info
-                                else round(
-                                    float(run.cost_info.get("total_cost_usd", 0)) * 100,
-                                    2,
-                                )
-                                if run.cost_info and "total_cost_usd" in run.cost_info
-                                else 0
-                            ),
-                            "call_duration_seconds": int(
-                                round(run.cost_info.get("call_duration_seconds") or 0)
-                            )
-                            if run.cost_info
-                            else None,
-                        }
-                        if run.cost_info
-                        else None,
+                        "cost_info": format_public_cost_info(
+                            run.cost_info, run.usage_info
+                        ),
                         "definition_id": run.definition_id,
                         "initial_context": run.initial_context,
                         "gathered_context": run.gathered_context,

api/enums.py

@@ -89,6 +89,11 @@ class OrganizationConfigurationKey(Enum):
     LANGFUSE_CREDENTIALS = (
         "LANGFUSE_CREDENTIALS"  # Org-level Langfuse tracing credentials
     )
+    MODEL_CONFIGURATION_V2 = (
+        "MODEL_CONFIGURATION_V2"  # Org-level v2 AI model configuration
+    )
+    ORGANIZATION_PREFERENCES = "ORGANIZATION_PREFERENCES"  # Org-level defaults such as timezone/test call number
+    MODEL_CONFIGURATION_PREFERENCES = "MODEL_CONFIGURATION_PREFERENCES"  # Deprecated; read fallback for old org preferences
 
 
 class WorkflowStatus(Enum):

api/pyproject.toml

@@ -1,5 +1,5 @@
 [project]
 name = "dograh-api"
-version = "1.33.0"
+version = "1.34.0"
 description = "Backend API for Dograh voice AI platform"
 requires-python = ">=3.13,<3.14"

api/routes/agent_stream.py

@@ -22,7 +22,7 @@ from starlette.websockets import WebSocketDisconnect
 
 from api.db import db_client
 from api.enums import CallType, WorkflowRunState
-from api.services.quota_service import check_dograh_quota_by_user_id
+from api.services.quota_service import authorize_workflow_run_start
 from api.services.telephony import registry as telephony_registry
 
 router = APIRouter(prefix="/agent-stream")
@@ -67,19 +67,6 @@ async def agent_stream_websocket(
         await websocket.close(code=1008, reason="Workflow not found")
         return
 
-    quota_result = await check_dograh_quota_by_user_id(
-        workflow.user_id, workflow_id=workflow.id
-    )
-    if not quota_result.has_quota:
-        logger.warning(
-            f"agent-stream quota exceeded for user {workflow.user_id}: "
-            f"{quota_result.error_message}"
-        )
-        await websocket.close(
-            code=1008, reason=quota_result.error_message or "Quota exceeded"
-        )
-        return
-
     numeric_suffix = int(str(uuid.uuid4()).replace("-", "")[:8], 16) % 100000000
     workflow_run_name = f"WR-AGS-{numeric_suffix:08d}"
     call_id = params.get("callId") or params.get("CallSid")
@@ -108,6 +95,20 @@ async def agent_stream_websocket(
     set_current_run_id(workflow_run.id)
     set_current_org_id(workflow.organization_id)
 
+    quota_result = await authorize_workflow_run_start(
+        workflow_id=workflow.id,
+        workflow_run_id=workflow_run.id,
+    )
+    if not quota_result.has_quota:
+        logger.warning(
+            f"agent-stream quota exceeded for user {workflow.user_id}: "
+            f"{quota_result.error_message}"
+        )
+        await websocket.close(
+            code=1008, reason=quota_result.error_message or "Quota exceeded"
+        )
+        return
+
     await db_client.update_workflow_run(
         run_id=workflow_run.id, state=WorkflowRunState.RUNNING.value
     )

api/routes/auth.py

@@ -3,9 +3,12 @@ from loguru import logger
 
 from api.db import db_client
 from api.db.models import UserModel
-from api.enums import PostHogEvent
+from api.enums import OrganizationConfigurationKey, PostHogEvent
 from api.schemas.auth import AuthResponse, LoginRequest, SignupRequest, UserResponse
 from api.services.auth.depends import create_user_configuration_with_mps_key, get_user
+from api.services.configuration.ai_model_configuration import (
+    convert_legacy_ai_model_configuration_to_v2,
+)
 from api.services.posthog_client import capture_event
 from api.utils.auth import create_jwt_token, hash_password, verify_password
 
@@ -47,6 +50,12 @@ async def signup(request: SignupRequest):
         )
         if mps_config:
             await db_client.update_user_configuration(user.id, mps_config)
+            model_config_v2 = convert_legacy_ai_model_configuration_to_v2(mps_config)
+            await db_client.upsert_configuration(
+                organization.id,
+                OrganizationConfigurationKey.MODEL_CONFIGURATION_V2.value,
+                model_config_v2.model_dump(mode="json", exclude_none=True),
+            )
     except Exception:
         logger.warning(
             "Failed to create default configuration for OSS user", exc_info=True

api/routes/campaign.py

@@ -18,7 +18,7 @@ from api.services.auth.depends import get_user
 from api.services.campaign.runner import campaign_runner_service
 from api.services.campaign.source_sync import CampaignSourceSyncService
 from api.services.campaign.source_sync_factory import get_sync_service
-from api.services.quota_service import check_dograh_quota
+from api.services.quota_service import authorize_workflow_run_start
 from api.services.reports import generate_campaign_report_csv
 from api.services.storage import storage_fs
 
@@ -550,7 +550,10 @@ async def start_campaign(
 
     # Check Dograh quota before starting campaign (apply per-workflow
     # model_overrides so we evaluate the keys this campaign will use).
-    quota_result = await check_dograh_quota(user, workflow_id=campaign.workflow_id)
+    quota_result = await authorize_workflow_run_start(
+        workflow_id=campaign.workflow_id,
+        actor_user=user,
+    )
     if not quota_result.has_quota:
         raise HTTPException(status_code=402, detail=quota_result.error_message)
 
@@ -872,7 +875,10 @@ async def resume_campaign(
 
     # Check Dograh quota before resuming campaign (apply per-workflow
     # model_overrides so we evaluate the keys this campaign will use).
-    quota_result = await check_dograh_quota(user, workflow_id=campaign.workflow_id)
+    quota_result = await authorize_workflow_run_start(
+        workflow_id=campaign.workflow_id,
+        actor_user=user,
+    )
     if not quota_result.has_quota:
         raise HTTPException(status_code=402, detail=quota_result.error_message)
 

api/routes/knowledge_base.py

@@ -369,6 +369,10 @@ async def search_chunks(
 
     try:
         # Import here to avoid circular dependency
+        from api.services.configuration.ai_model_configuration import (
+            apply_managed_embeddings_base_url,
+            get_resolved_ai_model_configuration,
+        )
         from api.services.configuration.registry import ServiceProviders
         from api.services.gen_ai import (
             AzureOpenAIEmbeddingService,
@@ -376,20 +380,29 @@ async def search_chunks(
         )
 
         # Try to get user's embeddings configuration
-        user_config = await db_client.get_user_configurations(user.id)
+        resolved_config = await get_resolved_ai_model_configuration(
+            user_id=user.id,
+            organization_id=user.selected_organization_id,
+        )
+        effective_config = resolved_config.effective
         embeddings_api_key = None
         embeddings_model = None
         embeddings_provider = None
+        embeddings_base_url = None
         embeddings_endpoint = None
         embeddings_api_version = None
 
-        if user_config.embeddings:
-            embeddings_api_key = user_config.embeddings.api_key
-            embeddings_model = user_config.embeddings.model
-            embeddings_provider = getattr(user_config.embeddings, "provider", None)
-            embeddings_endpoint = getattr(user_config.embeddings, "endpoint", None)
+        if effective_config.embeddings:
+            embeddings_api_key = effective_config.embeddings.api_key
+            embeddings_model = effective_config.embeddings.model
+            embeddings_provider = getattr(effective_config.embeddings, "provider", None)
+            embeddings_endpoint = getattr(effective_config.embeddings, "endpoint", None)
+            embeddings_base_url = apply_managed_embeddings_base_url(
+                provider=embeddings_provider,
+                base_url=getattr(effective_config.embeddings, "base_url", None),
+            )
             embeddings_api_version = getattr(
-                user_config.embeddings, "api_version", None
+                effective_config.embeddings, "api_version", None
             )
 
         # Initialize embedding service based on provider
@@ -406,9 +419,7 @@ async def search_chunks(
                 db_client=db_client,
                 api_key=embeddings_api_key,
                 model_id=embeddings_model or "text-embedding-3-small",
-                base_url=getattr(user_config.embeddings, "base_url", None)
-                if user_config.embeddings
-                else None,
+                base_url=embeddings_base_url,
             )
 
         # Perform search

api/routes/organization.py

@@ -1,15 +1,27 @@
 from typing import List, Optional
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Query
 from loguru import logger
 from pydantic import BaseModel
 from sqlalchemy.exc import IntegrityError
 
-from api.constants import DEFAULT_CAMPAIGN_RETRY_CONFIG, DEFAULT_ORG_CONCURRENCY_LIMIT
+from api.constants import (
+    DEFAULT_CAMPAIGN_RETRY_CONFIG,
+    DEFAULT_ORG_CONCURRENCY_LIMIT,
+    DEPLOYMENT_MODE,
+)
 from api.db import db_client
 from api.db.models import UserModel
 from api.db.telephony_configuration_client import TelephonyConfigurationInUseError
 from api.enums import OrganizationConfigurationKey, PostHogEvent
+from api.schemas.ai_model_configuration import (
+    DOGRAH_DEFAULT_LANGUAGE,
+    DOGRAH_DEFAULT_VOICE,
+    DOGRAH_SPEED_OPTIONS,
+    OrganizationAIModelConfigurationResponse,
+    OrganizationAIModelConfigurationV2,
+)
+from api.schemas.organization_preferences import OrganizationPreferences
 from api.schemas.telephony_config import (
     TelephonyConfigRequest,
     TelephonyConfigurationCreateRequest,
@@ -26,8 +38,36 @@ from api.schemas.telephony_phone_number import (
     PhoneNumberUpdateRequest,
     ProviderSyncStatus,
 )
-from api.services.auth.depends import get_user
-from api.services.configuration.masking import is_mask_of, mask_key
+from api.services.auth.depends import get_user, get_user_with_selected_organization
+from api.services.configuration.ai_model_configuration import (
+    check_for_masked_keys_in_ai_model_configuration_v2,
+    compile_ai_model_configuration_v2,
+    convert_legacy_ai_model_configuration_to_v2,
+    get_organization_ai_model_configuration_v2,
+    get_resolved_ai_model_configuration,
+    mask_ai_model_configuration_v2,
+    merge_ai_model_configuration_v2_secrets,
+    migrate_workflow_model_configurations_to_v2,
+    upsert_organization_ai_model_configuration_v2,
+)
+from api.services.configuration.check_validity import UserConfigurationValidator
+from api.services.configuration.defaults import DEFAULT_SERVICE_PROVIDERS
+from api.services.configuration.masking import is_mask_of, mask_key, mask_user_config
+from api.services.configuration.registry import (
+    DOGRAH_STT_LANGUAGES,
+    REGISTRY,
+    ServiceProviders,
+    ServiceType,
+)
+from api.services.mps_billing import ensure_hosted_mps_billing_account_v2
+from api.services.organization_context import (
+    OrganizationContextResponse,
+    get_organization_context,
+)
+from api.services.organization_preferences import (
+    get_organization_preferences,
+    upsert_organization_preferences,
+)
 from api.services.posthog_client import capture_event
 from api.services.telephony import registry as telephony_registry
 from api.services.telephony.factory import get_telephony_provider_by_id
@@ -98,6 +138,12 @@ class TelephonyConfigWarningsResponse(BaseModel):
     telnyx_missing_webhook_public_key_count: int
 
 
+@router.get("/context", response_model=OrganizationContextResponse)
+async def get_current_organization_context(user: UserModel = Depends(get_user)):
+    """Return organization-scoped configuration signals owned by Dograh."""
+    return await get_organization_context(user)
+
+
 @router.get(
     "/telephony-providers/metadata",
     response_model=TelephonyProvidersMetadataResponse,
@@ -159,6 +205,239 @@ async def get_telephony_config_warnings(user: UserModel = Depends(get_user)):
     )
 
 
+# ---------------------------------------------------------------------------
+# AI model configurations v2
+# ---------------------------------------------------------------------------
+
+
+def _byok_provider_schemas(service_type: ServiceType) -> dict[str, dict]:
+    return {
+        provider: model_cls.model_json_schema()
+        for provider, model_cls in REGISTRY[service_type].items()
+        if provider != ServiceProviders.DOGRAH.value
+    }
+
+
+async def _model_configuration_v2_response(
+    *,
+    user: UserModel,
+    configuration: OrganizationAIModelConfigurationV2 | None = None,
+) -> OrganizationAIModelConfigurationResponse:
+    resolved = await get_resolved_ai_model_configuration(
+        user_id=user.id,
+        organization_id=user.selected_organization_id,
+    )
+    raw_configuration = (
+        configuration
+        if configuration is not None
+        else resolved.organization_configuration
+    )
+    return OrganizationAIModelConfigurationResponse(
+        configuration=mask_ai_model_configuration_v2(raw_configuration),
+        effective_configuration=mask_user_config(resolved.effective),
+        source=resolved.source,
+    )
+
+
+@router.get("/model-configurations/v2/defaults")
+async def get_model_configuration_v2_defaults(
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    byok_default_providers = {
+        service: provider
+        for service, provider in DEFAULT_SERVICE_PROVIDERS.items()
+        if provider != ServiceProviders.DOGRAH.value
+    }
+    return {
+        "dograh": {
+            "voices": [DOGRAH_DEFAULT_VOICE],
+            "speeds": list(DOGRAH_SPEED_OPTIONS),
+            "languages": DOGRAH_STT_LANGUAGES,
+            "defaults": {
+                "voice": DOGRAH_DEFAULT_VOICE,
+                "speed": 1.0,
+                "language": DOGRAH_DEFAULT_LANGUAGE,
+            },
+        },
+        "byok": {
+            "pipeline": {
+                "llm": _byok_provider_schemas(ServiceType.LLM),
+                "tts": _byok_provider_schemas(ServiceType.TTS),
+                "stt": _byok_provider_schemas(ServiceType.STT),
+                "embeddings": _byok_provider_schemas(ServiceType.EMBEDDINGS),
+                "default_providers": byok_default_providers,
+            },
+            "realtime": {
+                "realtime": _byok_provider_schemas(ServiceType.REALTIME),
+                "llm": _byok_provider_schemas(ServiceType.LLM),
+                "embeddings": _byok_provider_schemas(ServiceType.EMBEDDINGS),
+                "default_providers": byok_default_providers,
+            },
+        },
+    }
+
+
+@router.get(
+    "/model-configurations/v2",
+    response_model=OrganizationAIModelConfigurationResponse,
+)
+async def get_model_configuration_v2(
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    return await _model_configuration_v2_response(user=user)
+
+
+@router.put(
+    "/model-configurations/v2",
+    response_model=OrganizationAIModelConfigurationResponse,
+)
+async def save_model_configuration_v2(
+    request: OrganizationAIModelConfigurationV2,
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    organization_id = user.selected_organization_id
+    existing = await get_organization_ai_model_configuration_v2(organization_id)
+    configuration = merge_ai_model_configuration_v2_secrets(request, existing)
+    try:
+        check_for_masked_keys_in_ai_model_configuration_v2(configuration)
+        effective = compile_ai_model_configuration_v2(configuration)
+        await UserConfigurationValidator().validate(
+            effective,
+            organization_id=organization_id,
+            created_by=user.provider_id,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=exc.args[0])
+
+    await upsert_organization_ai_model_configuration_v2(
+        organization_id,
+        configuration,
+    )
+    return await _model_configuration_v2_response(
+        user=user,
+        configuration=configuration,
+    )
+
+
+@router.get("/model-configurations/v2/migration-preview")
+async def preview_model_configuration_v2_migration(
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    legacy = await db_client.get_user_configurations(user.id)
+    try:
+        configuration = convert_legacy_ai_model_configuration_to_v2(legacy)
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc))
+    return {
+        "configuration": mask_ai_model_configuration_v2(configuration),
+        "effective_configuration": mask_user_config(
+            compile_ai_model_configuration_v2(configuration)
+        ),
+    }
+
+
+@router.post(
+    "/model-configurations/v2/migrate",
+    response_model=OrganizationAIModelConfigurationResponse,
+)
+async def migrate_model_configuration_v2(
+    force: bool = Query(default=False),
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    organization_id = user.selected_organization_id
+    existing = await get_organization_ai_model_configuration_v2(organization_id)
+    if existing is not None and not force:
+        raise HTTPException(
+            status_code=409,
+            detail="Organization already has a v2 model configuration",
+        )
+
+    legacy = await db_client.get_user_configurations(user.id)
+    try:
+        configuration = convert_legacy_ai_model_configuration_to_v2(legacy)
+        effective = compile_ai_model_configuration_v2(configuration)
+        await UserConfigurationValidator().validate(
+            effective,
+            organization_id=organization_id,
+            created_by=user.provider_id,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=exc.args[0])
+
+    if DEPLOYMENT_MODE != "oss":
+        try:
+            await ensure_hosted_mps_billing_account_v2(
+                organization_id,
+                created_by=str(user.provider_id),
+            )
+        except Exception as exc:
+            logger.error(
+                "Failed to initialize MPS billing v2 account for organization {}: {}",
+                organization_id,
+                exc,
+            )
+            raise HTTPException(
+                status_code=502,
+                detail="Failed to initialize MPS billing v2 account",
+            )
+
+    await upsert_organization_ai_model_configuration_v2(
+        organization_id,
+        configuration,
+    )
+    await migrate_workflow_model_configurations_to_v2(
+        organization_id=organization_id,
+        fallback_user_config=legacy,
+    )
+    return await _model_configuration_v2_response(
+        user=user,
+        configuration=configuration,
+    )
+
+
+@router.get("/preferences", response_model=OrganizationPreferences)
+async def get_preferences(
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    organization_id = user.selected_organization_id
+    return await get_organization_preferences(organization_id)
+
+
+@router.put("/preferences", response_model=OrganizationPreferences)
+async def save_preferences(
+    request: OrganizationPreferences,
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    organization_id = user.selected_organization_id
+    return await upsert_organization_preferences(
+        organization_id,
+        request,
+    )
+
+
+@router.get(
+    "/model-configurations/preferences",
+    response_model=OrganizationPreferences,
+    include_in_schema=False,
+)
+async def get_model_configuration_preferences_legacy(
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    return await get_preferences(user=user)
+
+
+@router.put(
+    "/model-configurations/preferences",
+    response_model=OrganizationPreferences,
+    include_in_schema=False,
+)
+async def save_model_configuration_preferences_legacy(
+    request: OrganizationPreferences,
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    return await save_preferences(request=request, user=user)
+
+
 def preserve_masked_fields(provider: str, request_dict: dict, existing: dict):
     """If the client re-submitted a masked sensitive field, restore the original."""
     for field_name in _sensitive_fields(provider):

api/routes/organization_usage.py

@@ -1,16 +1,16 @@
 import json
 from datetime import datetime, timedelta
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Literal, Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from fastapi.responses import StreamingResponse
 from loguru import logger
 from pydantic import BaseModel, Field
 
-from api.constants import DEPLOYMENT_MODE
+from api.constants import DEPLOYMENT_MODE, UI_APP_URL
 from api.db import db_client
 from api.db.models import UserModel
-from api.services.auth.depends import get_user
+from api.services.auth.depends import get_user, get_user_with_selected_organization
 from api.services.mps_service_key_client import mps_service_key_client
 from api.services.reports import generate_usage_runs_report_csv
 from api.utils.artifacts import artifact_url
@@ -22,14 +22,8 @@ class CurrentUsageResponse(BaseModel):
     period_start: str
     period_end: str
     used_dograh_tokens: float
-    quota_dograh_tokens: int
-    percentage_used: float
-    next_refresh_date: str
-    quota_enabled: bool
     total_duration_seconds: int
-    # New USD fields
     used_amount_usd: Optional[float] = None
-    quota_amount_usd: Optional[float] = None
     currency: Optional[str] = None
     price_per_second_usd: Optional[float] = None
 
@@ -40,6 +34,61 @@ class MPSCreditsResponse(BaseModel):
     total_quota: float
 
 
+class MPSCreditPurchaseUrlResponse(BaseModel):
+    checkout_url: str
+
+
+class MPSBillingAccountResponse(BaseModel):
+    id: int
+    organization_id: int
+    billing_mode: str
+    cached_balance_credits: float
+    currency: str
+
+
+class MPSCreditLedgerEntryResponse(BaseModel):
+    id: int
+    entry_type: str
+    origin: Optional[str] = None
+    credits_delta: float
+    balance_after: float
+    amount_minor: Optional[int] = None
+    amount_currency: Optional[str] = None
+    payment_order_id: Optional[int] = None
+    metric_code: Optional[str] = None
+    correlation_id: Optional[str] = None
+    aggregation_key: Optional[str] = None
+    usage_event_id: Optional[int] = None
+    workflow_run_id: Optional[int] = None
+    workflow_id: Optional[int] = None
+    billable_quantity: Optional[float] = None
+    quantity_unit: Optional[str] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    created_at: str
+
+
+class MPSBillingCreditsResponse(BaseModel):
+    billing_version: Literal["legacy", "v2"]
+    total_credits_used: float = 0.0
+    remaining_credits: float = 0.0
+    total_quota: float = 0.0
+    account: Optional[MPSBillingAccountResponse] = None
+    ledger_entries: List[MPSCreditLedgerEntryResponse] = Field(default_factory=list)
+    total_count: int = 0
+    page: int = 1
+    limit: int = 50
+    total_pages: int = 0
+
+
+def _optional_int(value: Any) -> Optional[int]:
+    if value is None:
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
 class WorkflowRunUsageResponse(BaseModel):
     id: int
     workflow_id: int
@@ -97,7 +146,7 @@ class DailyUsageBreakdownResponse(BaseModel):
 
 @router.get("/usage/current-period", response_model=CurrentUsageResponse)
 async def get_current_period_usage(user: UserModel = Depends(get_user)):
-    """Get current billing period usage for the user's organization."""
+    """Get current reporting-period usage for the user's organization."""
     if not user.selected_organization_id:
         raise HTTPException(status_code=400, detail="No organization selected")
 
@@ -142,6 +191,202 @@ async def get_mps_credits(user: UserModel = Depends(get_user)):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+async def _get_mps_billing_account_status(
+    user: UserModel, organization_id: int
+) -> Optional[dict]:
+    return await mps_service_key_client.get_billing_account_status(
+        organization_id=organization_id,
+        created_by=str(user.provider_id),
+    )
+
+
+def _is_mps_billing_v2(account: Optional[dict]) -> bool:
+    return bool(account and account.get("billing_mode") == "v2")
+
+
+async def _legacy_mps_credits_response(user: UserModel) -> MPSBillingCreditsResponse:
+    if DEPLOYMENT_MODE == "oss":
+        usage = await mps_service_key_client.get_usage_by_created_by(
+            str(user.provider_id)
+        )
+    else:
+        if not user.selected_organization_id:
+            raise HTTPException(status_code=400, detail="No organization selected")
+        usage = await mps_service_key_client.get_usage_by_organization(
+            user.selected_organization_id
+        )
+
+    total_used = float(usage.get("total_credits_used", 0.0))
+    total_remaining = float(usage.get("remaining_credits", 0.0))
+    return MPSBillingCreditsResponse(
+        billing_version="legacy",
+        total_credits_used=total_used,
+        remaining_credits=total_remaining,
+        total_quota=total_used + total_remaining,
+    )
+
+
+@router.get("/billing/credits", response_model=MPSBillingCreditsResponse)
+async def get_billing_credits(
+    page: int = Query(1, ge=1),
+    limit: int = Query(50, ge=1, le=100),
+    user: UserModel = Depends(get_user),
+):
+    """Return legacy MPS credits or paginated v2 billing ledger details for the org."""
+    try:
+        if DEPLOYMENT_MODE == "oss" or not user.selected_organization_id:
+            return await _legacy_mps_credits_response(user)
+
+        organization_id = user.selected_organization_id
+        account_status = await _get_mps_billing_account_status(user, organization_id)
+        if not _is_mps_billing_v2(account_status):
+            return await _legacy_mps_credits_response(user)
+
+        ledger = await mps_service_key_client.get_credit_ledger(
+            organization_id=organization_id,
+            page=page,
+            limit=limit,
+            created_by=str(user.provider_id),
+        )
+        account = ledger.get("account") or {}
+        ledger_entries = ledger.get("ledger_entries") or []
+        total_count = int(ledger.get("total_count") or len(ledger_entries))
+        response_limit = int(ledger.get("limit") or limit)
+        total_pages = int(
+            ledger.get("total_pages")
+            or ((total_count + response_limit - 1) // response_limit)
+        )
+        workflow_ids_by_run_id: dict[int, int] = {}
+        workflow_run_ids = {
+            workflow_run_id
+            for entry in ledger_entries
+            if (workflow_run_id := _optional_int(entry.get("workflow_run_id")))
+            is not None
+        }
+        for workflow_run_id in workflow_run_ids:
+            workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
+            if (
+                workflow_run
+                and workflow_run.workflow
+                and workflow_run.workflow.organization_id == organization_id
+            ):
+                workflow_ids_by_run_id[workflow_run_id] = workflow_run.workflow_id
+
+        balance = float(account.get("cached_balance_credits") or 0.0)
+        total_debits = sum(
+            abs(float(entry.get("credits_delta") or 0.0))
+            for entry in ledger_entries
+            if float(entry.get("credits_delta") or 0.0) < 0
+        )
+        if ledger.get("total_debits_credits") is not None:
+            total_debits = float(ledger["total_debits_credits"])
+
+        return MPSBillingCreditsResponse(
+            billing_version="v2",
+            total_credits_used=total_debits,
+            remaining_credits=balance,
+            total_quota=balance + total_debits,
+            account=MPSBillingAccountResponse(
+                id=int(account["id"]),
+                organization_id=int(account["organization_id"]),
+                billing_mode=str(account["billing_mode"]),
+                cached_balance_credits=balance,
+                currency=str(account.get("currency") or "USD"),
+            ),
+            ledger_entries=[
+                MPSCreditLedgerEntryResponse(
+                    id=int(entry["id"]),
+                    entry_type=str(entry["entry_type"]),
+                    origin=entry.get("origin"),
+                    credits_delta=float(entry.get("credits_delta") or 0.0),
+                    balance_after=float(entry.get("balance_after") or 0.0),
+                    amount_minor=entry.get("amount_minor"),
+                    amount_currency=entry.get("amount_currency"),
+                    payment_order_id=entry.get("payment_order_id"),
+                    metric_code=entry.get("metric_code"),
+                    correlation_id=entry.get("correlation_id"),
+                    aggregation_key=entry.get("aggregation_key"),
+                    usage_event_id=_optional_int(entry.get("usage_event_id")),
+                    workflow_run_id=_optional_int(entry.get("workflow_run_id")),
+                    workflow_id=workflow_ids_by_run_id.get(
+                        _optional_int(entry.get("workflow_run_id"))
+                    )
+                    if entry.get("workflow_run_id") is not None
+                    else None,
+                    billable_quantity=float(entry["billable_quantity"])
+                    if entry.get("billable_quantity") is not None
+                    else None,
+                    quantity_unit=entry.get("quantity_unit"),
+                    metadata=entry.get("metadata") or {},
+                    created_at=str(entry["created_at"]),
+                )
+                for entry in ledger_entries
+            ],
+            total_count=total_count,
+            page=int(ledger.get("page") or page),
+            limit=response_limit,
+            total_pages=total_pages,
+        )
+    except HTTPException:
+        raise
+    except Exception as exc:
+        logger.error(f"Failed to fetch billing credits: {exc}")
+        raise HTTPException(status_code=500, detail=str(exc))
+
+
+@router.post(
+    "/usage/mps-credits/purchase-url",
+    response_model=MPSCreditPurchaseUrlResponse,
+)
+async def create_mps_credit_purchase_url(
+    user: UserModel = Depends(get_user_with_selected_organization),
+):
+    """Create a checkout URL for organizations using Dograh-managed MPS v2."""
+    if DEPLOYMENT_MODE == "oss":
+        raise HTTPException(
+            status_code=404,
+            detail="Credit purchases are not available in OSS mode",
+        )
+
+    organization_id = user.selected_organization_id
+    assert organization_id is not None
+    account_status = await _get_mps_billing_account_status(user, organization_id)
+    if not _is_mps_billing_v2(account_status):
+        raise HTTPException(
+            status_code=403,
+            detail=(
+                "Credit purchases are available only for organizations using billing v2"
+            ),
+        )
+
+    try:
+        session = await mps_service_key_client.create_credit_purchase_url(
+            organization_id=organization_id,
+            created_by=str(user.provider_id),
+            return_url=f"{UI_APP_URL.rstrip('/')}/billing",
+            billing_details={
+                "source": "dograh_billing",
+                "dograh_user_id": str(user.id),
+                "dograh_provider_id": str(user.provider_id),
+            },
+        )
+    except Exception as exc:
+        logger.error(f"Failed to create MPS credit purchase URL: {exc}")
+        raise HTTPException(
+            status_code=502,
+            detail="Failed to create credit purchase URL",
+        )
+
+    checkout_url = session.get("checkout_url")
+    if not checkout_url:
+        logger.error(f"MPS checkout session response missing checkout_url: {session}")
+        raise HTTPException(
+            status_code=502,
+            detail="MPS checkout session response missing checkout_url",
+        )
+    return MPSCreditPurchaseUrlResponse(checkout_url=checkout_url)
+
+
 FILTERS_DESCRIPTION = """\
 JSON-encoded array of filter objects. Each object has the shape:
 

api/routes/public_agent.py

@@ -14,7 +14,7 @@ from pydantic import BaseModel
 
 from api.db import db_client
 from api.enums import TriggerState, WorkflowStatus
-from api.services.quota_service import check_dograh_quota_by_user_id
+from api.services.quota_service import authorize_workflow_run_start
 from api.services.telephony.factory import (
     get_default_telephony_provider,
     get_telephony_provider_by_id,
@@ -179,14 +179,6 @@ async def _execute_resolved_target(
     """Shared execution path once the target workflow has been resolved."""
     execution_user_id = _get_execution_user_id(target.workflow)
 
-    # Check Dograh quota using the workflow owner's config and model overrides.
-    quota_result = await check_dograh_quota_by_user_id(
-        execution_user_id,
-        workflow_id=target.workflow.id,
-    )
-    if not quota_result.has_quota:
-        raise HTTPException(status_code=402, detail=quota_result.error_message)
-
     # Get telephony provider — either the caller-specified config (validated
     # against the workflow's org) or the org's default config.
     if request.telephony_configuration_id is not None:
@@ -268,6 +260,15 @@ async def _execute_resolved_target(
         f"to phone number {request.phone_number}"
     )
 
+    # Check Dograh quota after the run exists so hosted v2 can mint and store
+    # the MPS correlation id before the provider starts the call.
+    quota_result = await authorize_workflow_run_start(
+        workflow_id=target.workflow.id,
+        workflow_run_id=workflow_run.id,
+    )
+    if not quota_result.has_quota:
+        raise HTTPException(status_code=402, detail=quota_result.error_message)
+
     # 9. Construct webhook URL for telephony provider callback
     backend_endpoint, _ = await get_backend_endpoints()
     webhook_endpoint = provider.WEBHOOK_ENDPOINT

api/routes/public_embed.py

@@ -7,6 +7,7 @@ They handle CORS, domain validation, and session management for embedded workflo
 import secrets
 from datetime import UTC, datetime, timedelta
 from typing import Optional
+from urllib.parse import urlsplit
 
 from fastapi import (
     APIRouter,
@@ -16,6 +17,8 @@ from fastapi import (
 )
 from loguru import logger
 from pydantic import BaseModel
+from starlette.datastructures import Headers
+from starlette.types import ASGIApp, Receive, Scope, Send
 
 from api.db import db_client
 from api.enums import WorkflowRunMode
@@ -27,6 +30,9 @@ from api.routes.turn_credentials import (
 
 router = APIRouter(prefix="/public/embed")
 
+EMBED_CORS_ALLOW_HEADERS = "Content-Type, Origin"
+EMBED_CORS_MAX_AGE = "86400"
+
 
 class InitEmbedRequest(BaseModel):
     """Request model for initializing an embed session"""
@@ -70,11 +76,9 @@ def validate_origin(origin: str, allowed_domains: list) -> bool:
         # If no domains specified, allow all origins
         return True
 
-    # Extract domain from origin (remove protocol)
-    if "://" in origin:
-        domain = origin.split("://")[1].split("/")[0].split(":")[0]
-    else:
-        domain = origin
+    domain, origin_port = _parse_origin_host_port(origin)
+    if not domain:
+        return False
 
     # Normalize domain for www matching
     def normalize_www(d: str) -> tuple[str, str]:
@@ -87,16 +91,23 @@ def validate_origin(origin: str, allowed_domains: list) -> bool:
     domain_variants = normalize_www(domain)
 
     for allowed in allowed_domains:
+        allowed = str(allowed).strip().lower()
         if allowed == "*":
             return True
-        elif allowed.startswith("*."):
+        allowed_domain, allowed_port = _parse_origin_host_port(allowed)
+        if not allowed_domain:
+            continue
+        if allowed_port is not None and allowed_port != origin_port:
+            continue
+
+        if allowed_domain.startswith("*."):
             # Wildcard subdomain matching
-            base_domain = allowed[2:]
+            base_domain = allowed_domain[2:]
             if domain == base_domain or domain.endswith("." + base_domain):
                 return True
         else:
             # Check both www and non-www versions
-            allowed_variants = normalize_www(allowed)
+            allowed_variants = normalize_www(allowed_domain)
             # If any variant of domain matches any variant of allowed, it's valid
             if any(
                 dv in allowed_variants or av in domain_variants
@@ -108,6 +119,24 @@ def validate_origin(origin: str, allowed_domains: list) -> bool:
     return False
 
 
+def _parse_origin_host_port(value: str) -> tuple[str, str | None]:
+    candidate = value.strip().lower()
+    if not candidate:
+        return "", None
+
+    if "://" not in candidate and not candidate.startswith("//"):
+        candidate = f"//{candidate}"
+
+    parsed = urlsplit(candidate)
+    try:
+        parsed_port = parsed.port
+    except ValueError:
+        parsed_port = None
+
+    port = str(parsed_port) if parsed_port is not None else None
+    return (parsed.hostname or "").rstrip("."), port
+
+
 def generate_session_token() -> str:
     """Generate a cryptographically secure session token"""
     return f"emb_session_{secrets.token_urlsafe(32)}"
@@ -121,8 +150,120 @@ def get_request_origin(request: Request) -> str:
     return origin
 
 
+def _cors_response(origin: str, methods: str) -> Response:
+    return Response(
+        headers={
+            "Access-Control-Allow-Origin": origin,
+            "Access-Control-Allow-Methods": methods,
+            "Access-Control-Allow-Headers": EMBED_CORS_ALLOW_HEADERS,
+            "Access-Control-Max-Age": EMBED_CORS_MAX_AGE,
+            "Vary": "Origin",
+        }
+    )
+
+
+def _allow_embed_origin(response: Response, origin: str) -> None:
+    response.headers["Access-Control-Allow-Origin"] = origin
+    vary = response.headers.get("Vary")
+    if not vary:
+        response.headers["Vary"] = "Origin"
+        return
+
+    vary_values = {value.strip().lower() for value in vary.split(",")}
+    if "origin" not in vary_values:
+        response.headers["Vary"] = f"{vary}, Origin"
+
+
+async def _config_preflight_response(token: str, origin: str) -> Response:
+    embed_token = await db_client.get_embed_token_by_token(token)
+    if not embed_token or not embed_token.is_active:
+        return Response(status_code=403)
+
+    if not validate_origin(origin, embed_token.allowed_domains or []):
+        return Response(status_code=403)
+
+    return _cors_response(origin, "GET, OPTIONS")
+
+
+async def _turn_credentials_preflight_response(
+    session_token: str, origin: str
+) -> Response:
+    embed_session = await db_client.get_embed_session_by_token(session_token)
+    if not embed_session:
+        return Response(status_code=403)
+
+    if embed_session.expires_at and embed_session.expires_at < datetime.now(UTC):
+        return Response(status_code=403)
+
+    embed_token = await db_client.get_embed_token_by_id(embed_session.embed_token_id)
+    if not embed_token:
+        return Response(status_code=403)
+
+    if not validate_origin(origin, embed_token.allowed_domains or []):
+        return Response(status_code=403)
+
+    return _cors_response(origin, "GET, OPTIONS")
+
+
+async def build_public_embed_preflight_response(
+    path: str, origin: str, requested_method: str, api_prefix: str = "/api/v1"
+) -> Response | None:
+    """Handle embed preflights before global CORSMiddleware rejects external sites."""
+    public_embed_prefix = f"{api_prefix.rstrip('/')}/public/embed"
+
+    if path == f"{public_embed_prefix}/init":
+        if requested_method.upper() != "POST":
+            return Response(status_code=405)
+        return _cors_response(origin, "POST, OPTIONS")
+
+    config_prefix = f"{public_embed_prefix}/config/"
+    if path.startswith(config_prefix):
+        if requested_method.upper() != "GET":
+            return Response(status_code=405)
+        token = path[len(config_prefix) :].split("/", 1)[0]
+        return await _config_preflight_response(token, origin)
+
+    turn_credentials_prefix = f"{public_embed_prefix}/turn-credentials/"
+    if path.startswith(turn_credentials_prefix):
+        if requested_method.upper() != "GET":
+            return Response(status_code=405)
+        session_token = path[len(turn_credentials_prefix) :].split("/", 1)[0]
+        return await _turn_credentials_preflight_response(session_token, origin)
+
+    return None
+
+
+class PublicEmbedCORSMiddleware:
+    """Allow token-gated embed CORS before global SaaS CORS rejects preflights."""
+
+    def __init__(self, app: ASGIApp, api_prefix: str = "/api/v1"):
+        self.app = app
+        self.api_prefix = api_prefix
+
+    async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
+        if scope["type"] != "http" or scope.get("method") != "OPTIONS":
+            await self.app(scope, receive, send)
+            return
+
+        headers = Headers(scope=scope)
+        origin = headers.get("origin")
+        requested_method = headers.get("access-control-request-method")
+
+        if origin and requested_method:
+            response = await build_public_embed_preflight_response(
+                scope.get("path", ""), origin, requested_method, self.api_prefix
+            )
+            if response is not None:
+                await response(scope, receive, send)
+                return
+
+        await self.app(scope, receive, send)
+
+
 @router.post("/init", response_model=InitEmbedResponse)
-async def initialize_embed_session(request: Request, init_request: InitEmbedRequest):
+async def initialize_embed_session(
+    request: Request, init_request: InitEmbedRequest, response: Response
+):
     """Initialize an embed session with token validation and domain checking.
 
     This endpoint:
@@ -158,6 +299,9 @@ async def initialize_embed_session(request: Request, init_request: InitEmbedRequ
         )
         raise HTTPException(status_code=403, detail=f"Domain not allowed: {origin}")
 
+    if origin:
+        _allow_embed_origin(response, origin)
+
     # Create workflow run
     try:
         workflow_run = await db_client.create_workflow_run(
@@ -204,8 +348,19 @@ async def initialize_embed_session(request: Request, init_request: InitEmbedRequ
     )
 
 
+@router.options("/config/{token}")
+async def options_embed_config(token: str, request: Request):
+    """Fallback OPTIONS handler for the embed config endpoint.
+
+    Browser preflights include Access-Control-Request-Method and are handled by
+    PublicEmbedCORSMiddleware before global CORS. This keeps non-conformant
+    OPTIONS requests on the same validation path.
+    """
+    return await _config_preflight_response(token, request.headers.get("origin", ""))
+
+
 @router.get("/config/{token}", response_model=EmbedConfigResponse)
-async def get_embed_config(token: str, request: Request):
+async def get_embed_config(token: str, request: Request, response: Response):
     """Get embed configuration without creating a session.
 
     This endpoint is used to fetch widget configuration for display purposes
@@ -226,6 +381,11 @@ async def get_embed_config(token: str, request: Request):
     if not validate_origin(origin, embed_token.allowed_domains or []):
         raise HTTPException(status_code=403, detail=f"Domain not allowed: {origin}")
 
+    # Set CORS header explicitly; the global CORSMiddleware covers only
+    # first-party origins; this endpoint is fetched by external embed sites.
+    if origin:
+        _allow_embed_origin(response, origin)
+
     # Extract settings with defaults
     settings = embed_token.settings or {}
 
@@ -243,24 +403,20 @@ async def get_embed_config(token: str, request: Request):
 
 @router.options("/init")
 async def options_init(request: Request):
-    """Handle CORS preflight for init endpoint"""
+    """Fallback OPTIONS handler for init endpoint."""
+    # Browser preflights are handled by PublicEmbedCORSMiddleware before global CORS.
     # For init endpoint, we need to check the token in the request body
     # But OPTIONS requests don't have body, so we'll be permissive
     # The actual validation happens in the POST request
     origin = request.headers.get("origin", "*")
 
-    return Response(
-        headers={
-            "Access-Control-Allow-Origin": origin,
-            "Access-Control-Allow-Methods": "POST, OPTIONS",
-            "Access-Control-Allow-Headers": "Content-Type, Origin",
-            "Access-Control-Max-Age": "86400",
-        }
-    )
+    return _cors_response(origin, "POST, OPTIONS")
 
 
 @router.get("/turn-credentials/{session_token}", response_model=TurnCredentialsResponse)
-async def get_public_turn_credentials(session_token: str, request: Request):
+async def get_public_turn_credentials(
+    session_token: str, request: Request, response: Response
+):
     """Get TURN credentials for an embed session.
 
     This endpoint allows embedded widgets to obtain TURN server credentials
@@ -295,6 +451,9 @@ async def get_public_turn_credentials(session_token: str, request: Request):
         )
         raise HTTPException(status_code=403, detail=f"Domain not allowed: {origin}")
 
+    if origin:
+        _allow_embed_origin(response, origin)
+
     # Check if TURN is configured
     if not TURN_SECRET:
         raise HTTPException(
@@ -316,63 +475,8 @@ async def get_public_turn_credentials(session_token: str, request: Request):
 
 @router.options("/turn-credentials/{session_token}")
 async def options_turn_credentials(request: Request, session_token: str):
-    """Handle CORS preflight for TURN credentials endpoint"""
-    origin = request.headers.get("origin", "*")
-
-    # Try to validate the session token and get allowed domains
-    allowed_origin = origin
-    try:
-        embed_session = await db_client.get_embed_session_by_token(session_token)
-        if embed_session:
-            embed_token = await db_client.get_embed_token_by_id(
-                embed_session.embed_token_id
-            )
-            if embed_token:
-                # Check if origin is in allowed domains (empty means allow all)
-                if validate_origin(origin, embed_token.allowed_domains or []):
-                    allowed_origin = origin
-                else:
-                    allowed_origin = ""
-    except Exception:
-        # On error, be permissive for OPTIONS
-        pass
-
-    return Response(
-        headers={
-            "Access-Control-Allow-Origin": allowed_origin,
-            "Access-Control-Allow-Methods": "GET, OPTIONS",
-            "Access-Control-Allow-Headers": "Content-Type",
-            "Access-Control-Max-Age": "86400",
-        }
-    )
-
-
-@router.options("/config/{token}")
-async def options_config(request: Request, token: str):
-    """Handle CORS preflight for config endpoint"""
-    # Get origin header
-    origin = request.headers.get("origin", "*")
-
-    # Try to validate the token and get allowed domains
-    allowed_origin = origin
-    try:
-        embed_token = await db_client.get_embed_token_by_token(token)
-        if embed_token and embed_token.is_active:
-            # Check if origin is in allowed domains
-            if validate_origin(origin, embed_token.allowed_domains or []):
-                allowed_origin = origin
-            else:
-                # If not allowed, don't include the origin
-                allowed_origin = ""
-    except Exception:
-        # On error, be permissive for OPTIONS
-        pass
-
-    return Response(
-        headers={
-            "Access-Control-Allow-Origin": allowed_origin,
-            "Access-Control-Allow-Methods": "GET, OPTIONS",
-            "Access-Control-Allow-Headers": "Content-Type",
-            "Access-Control-Max-Age": "86400",
-        }
+    """Fallback OPTIONS handler for TURN credentials endpoint."""
+    # Browser preflights are handled by PublicEmbedCORSMiddleware before global CORS.
+    return await _turn_credentials_preflight_response(
+        session_token, request.headers.get("origin", "")
     )

api/routes/telephony.py

@@ -25,7 +25,7 @@ from api.enums import CallType, WorkflowRunState
 from api.errors.telephony_errors import TelephonyError
 from api.sdk_expose import sdk_expose
 from api.services.auth.depends import get_user
-from api.services.quota_service import check_dograh_quota_by_user_id
+from api.services.quota_service import authorize_workflow_run_start
 from api.services.telephony.call_transfer_manager import get_call_transfer_manager
 from api.services.telephony.factory import (
     get_all_telephony_providers,
@@ -53,7 +53,7 @@ class InitiateCallRequest(BaseModel):
     workflow_run_id: int | None = None
     phone_number: str | None = None
     # Optional explicit telephony config to use for the test call. If omitted,
-    # falls back to the user's per-user default (when set), then the org default.
+    # falls back to the org default.
     telephony_configuration_id: int | None = None
     # Optional caller-ID phone number to dial out from. Must belong to the
     # resolved telephony configuration; otherwise the provider picks one.
@@ -82,7 +82,12 @@ async def initiate_call(
     """Initiate a call using the configured telephony provider from web browser. This is
     supposed to be a test call method for the draft version of the agent."""
 
-    user_configuration = await db_client.get_user_configurations(user.id)
+    from api.services.organization_preferences import get_organization_preferences
+
+    preferences = await get_organization_preferences(
+        user.selected_organization_id,
+        db=db_client,
+    )
 
     # Resolve which telephony config to use: explicit request value, otherwise
     # the org's default outbound config.
@@ -116,13 +121,12 @@ async def initiate_call(
             detail="telephony_not_configured",
         )
 
-    phone_number = request.phone_number or user_configuration.test_phone_number
+    phone_number = request.phone_number or preferences.test_phone_number
 
     if not phone_number:
         raise HTTPException(
             status_code=400,
-            detail="Phone number must be provided in request or set in user "
-            "configuration",
+            detail="Phone number must be provided in request or set in organization preferences",
         )
 
     workflow = await db_client.get_workflow(
@@ -132,14 +136,6 @@ async def initiate_call(
         raise HTTPException(status_code=404, detail="Workflow not found")
     execution_user_id = _get_execution_user_id(workflow)
 
-    # Check Dograh quota before initiating the call (apply per-workflow
-    # model_overrides so the keys we will actually use are the ones checked).
-    quota_result = await check_dograh_quota_by_user_id(
-        execution_user_id, workflow_id=workflow.id
-    )
-    if not quota_result.has_quota:
-        raise HTTPException(status_code=402, detail=quota_result.error_message)
-
     # Determine the workflow run mode based on provider type
     workflow_run_mode = provider.PROVIDER_NAME
 
@@ -182,6 +178,16 @@ async def initiate_call(
             )
         workflow_run_name = workflow_run.name
 
+    # Check Dograh quota after the run exists so hosted v2 can mint and store
+    # the MPS correlation id before initiating the call.
+    quota_result = await authorize_workflow_run_start(
+        workflow_id=workflow.id,
+        workflow_run_id=workflow_run_id,
+        actor_user=user,
+    )
+    if not quota_result.has_quota:
+        raise HTTPException(status_code=402, detail=quota_result.error_message)
+
     # Construct webhook URL based on provider type
     backend_endpoint, _ = await get_backend_endpoints()
 
@@ -735,19 +741,8 @@ async def handle_inbound_run(request: Request):
                 TelephonyError.SIGNATURE_VALIDATION_FAILED
             )
 
-        # 4. Quota check (use the workflow's model_overrides if set).
-        quota_result = await check_dograh_quota_by_user_id(
-            user_id, workflow_id=workflow_id
-        )
-        if not quota_result.has_quota:
-            logger.warning(
-                f"User {user_id} has exceeded quota: {quota_result.error_message}"
-            )
-            return provider_class.generate_validation_error_response(
-                TelephonyError.QUOTA_EXCEEDED
-            )
-
-        # 5. Create workflow run + return provider-shaped response.
+        # 5. Create workflow run + authorize quota before returning provider
+        # stream instructions.
         workflow_run_id = await _create_inbound_workflow_run(
             workflow_id,
             user_id,
@@ -756,6 +751,17 @@ async def handle_inbound_run(request: Request):
             telephony_configuration_id=telephony_configuration_id,
             from_phone_number_id=phone_row.id,
         )
+        quota_result = await authorize_workflow_run_start(
+            workflow_id=workflow_id,
+            workflow_run_id=workflow_run_id,
+        )
+        if not quota_result.has_quota:
+            logger.warning(
+                f"User {user_id} has exceeded quota: {quota_result.error_message}"
+            )
+            return provider_class.generate_validation_error_response(
+                TelephonyError.QUOTA_EXCEEDED
+            )
 
         backend_endpoint, wss_backend_endpoint = await get_backend_endpoints()
         websocket_url = (
@@ -870,20 +876,8 @@ async def handle_inbound_telephony(
             logger.error(f"Request validation failed: {error_type}")
             return provider_class.generate_validation_error_response(error_type)
 
-        # Check quota before processing (apply per-workflow model_overrides).
+        # Create workflow run.
         user_id = workflow_context["user_id"]
-        quota_result = await check_dograh_quota_by_user_id(
-            user_id, workflow_id=workflow_id
-        )
-        if not quota_result.has_quota:
-            logger.warning(
-                f"User {user_id} has exceeded quota for inbound calls: {quota_result.error_message}"
-            )
-            return provider_class.generate_validation_error_response(
-                TelephonyError.QUOTA_EXCEEDED
-            )
-
-        # Create workflow run
         workflow_run_id = await _create_inbound_workflow_run(
             workflow_id,
             workflow_context["user_id"],
@@ -892,6 +886,17 @@ async def handle_inbound_telephony(
             telephony_configuration_id=workflow_context["telephony_configuration_id"],
             from_phone_number_id=workflow_context.get("from_phone_number_id"),
         )
+        quota_result = await authorize_workflow_run_start(
+            workflow_id=workflow_id,
+            workflow_run_id=workflow_run_id,
+        )
+        if not quota_result.has_quota:
+            logger.warning(
+                f"User {user_id} has exceeded quota for inbound calls: {quota_result.error_message}"
+            )
+            return provider_class.generate_validation_error_response(
+                TelephonyError.QUOTA_EXCEEDED
+            )
 
         # Generate response URLs
         backend_endpoint, wss_backend_endpoint = await get_backend_endpoints()

api/routes/user.py

@@ -10,6 +10,9 @@ from api.db.models import (
     UserModel,
 )
 from api.services.auth.depends import get_user
+from api.services.configuration.ai_model_configuration import (
+    get_resolved_ai_model_configuration,
+)
 from api.services.configuration.check_validity import (
     APIKeyStatusResponse,
     UserConfigurationValidator,
@@ -19,6 +22,10 @@ from api.services.configuration.masking import check_for_masked_keys, mask_user_
 from api.services.configuration.merge import merge_user_configurations
 from api.services.configuration.registry import REGISTRY, ServiceType
 from api.services.mps_service_key_client import mps_service_key_client
+from api.services.organization_preferences import (
+    get_organization_preferences,
+    upsert_organization_preferences,
+)
 
 router = APIRouter(prefix="/user")
 
@@ -94,8 +101,17 @@ class UserConfigurationRequestResponseSchema(BaseModel):
 async def get_user_configurations(
     user: UserModel = Depends(get_user),
 ) -> UserConfigurationRequestResponseSchema:
-    user_configurations = await db_client.get_user_configurations(user.id)
-    masked_config = mask_user_config(user_configurations)
+    resolved_config = await get_resolved_ai_model_configuration(
+        user_id=user.id,
+        organization_id=user.selected_organization_id,
+    )
+    masked_config = mask_user_config(resolved_config.effective)
+    if user.selected_organization_id:
+        preferences = await get_organization_preferences(user.selected_organization_id)
+        if preferences.test_phone_number is not None:
+            masked_config["test_phone_number"] = preferences.test_phone_number
+        if preferences.timezone is not None:
+            masked_config["timezone"] = preferences.timezone
 
     # Add organization pricing info if available
     if user.selected_organization_id:
@@ -121,34 +137,61 @@ async def update_user_configurations(
 
     # Remove organization_pricing from incoming dict as it's read-only
     incoming_dict.pop("organization_pricing", None)
+    preferences_update = {
+        key: incoming_dict.pop(key)
+        for key in ("test_phone_number", "timezone")
+        if key in incoming_dict
+    }
 
-    # Merge via helper
-    try:
-        user_configurations = merge_user_configurations(existing_config, incoming_dict)
-    except ValidationError as e:
-        raise HTTPException(status_code=422, detail=str(e))
+    if incoming_dict:
+        # Merge via helper
+        try:
+            user_configurations = merge_user_configurations(
+                existing_config, incoming_dict
+            )
+        except ValidationError as e:
+            raise HTTPException(status_code=422, detail=str(e))
 
-    try:
-        check_for_masked_keys(user_configurations)
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
+        try:
+            check_for_masked_keys(user_configurations)
+        except ValueError as e:
+            raise HTTPException(status_code=400, detail=str(e))
 
-    try:
-        validator = UserConfigurationValidator()
-        await validator.validate(
-            user_configurations,
-            organization_id=user.selected_organization_id,
-            created_by=user.provider_id,
+        try:
+            validator = UserConfigurationValidator()
+            await validator.validate(
+                user_configurations,
+                organization_id=user.selected_organization_id,
+                created_by=user.provider_id,
+            )
+        except ValueError as e:
+            raise HTTPException(status_code=422, detail=e.args[0])
+
+        user_configurations = await db_client.update_user_configuration(
+            user.id, user_configurations
         )
-    except ValueError as e:
-        raise HTTPException(status_code=422, detail=e.args[0])
+    else:
+        user_configurations = existing_config
 
-    user_configurations = await db_client.update_user_configuration(
-        user.id, user_configurations
-    )
+    if user.selected_organization_id and preferences_update:
+        preferences = await get_organization_preferences(user.selected_organization_id)
+        if "test_phone_number" in preferences_update:
+            preferences.test_phone_number = preferences_update["test_phone_number"]
+        if "timezone" in preferences_update:
+            preferences.timezone = preferences_update["timezone"]
+        await upsert_organization_preferences(
+            user.selected_organization_id,
+            preferences,
+        )
 
     # Return masked version of updated config
     masked_config = mask_user_config(user_configurations)
+    if user.selected_organization_id:
+        preferences = await get_organization_preferences(user.selected_organization_id)
+        if preferences.test_phone_number is not None:
+            masked_config["test_phone_number"] = preferences.test_phone_number
+        if preferences.timezone is not None:
+            masked_config["timezone"] = preferences.timezone
 
     # Add organization pricing info if available
     if user.selected_organization_id:
@@ -168,7 +211,11 @@ async def validate_user_configurations(
     validity_ttl_seconds: int = Query(default=60, ge=0, le=86400),
     user: UserModel = Depends(get_user),
 ) -> APIKeyStatusResponse:
-    configurations = await db_client.get_user_configurations(user.id)
+    resolved_config = await get_resolved_ai_model_configuration(
+        user_id=user.id,
+        organization_id=user.selected_organization_id,
+    )
+    configurations = resolved_config.effective
 
     if (
         configurations.last_validated_at

api/routes/webrtc_signaling.py

@@ -45,7 +45,7 @@ from api.services.pipecat.ws_sender_registry import (
     register_ws_sender,
     unregister_ws_sender,
 )
-from api.services.quota_service import check_dograh_quota
+from api.services.quota_service import authorize_workflow_run_start
 
 router = APIRouter(prefix="/ws")
 
@@ -329,7 +329,11 @@ class SignalingManager:
 
         # Check Dograh quota before initiating the call (apply per-workflow
         # model_overrides so we evaluate the keys this workflow will use).
-        quota_result = await check_dograh_quota(user, workflow_id=workflow_id)
+        quota_result = await authorize_workflow_run_start(
+            workflow_id=workflow_id,
+            workflow_run_id=workflow_run_id,
+            actor_user=user,
+        )
         if not quota_result.has_quota:
             # Send error response for quota issues
             await ws.send_json(

api/routes/workflow.py

@@ -16,9 +16,18 @@ from api.db.agent_trigger_client import TriggerPathConflictError
 from api.db.models import UserModel
 from api.db.workflow_template_client import WorkflowTemplateClient
 from api.enums import CallType, PostHogEvent, StorageBackend
+from api.schemas.ai_model_configuration import OrganizationAIModelConfigurationV2
 from api.schemas.workflow import WorkflowRunResponseSchema
 from api.sdk_expose import sdk_expose
 from api.services.auth.depends import get_user
+from api.services.configuration.ai_model_configuration import (
+    WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY,
+    check_for_masked_keys_in_ai_model_configuration_v2,
+    compile_ai_model_configuration_v2,
+    convert_legacy_ai_model_configuration_to_v2,
+    get_resolved_ai_model_configuration,
+    merge_ai_model_configuration_v2_secrets,
+)
 from api.services.configuration.check_validity import UserConfigurationValidator
 from api.services.configuration.masking import (
     mask_workflow_configurations,
@@ -32,12 +41,15 @@ from api.services.configuration.resolve import (
 )
 from api.services.mps_service_key_client import mps_service_key_client
 from api.services.posthog_client import capture_event
-from api.services.pricing.run_usage_response import format_public_usage_info
 from api.services.reports import generate_workflow_report_csv
 from api.services.storage import storage_fs
 from api.services.workflow.dto import ReactFlowDTO, sanitize_workflow_definition
 from api.services.workflow.duplicate import duplicate_workflow
 from api.services.workflow.errors import ItemKind, WorkflowError
+from api.services.workflow.run_usage_response import (
+    format_public_cost_info,
+    format_public_usage_info,
+)
 from api.services.workflow.trigger_paths import (
     TriggerPathIssue,
     ensure_trigger_paths,
@@ -955,12 +967,74 @@ async def update_workflow(
                     existing_def,
                 )
 
-        # Validate model_overrides: resolve onto global config, then
-        # run the same validator used by the user-configurations endpoint.
-        # Also stamp the current global API key into the override so the override
-        # remains functional if the global config later switches to a different provider.
+        # Validate model overrides. v2 uses a complete workflow-level model
+        # configuration; legacy v1 uses partial service overlays.
         workflow_configurations = request.workflow_configurations
-        if workflow_configurations and workflow_configurations.get("model_overrides"):
+        if workflow_configurations and workflow_configurations.get(
+            WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY
+        ):
+            existing_workflow = await db_client.get_workflow(
+                workflow_id, organization_id=user.selected_organization_id
+            )
+            if existing_workflow is None:
+                raise HTTPException(
+                    status_code=404, detail=f"Workflow with id {workflow_id} not found"
+                )
+            existing_draft = await db_client.get_draft_version(workflow_id)
+            existing_configs = (
+                existing_draft.workflow_configurations
+                if existing_draft
+                else existing_workflow.released_definition.workflow_configurations
+            )
+            existing_v2_override = (existing_configs or {}).get(
+                WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY
+            )
+            try:
+                incoming_v2_override = (
+                    OrganizationAIModelConfigurationV2.model_validate(
+                        workflow_configurations[
+                            WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY
+                        ]
+                    )
+                )
+                existing_v2_override_config = (
+                    OrganizationAIModelConfigurationV2.model_validate(
+                        existing_v2_override
+                    )
+                    if existing_v2_override
+                    else None
+                )
+                v2_override = merge_ai_model_configuration_v2_secrets(
+                    incoming_v2_override,
+                    existing_v2_override_config,
+                )
+                if existing_v2_override_config is None:
+                    resolved_config = await get_resolved_ai_model_configuration(
+                        user_id=user.id,
+                        organization_id=user.selected_organization_id,
+                    )
+                    v2_override = merge_ai_model_configuration_v2_secrets(
+                        v2_override,
+                        resolved_config.organization_configuration,
+                    )
+                check_for_masked_keys_in_ai_model_configuration_v2(v2_override)
+                effective = compile_ai_model_configuration_v2(v2_override)
+                await UserConfigurationValidator().validate(
+                    effective,
+                    organization_id=user.selected_organization_id,
+                    created_by=user.provider_id,
+                )
+            except (ValidationError, ValueError) as e:
+                raise HTTPException(status_code=422, detail=str(e))
+            workflow_configurations = {
+                **workflow_configurations,
+                WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY: v2_override.model_dump(
+                    mode="json",
+                    exclude_none=True,
+                ),
+            }
+            workflow_configurations.pop("model_overrides", None)
+        elif workflow_configurations and workflow_configurations.get("model_overrides"):
             existing_workflow = await db_client.get_workflow(
                 workflow_id, organization_id=user.selected_organization_id
             )
@@ -978,24 +1052,48 @@ async def update_workflow(
                 workflow_configurations,
                 existing_configs,
             )
-            user_config = await db_client.get_user_configurations(user.id)
+            resolved_config = await get_resolved_ai_model_configuration(
+                user_id=user.id,
+                organization_id=user.selected_organization_id,
+            )
+            effective_config = resolved_config.effective
             try:
                 enriched_overrides = enrich_overrides_with_api_keys(
                     workflow_configurations["model_overrides"],
-                    user_config,
+                    effective_config,
                 )
-                effective = resolve_effective_config(user_config, enriched_overrides)
-                await UserConfigurationValidator().validate(
-                    effective,
-                    organization_id=user.selected_organization_id,
-                    created_by=user.provider_id,
+                effective = resolve_effective_config(
+                    effective_config, enriched_overrides
                 )
+                if resolved_config.source == "organization_v2":
+                    v2_override = convert_legacy_ai_model_configuration_to_v2(effective)
+                    await UserConfigurationValidator().validate(
+                        compile_ai_model_configuration_v2(v2_override),
+                        organization_id=user.selected_organization_id,
+                        created_by=user.provider_id,
+                    )
+                else:
+                    await UserConfigurationValidator().validate(
+                        effective,
+                        organization_id=user.selected_organization_id,
+                        created_by=user.provider_id,
+                    )
             except ValueError as e:
                 raise HTTPException(status_code=422, detail=str(e))
-            workflow_configurations = {
-                **workflow_configurations,
-                "model_overrides": enriched_overrides,
-            }
+            if resolved_config.source == "organization_v2":
+                workflow_configurations = {
+                    **workflow_configurations,
+                    WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY: v2_override.model_dump(
+                        mode="json",
+                        exclude_none=True,
+                    ),
+                }
+                workflow_configurations.pop("model_overrides", None)
+            else:
+                workflow_configurations = {
+                    **workflow_configurations,
+                    "model_overrides": enriched_overrides,
+                }
 
         # Reject upfront if any new trigger path collides with another
         # workflow's trigger — keeps the workflow record from
@@ -1171,22 +1269,7 @@ async def get_workflow_run(
         "transcript_public_url": artifact_url(public_access_token, "transcript"),
         "recording_public_url": artifact_url(public_access_token, "recording"),
         "public_access_token": public_access_token,
-        "cost_info": {
-            "dograh_token_usage": (
-                run.cost_info.get("dograh_token_usage")
-                if run.cost_info and "dograh_token_usage" in run.cost_info
-                else round(float(run.cost_info.get("total_cost_usd", 0)) * 100, 2)
-                if run.cost_info and "total_cost_usd" in run.cost_info
-                else 0
-            ),
-            "call_duration_seconds": int(
-                round(run.cost_info.get("call_duration_seconds"))
-            )
-            if run.cost_info and run.cost_info.get("call_duration_seconds") is not None
-            else None,
-        }
-        if run.cost_info
-        else None,
+        "cost_info": format_public_cost_info(run.cost_info, run.usage_info),
         "usage_info": format_public_usage_info(run.usage_info),
         "created_at": run.created_at,
         "definition_id": run.definition_id,

api/routes/workflow_text_chat.py

@@ -9,8 +9,8 @@ from pydantic import BaseModel, Field
 from api.db import db_client
 from api.db.models import UserModel, WorkflowRunTextSessionModel
 from api.enums import WorkflowRunMode
-from api.services.auth.depends import get_user
-from api.services.quota_service import check_dograh_quota
+from api.services.auth.depends import get_user_with_selected_organization
+from api.services.quota_service import authorize_workflow_run_start
 from api.services.workflow.text_chat_session_service import (
     TextChatPendingTurnLostError,
     TextChatSessionExecutionError,
@@ -96,14 +96,16 @@ def _revision_conflict_detail(e: Any) -> dict[str, Any]:
     }
 
 
-def _require_selected_organization_id(user: UserModel) -> int:
-    if user.selected_organization_id is None:
-        raise HTTPException(status_code=403, detail="Organization context is required")
-    return user.selected_organization_id
-
-
-async def _ensure_text_chat_quota(user: UserModel, workflow_id: int) -> None:
-    quota_result = await check_dograh_quota(user, workflow_id=workflow_id)
+async def _ensure_text_chat_quota(
+    user: UserModel,
+    workflow_id: int,
+    workflow_run_id: int,
+) -> None:
+    quota_result = await authorize_workflow_run_start(
+        workflow_id=workflow_id,
+        workflow_run_id=workflow_run_id,
+        actor_user=user,
+    )
     if not quota_result.has_quota:
         raise HTTPException(status_code=402, detail=quota_result.error_message)
 
@@ -114,9 +116,8 @@ async def _load_text_session_or_404(
     user: UserModel,
 ) -> WorkflowRunTextSessionModel:
     set_current_run_id(run_id)
-    organization_id = _require_selected_organization_id(user)
     text_session = await db_client.get_workflow_run_text_session(
-        run_id, organization_id=organization_id
+        run_id, organization_id=user.selected_organization_id
     )
     if not text_session or not text_session.workflow_run:
         raise HTTPException(status_code=404, detail="Text chat session not found")
@@ -158,11 +159,8 @@ async def _execute_pending_turn_response(
 async def create_text_chat_session(
     workflow_id: int,
     request: CreateTextChatSessionRequest,
-    user: UserModel = Depends(get_user),
+    user: UserModel = Depends(get_user_with_selected_organization),
 ) -> WorkflowRunTextSessionResponse:
-    organization_id = _require_selected_organization_id(user)
-    await _ensure_text_chat_quota(user, workflow_id)
-
     session_name = request.name or f"WR-TEXT-{uuid4().hex[:6].upper()}"
     try:
         workflow_run = await db_client.create_workflow_run(
@@ -172,12 +170,13 @@ async def create_text_chat_session(
             user_id=user.id,
             initial_context=request.initial_context,
             use_draft=True,
-            organization_id=organization_id,
+            organization_id=user.selected_organization_id,
         )
     except ValueError as e:
         raise HTTPException(status_code=404, detail=str(e))
 
     set_current_run_id(workflow_run.id)
+    await _ensure_text_chat_quota(user, workflow_id, workflow_run.id)
 
     annotations = {
         "tester": {
@@ -220,7 +219,7 @@ async def create_text_chat_session(
 async def get_text_chat_session(
     workflow_id: int,
     run_id: int,
-    user: UserModel = Depends(get_user),
+    user: UserModel = Depends(get_user_with_selected_organization),
 ) -> WorkflowRunTextSessionResponse:
     text_session = await _load_text_session_or_404(workflow_id, run_id, user)
     return _build_response(text_session)
@@ -234,10 +233,10 @@ async def append_text_chat_message(
     workflow_id: int,
     run_id: int,
     request: AppendTextChatMessageRequest,
-    user: UserModel = Depends(get_user),
+    user: UserModel = Depends(get_user_with_selected_organization),
 ) -> WorkflowRunTextSessionResponse:
     text_session = await _load_text_session_or_404(workflow_id, run_id, user)
-    await _ensure_text_chat_quota(user, workflow_id)
+    await _ensure_text_chat_quota(user, workflow_id, run_id)
 
     try:
         text_session = await append_text_chat_user_message(
@@ -264,7 +263,7 @@ async def rewind_text_chat_session(
     workflow_id: int,
     run_id: int,
     request: RewindTextChatSessionRequest,
-    user: UserModel = Depends(get_user),
+    user: UserModel = Depends(get_user_with_selected_organization),
 ) -> WorkflowRunTextSessionResponse:
     text_session = await _load_text_session_or_404(workflow_id, run_id, user)
     try:

api/schemas/ai_model_configuration.py

@@ -0,0 +1,198 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Literal
+
+from pydantic import BaseModel, Field, model_validator
+
+from api.services.configuration.registry import (
+    DograhEmbeddingsConfiguration,
+    DograhLLMService,
+    DograhSTTService,
+    DograhTTSService,
+    EmbeddingsConfig,
+    LLMConfig,
+    RealtimeConfig,
+    ServiceProviders,
+    STTConfig,
+    TTSConfig,
+)
+
+DOGRAH_SPEED_OPTIONS: tuple[float, ...] = (0.8, 1.0, 1.2)
+DOGRAH_DEFAULT_VOICE = "default"
+DOGRAH_DEFAULT_LANGUAGE = "multi"
+
+
+class EffectiveAIModelConfiguration(BaseModel):
+    llm: LLMConfig | None = None
+    stt: STTConfig | None = None
+    tts: TTSConfig | None = None
+    embeddings: EmbeddingsConfig | None = None
+    realtime: RealtimeConfig | None = None
+    is_realtime: bool = False
+    managed_service_version: int | None = None
+    test_phone_number: str | None = None
+    timezone: str | None = None
+    last_validated_at: datetime | None = None
+    # Post-signup onboarding gate: set once the user submits or skips the
+    # onboarding form, so it shows only once per user.
+    onboarding_completed_at: datetime | None = None
+    onboarding_skipped: bool = False
+
+    @model_validator(mode="before")
+    @classmethod
+    def strip_incomplete_realtime_when_disabled(cls, data):
+        """Skip realtime validation when is_realtime is False and api_key is missing."""
+        if isinstance(data, dict) and not data.get("is_realtime", False):
+            realtime = data.get("realtime")
+            if isinstance(realtime, dict) and not realtime.get("api_key"):
+                data.pop("realtime", None)
+        return data
+
+
+class DograhManagedAIModelConfiguration(BaseModel):
+    api_key: str
+    voice: str = DOGRAH_DEFAULT_VOICE
+    speed: float = Field(default=1.0)
+    language: str = DOGRAH_DEFAULT_LANGUAGE
+
+    @model_validator(mode="after")
+    def validate_speed(self):
+        if self.speed not in DOGRAH_SPEED_OPTIONS:
+            allowed = ", ".join(str(speed) for speed in DOGRAH_SPEED_OPTIONS)
+            raise ValueError(f"Dograh speed must be one of: {allowed}")
+        return self
+
+
+class BYOKPipelineAIModelConfiguration(BaseModel):
+    llm: LLMConfig
+    tts: TTSConfig
+    stt: STTConfig
+    embeddings: EmbeddingsConfig | None = None
+
+    @model_validator(mode="after")
+    def reject_dograh_providers(self):
+        _reject_dograh_provider("llm", self.llm)
+        _reject_dograh_provider("tts", self.tts)
+        _reject_dograh_provider("stt", self.stt)
+        _reject_dograh_provider("embeddings", self.embeddings)
+        return self
+
+
+class BYOKRealtimeAIModelConfiguration(BaseModel):
+    realtime: RealtimeConfig
+    llm: LLMConfig
+    embeddings: EmbeddingsConfig | None = None
+
+    @model_validator(mode="after")
+    def reject_dograh_providers(self):
+        _reject_dograh_provider("llm", self.llm)
+        _reject_dograh_provider("embeddings", self.embeddings)
+        return self
+
+
+class BYOKAIModelConfiguration(BaseModel):
+    mode: Literal["pipeline", "realtime"]
+    pipeline: BYOKPipelineAIModelConfiguration | None = None
+    realtime: BYOKRealtimeAIModelConfiguration | None = None
+
+    @model_validator(mode="after")
+    def validate_selected_mode(self):
+        if self.mode == "pipeline" and self.pipeline is None:
+            raise ValueError("byok.pipeline is required when byok.mode is pipeline")
+        if self.mode == "realtime" and self.realtime is None:
+            raise ValueError("byok.realtime is required when byok.mode is realtime")
+        return self
+
+
+class OrganizationAIModelConfigurationV2(BaseModel):
+    version: Literal[2] = 2
+    mode: Literal["dograh", "byok"]
+    dograh: DograhManagedAIModelConfiguration | None = None
+    byok: BYOKAIModelConfiguration | None = None
+
+    @model_validator(mode="after")
+    def validate_selected_mode(self):
+        if self.mode == "dograh" and self.dograh is None:
+            raise ValueError("dograh configuration is required when mode is dograh")
+        if self.mode == "byok" and self.byok is None:
+            raise ValueError("byok configuration is required when mode is byok")
+        return self
+
+
+class OrganizationAIModelConfigurationResponse(BaseModel):
+    configuration: dict | None
+    effective_configuration: dict
+    source: Literal["organization_v2", "legacy_user_v1", "empty"]
+
+
+def compile_ai_model_configuration_v2(
+    configuration: OrganizationAIModelConfigurationV2,
+) -> EffectiveAIModelConfiguration:
+    if configuration.mode == "dograh":
+        if configuration.dograh is None:
+            raise ValueError("dograh configuration is required")
+        return _compile_dograh_configuration(configuration.dograh)
+
+    if configuration.byok is None:
+        raise ValueError("byok configuration is required")
+    if configuration.byok.mode == "pipeline":
+        if configuration.byok.pipeline is None:
+            raise ValueError("byok.pipeline is required")
+        pipeline = configuration.byok.pipeline
+        return EffectiveAIModelConfiguration(
+            llm=pipeline.llm,
+            tts=pipeline.tts,
+            stt=pipeline.stt,
+            embeddings=pipeline.embeddings,
+            is_realtime=False,
+        )
+
+    if configuration.byok.realtime is None:
+        raise ValueError("byok.realtime is required")
+    realtime = configuration.byok.realtime
+    return EffectiveAIModelConfiguration(
+        llm=realtime.llm,
+        realtime=realtime.realtime,
+        embeddings=realtime.embeddings,
+        is_realtime=True,
+    )
+
+
+def _compile_dograh_configuration(
+    configuration: DograhManagedAIModelConfiguration,
+) -> EffectiveAIModelConfiguration:
+    return EffectiveAIModelConfiguration(
+        llm=DograhLLMService(
+            provider=ServiceProviders.DOGRAH,
+            api_key=configuration.api_key,
+            model="default",
+        ),
+        tts=DograhTTSService(
+            provider=ServiceProviders.DOGRAH,
+            api_key=configuration.api_key,
+            model="default",
+            voice=configuration.voice,
+            speed=configuration.speed,
+        ),
+        stt=DograhSTTService(
+            provider=ServiceProviders.DOGRAH,
+            api_key=configuration.api_key,
+            model="default",
+            language=configuration.language,
+        ),
+        embeddings=DograhEmbeddingsConfiguration(
+            provider=ServiceProviders.DOGRAH,
+            api_key=configuration.api_key,
+            model="default",
+        ),
+        is_realtime=False,
+        managed_service_version=2,
+    )
+
+
+def _reject_dograh_provider(section: str, service) -> None:
+    if service is None:
+        return
+    if getattr(service, "provider", None) == ServiceProviders.DOGRAH:
+        raise ValueError(f"BYOK {section} cannot use Dograh provider")

api/schemas/organization_preferences.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+
+
+class OrganizationPreferences(BaseModel):
+    test_phone_number: str | None = None
+    timezone: str | None = None

api/schemas/user_configuration.py

@@ -1,37 +0,0 @@
-from datetime import datetime
-
-from pydantic import BaseModel, model_validator
-
-from api.services.configuration.registry import (
-    EmbeddingsConfig,
-    LLMConfig,
-    RealtimeConfig,
-    STTConfig,
-    TTSConfig,
-)
-
-
-class UserConfiguration(BaseModel):
-    llm: LLMConfig | None = None
-    stt: STTConfig | None = None
-    tts: TTSConfig | None = None
-    embeddings: EmbeddingsConfig | None = None
-    realtime: RealtimeConfig | None = None
-    is_realtime: bool = False
-    test_phone_number: str | None = None
-    timezone: str | None = None
-    last_validated_at: datetime | None = None
-    # Post-signup onboarding gate: set once the user submits or skips the
-    # onboarding form, so it shows only once per user (server-side, cross-device).
-    onboarding_completed_at: datetime | None = None
-    onboarding_skipped: bool = False
-
-    @model_validator(mode="before")
-    @classmethod
-    def strip_incomplete_realtime_when_disabled(cls, data):
-        """Skip realtime validation when is_realtime is False and api_key is missing."""
-        if isinstance(data, dict) and not data.get("is_realtime", False):
-            realtime = data.get("realtime")
-            if isinstance(realtime, dict) and not realtime.get("api_key"):
-                data.pop("realtime", None)
-        return data

api/services/auth/depends.py

@@ -1,7 +1,7 @@
 from typing import Annotated, Optional
 
 import httpx
-from fastapi import Header, HTTPException, Query, WebSocket
+from fastapi import Depends, Header, HTTPException, Query, WebSocket
 from loguru import logger
 from pydantic import ValidationError
 
@@ -9,9 +9,10 @@ from api.constants import AUTH_PROVIDER, DOGRAH_MPS_SECRET_KEY, MPS_API_URL
 from api.db import db_client
 from api.db.models import UserModel
 from api.enums import PostHogEvent
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.auth.stack_auth import stackauth
 from api.services.configuration.registry import ServiceProviders
+from api.services.mps_billing import ensure_hosted_mps_billing_account_v2
 from api.services.posthog_client import capture_event
 from api.utils.auth import decode_jwt_token
 
@@ -110,6 +111,19 @@ async def get_user(
             # This prevents race conditions where multiple concurrent requests
             # might try to create configurations
             if org_was_created:
+                try:
+                    await ensure_hosted_mps_billing_account_v2(
+                        organization.id,
+                        created_by=str(stack_user["id"]),
+                    )
+                except Exception:
+                    logger.warning(
+                        "Failed to initialize hosted MPS billing account for "
+                        "organization {}",
+                        organization.id,
+                        exc_info=True,
+                    )
+
                 existing_cfg = await db_client.get_user_configurations(user_model.id)
                 if not (existing_cfg.llm or existing_cfg.tts or existing_cfg.stt):
                     mps_config = await create_user_configuration_with_mps_key(
@@ -119,6 +133,19 @@ async def get_user(
                         await db_client.update_user_configuration(
                             user_model.id, mps_config
                         )
+                        from api.enums import OrganizationConfigurationKey
+                        from api.services.configuration.ai_model_configuration import (
+                            convert_legacy_ai_model_configuration_to_v2,
+                        )
+
+                        model_config_v2 = convert_legacy_ai_model_configuration_to_v2(
+                            mps_config
+                        )
+                        await db_client.upsert_configuration(
+                            organization.id,
+                            OrganizationConfigurationKey.MODEL_CONFIGURATION_V2.value,
+                            model_config_v2.model_dump(mode="json", exclude_none=True),
+                        )
 
     except Exception as exc:
         raise HTTPException(
@@ -129,6 +156,14 @@ async def get_user(
     return user_model
 
 
+async def get_user_with_selected_organization(
+    user: Annotated[UserModel, Depends(get_user)],
+) -> UserModel:
+    if not user.selected_organization_id:
+        raise HTTPException(status_code=400, detail="No organization selected")
+    return user
+
+
 async def _handle_oss_auth(authorization: str | None) -> UserModel:
     """
     Handle authentication for OSS deployment mode.
@@ -192,7 +227,7 @@ async def _handle_api_key_auth(api_key: str) -> UserModel:
 
 async def create_user_configuration_with_mps_key(
     user_id: int, organization_id: int, user_provider_id: str
-) -> Optional[UserConfiguration]:
+) -> Optional[EffectiveAIModelConfiguration]:
     """Create user configuration using MPS service key.
 
     Args:
@@ -201,7 +236,7 @@ async def create_user_configuration_with_mps_key(
         user_provider_id: The user's provider ID (for created_by field)
 
     Returns:
-        UserConfiguration with MPS-provided API keys or None if failed
+        EffectiveAIModelConfiguration with MPS-provided API keys or None if failed
     """
 
     async with httpx.AsyncClient() as client:
@@ -211,7 +246,7 @@ async def create_user_configuration_with_mps_key(
             response = await client.post(
                 f"{MPS_API_URL}/api/v1/service-keys/",
                 json={
-                    "name": f"Default Dograh Model Service Key",
+                    "name": "Default Dograh Model Service Key",
                     "description": "Auto-generated key for OSS user",
                     "expires_in_days": 7,  # Short-lived for OSS
                     "created_by": user_provider_id,
@@ -229,7 +264,7 @@ async def create_user_configuration_with_mps_key(
             response = await client.post(
                 f"{MPS_API_URL}/api/v1/service-keys/",
                 json={
-                    "name": f"Default Dograh Model Service Key",
+                    "name": "Default Dograh Model Service Key",
                     "description": f"Auto-generated key for organization {organization_id}",
                     "organization_id": organization_id,
                     "expires_in_days": 90,  # Longer-lived for authenticated users
@@ -264,8 +299,8 @@ async def create_user_configuration_with_mps_key(
                         "model": "default",
                     },
                 }
-                user_config = UserConfiguration(**configuration)
-                return user_config
+                effective_config = EffectiveAIModelConfiguration(**configuration)
+                return effective_config
         else:
             logger.warning(
                 f"Failed to get MPS service key: {response.status_code} - {response.text}"

api/services/campaign/campaign_call_dispatcher.py

@@ -15,6 +15,7 @@ from api.services.campaign.errors import (
     PhoneNumberPoolExhaustedError,
 )
 from api.services.campaign.rate_limiter import rate_limiter
+from api.services.quota_service import authorize_workflow_run_start
 from api.utils.common import get_backend_endpoints
 
 if TYPE_CHECKING:
@@ -339,6 +340,41 @@ class CampaignCallDispatcher:
                 },
             )
 
+        quota_result = await authorize_workflow_run_start(
+            workflow_id=campaign.workflow_id,
+            workflow_run_id=workflow_run.id,
+        )
+        if not quota_result.has_quota:
+            error_message = quota_result.error_message or "Quota exceeded"
+            logger.warning(
+                f"Campaign {campaign.id} quota check failed for workflow run "
+                f"{workflow_run.id}: {error_message}"
+            )
+            await db_client.update_workflow_run(
+                run_id=workflow_run.id,
+                is_completed=True,
+                state=WorkflowRunState.COMPLETED.value,
+                gathered_context={"error": error_message},
+            )
+
+            mapping = await rate_limiter.get_workflow_slot_mapping(workflow_run.id)
+            if mapping:
+                org_id, mapped_slot_id = mapping
+                await rate_limiter.release_concurrent_slot(org_id, mapped_slot_id)
+                await rate_limiter.delete_workflow_slot_mapping(workflow_run.id)
+
+            from_number_mapping = await rate_limiter.get_workflow_from_number_mapping(
+                workflow_run.id
+            )
+            if from_number_mapping:
+                fn_org_id, fn_number, fn_tcid = from_number_mapping
+                await rate_limiter.release_from_number(
+                    fn_org_id, fn_number, telephony_configuration_id=fn_tcid
+                )
+                await rate_limiter.delete_workflow_from_number_mapping(workflow_run.id)
+
+            raise ValueError(error_message)
+
         # Initiate call via telephony provider
         try:
             # Construct webhook URL with parameters

api/services/configuration/ai_model_configuration.py

@@ -0,0 +1,484 @@
+from __future__ import annotations
+
+import copy
+from dataclasses import dataclass
+from typing import Literal
+
+from loguru import logger
+from pydantic import ValidationError
+from sqlalchemy import select, update
+from sqlalchemy.orm import selectinload
+
+from api.constants import MPS_API_URL
+from api.db import db_client
+from api.db.models import WorkflowDefinitionModel, WorkflowModel
+from api.enums import OrganizationConfigurationKey
+from api.schemas.ai_model_configuration import (
+    DOGRAH_DEFAULT_LANGUAGE,
+    DOGRAH_DEFAULT_VOICE,
+    DOGRAH_SPEED_OPTIONS,
+    BYOKAIModelConfiguration,
+    BYOKPipelineAIModelConfiguration,
+    BYOKRealtimeAIModelConfiguration,
+    DograhManagedAIModelConfiguration,
+    EffectiveAIModelConfiguration,
+    OrganizationAIModelConfigurationV2,
+    compile_ai_model_configuration_v2,
+)
+from api.services.configuration.masking import (
+    SERVICE_SECRET_FIELDS,
+    contains_masked_key,
+    mask_key,
+    resolve_masked_api_keys,
+)
+from api.services.configuration.registry import ServiceProviders
+from api.services.configuration.resolve import resolve_effective_config
+
+AIModelConfigurationSource = Literal["organization_v2", "legacy_user_v1", "empty"]
+WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY = "model_configuration_v2_override"
+
+
+@dataclass
+class ResolvedAIModelConfiguration:
+    effective: EffectiveAIModelConfiguration
+    source: AIModelConfigurationSource
+    organization_configuration: OrganizationAIModelConfigurationV2 | None = None
+
+
+@dataclass
+class WorkflowAIModelConfigurationMigrationResult:
+    workflow_count: int = 0
+    definition_count: int = 0
+    workflow_ids: list[int] | None = None
+
+
+async def get_resolved_ai_model_configuration(
+    *,
+    user_id: int | None,
+    organization_id: int | None,
+) -> ResolvedAIModelConfiguration:
+    organization_configuration = await get_organization_ai_model_configuration_v2(
+        organization_id
+    )
+    if organization_configuration is not None:
+        return ResolvedAIModelConfiguration(
+            effective=compile_ai_model_configuration_v2(organization_configuration),
+            source="organization_v2",
+            organization_configuration=organization_configuration,
+        )
+
+    if user_id is None:
+        return ResolvedAIModelConfiguration(
+            effective=EffectiveAIModelConfiguration(),
+            source="empty",
+        )
+
+    legacy = await db_client.get_user_configurations(user_id)
+    return ResolvedAIModelConfiguration(
+        effective=legacy,
+        source="legacy_user_v1" if _has_model_services(legacy) else "empty",
+    )
+
+
+async def get_effective_ai_model_configuration_for_workflow(
+    *,
+    user_id: int | None,
+    organization_id: int | None,
+    workflow_configurations: dict | None,
+) -> EffectiveAIModelConfiguration:
+    workflow_configurations = workflow_configurations or {}
+    v2_override = workflow_configurations.get(
+        WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY
+    )
+    if v2_override:
+        return compile_ai_model_configuration_v2(
+            OrganizationAIModelConfigurationV2.model_validate(v2_override)
+        )
+
+    resolved_config = await get_resolved_ai_model_configuration(
+        user_id=user_id,
+        organization_id=organization_id,
+    )
+    return resolve_effective_config(
+        resolved_config.effective,
+        workflow_configurations.get("model_overrides"),
+    )
+
+
+async def get_organization_ai_model_configuration_v2(
+    organization_id: int | None,
+) -> OrganizationAIModelConfigurationV2 | None:
+    if organization_id is None:
+        return None
+    row = await db_client.get_configuration(
+        organization_id,
+        OrganizationConfigurationKey.MODEL_CONFIGURATION_V2.value,
+    )
+    if row is None or not row.value:
+        return None
+    try:
+        return OrganizationAIModelConfigurationV2.model_validate(row.value)
+    except ValidationError as exc:
+        logger.warning(
+            "Invalid org AI model configuration v2 for organization "
+            f"{organization_id}: {exc}. Falling back to legacy configuration."
+        )
+        return None
+
+
+async def upsert_organization_ai_model_configuration_v2(
+    organization_id: int,
+    configuration: OrganizationAIModelConfigurationV2,
+) -> OrganizationAIModelConfigurationV2:
+    await db_client.upsert_configuration(
+        organization_id,
+        OrganizationConfigurationKey.MODEL_CONFIGURATION_V2.value,
+        configuration.model_dump(mode="json", exclude_none=True),
+    )
+    return configuration
+
+
+async def migrate_workflow_model_configurations_to_v2(
+    *,
+    organization_id: int,
+    fallback_user_config: EffectiveAIModelConfiguration,
+) -> WorkflowAIModelConfigurationMigrationResult:
+    workflows = await _list_workflows_for_model_configuration_migration(organization_id)
+    owner_configs: dict[int, EffectiveAIModelConfiguration] = {}
+    workflow_updates: list[tuple[int, dict]] = []
+    definition_updates: list[tuple[int, dict]] = []
+    migrated_workflow_ids: set[int] = set()
+
+    for workflow in workflows:
+        base_config = fallback_user_config
+        if workflow.user_id is not None:
+            if workflow.user_id not in owner_configs:
+                owner_configs[
+                    workflow.user_id
+                ] = await db_client.get_user_configurations(workflow.user_id)
+            base_config = owner_configs[workflow.user_id]
+
+        workflow_configs, workflow_changed = (
+            migrate_workflow_configuration_model_override_to_v2(
+                workflow.workflow_configurations,
+                base_config,
+            )
+        )
+        if workflow_changed:
+            workflow_updates.append((workflow.id, workflow_configs))
+            migrated_workflow_ids.add(workflow.id)
+
+        for definition in workflow.definitions:
+            definition_configs, definition_changed = (
+                migrate_workflow_configuration_model_override_to_v2(
+                    definition.workflow_configurations,
+                    base_config,
+                )
+            )
+            if definition_changed:
+                definition_updates.append((definition.id, definition_configs))
+                migrated_workflow_ids.add(workflow.id)
+
+    if workflow_updates or definition_updates:
+        async with db_client.async_session() as session:
+            for workflow_id, workflow_configs in workflow_updates:
+                await session.execute(
+                    update(WorkflowModel)
+                    .where(WorkflowModel.id == workflow_id)
+                    .values(workflow_configurations=workflow_configs)
+                )
+            for definition_id, definition_configs in definition_updates:
+                await session.execute(
+                    update(WorkflowDefinitionModel)
+                    .where(WorkflowDefinitionModel.id == definition_id)
+                    .values(workflow_configurations=definition_configs)
+                )
+            await session.commit()
+
+    return WorkflowAIModelConfigurationMigrationResult(
+        workflow_count=len(migrated_workflow_ids),
+        definition_count=len(definition_updates),
+        workflow_ids=sorted(migrated_workflow_ids),
+    )
+
+
+def migrate_workflow_configuration_model_override_to_v2(
+    workflow_configurations: dict | None,
+    base_config: EffectiveAIModelConfiguration,
+) -> tuple[dict, bool]:
+    if not isinstance(workflow_configurations, dict):
+        return {}, False
+
+    migrated = copy.deepcopy(workflow_configurations)
+    model_overrides = migrated.get("model_overrides")
+    existing_v2_override = migrated.get(WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY)
+    if not isinstance(model_overrides, dict):
+        if "model_overrides" in migrated:
+            migrated.pop("model_overrides", None)
+            return migrated, True
+        return migrated, False
+
+    if not existing_v2_override:
+        effective = resolve_effective_config(base_config, model_overrides)
+        v2_override = convert_legacy_ai_model_configuration_to_v2(effective)
+        migrated[WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY] = v2_override.model_dump(
+            mode="json", exclude_none=True
+        )
+    migrated.pop("model_overrides", None)
+    return migrated, True
+
+
+def merge_ai_model_configuration_v2_secrets(
+    incoming: OrganizationAIModelConfigurationV2,
+    existing: OrganizationAIModelConfigurationV2 | None,
+) -> OrganizationAIModelConfigurationV2:
+    if existing is None:
+        return incoming
+
+    incoming_dict = incoming.model_dump(mode="json", exclude_none=True)
+    existing_dict = existing.model_dump(mode="json", exclude_none=True)
+
+    if incoming_dict.get("mode") == "dograh" and existing_dict.get("mode") == "dograh":
+        incoming_dograh = incoming_dict.get("dograh") or {}
+        existing_dograh = existing_dict.get("dograh") or {}
+        incoming_key = incoming_dograh.get("api_key")
+        existing_key = existing_dograh.get("api_key")
+        if incoming_key and existing_key and contains_masked_key(incoming_key):
+            incoming_dograh["api_key"] = resolve_masked_api_keys(
+                incoming_key,
+                existing_key,
+            )
+
+    if incoming_dict.get("mode") == "byok" and existing_dict.get("mode") == "byok":
+        _merge_byok_secret_fields(incoming_dict.get("byok"), existing_dict.get("byok"))
+
+    return OrganizationAIModelConfigurationV2.model_validate(incoming_dict)
+
+
+def check_for_masked_keys_in_ai_model_configuration_v2(
+    configuration: OrganizationAIModelConfigurationV2,
+) -> None:
+    data = configuration.model_dump(mode="json", exclude_none=True)
+    _raise_if_masked_secret(data)
+
+
+def mask_ai_model_configuration_v2(
+    configuration: OrganizationAIModelConfigurationV2 | None,
+) -> dict | None:
+    if configuration is None:
+        return None
+    data = configuration.model_dump(mode="json", exclude_none=True)
+    _mask_secret_fields(data)
+    return data
+
+
+def convert_legacy_ai_model_configuration_to_v2(
+    configuration: EffectiveAIModelConfiguration,
+) -> OrganizationAIModelConfigurationV2:
+    dograh_key = _first_dograh_api_key(configuration)
+    if dograh_key:
+        return _convert_any_dograh_legacy_configuration(configuration, dograh_key)
+
+    if configuration.is_realtime:
+        if configuration.realtime is None or configuration.llm is None:
+            raise ValueError("Realtime legacy configuration is incomplete")
+        return OrganizationAIModelConfigurationV2(
+            mode="byok",
+            byok=BYOKAIModelConfiguration(
+                mode="realtime",
+                realtime=BYOKRealtimeAIModelConfiguration(
+                    realtime=configuration.realtime,
+                    llm=configuration.llm,
+                    embeddings=configuration.embeddings,
+                ),
+            ),
+        )
+
+    if (
+        configuration.llm is None
+        or configuration.tts is None
+        or configuration.stt is None
+    ):
+        raise ValueError("Pipeline legacy configuration is incomplete")
+    return OrganizationAIModelConfigurationV2(
+        mode="byok",
+        byok=BYOKAIModelConfiguration(
+            mode="pipeline",
+            pipeline=BYOKPipelineAIModelConfiguration(
+                llm=configuration.llm,
+                tts=configuration.tts,
+                stt=configuration.stt,
+                embeddings=configuration.embeddings,
+            ),
+        ),
+    )
+
+
+def dograh_embeddings_base_url() -> str:
+    return f"{MPS_API_URL}/api/v1/llm"
+
+
+def apply_managed_embeddings_base_url(
+    *,
+    provider: str | None,
+    base_url: str | None,
+) -> str | None:
+    if provider == ServiceProviders.DOGRAH.value or provider == ServiceProviders.DOGRAH:
+        return dograh_embeddings_base_url()
+    return base_url
+
+
+def _merge_byok_secret_fields(incoming_byok: dict | None, existing_byok: dict | None):
+    if not isinstance(incoming_byok, dict) or not isinstance(existing_byok, dict):
+        return
+    incoming_mode = incoming_byok.get("mode")
+    existing_mode = existing_byok.get("mode")
+    if incoming_mode != existing_mode:
+        return
+    section_names = (
+        ("llm", "tts", "stt", "embeddings")
+        if incoming_mode == "pipeline"
+        else ("realtime", "llm", "embeddings")
+    )
+    incoming_container = incoming_byok.get(incoming_mode)
+    existing_container = existing_byok.get(existing_mode)
+    if not isinstance(incoming_container, dict) or not isinstance(
+        existing_container, dict
+    ):
+        return
+    for section_name in section_names:
+        incoming_section = incoming_container.get(section_name)
+        existing_section = existing_container.get(section_name)
+        if isinstance(incoming_section, dict) and isinstance(existing_section, dict):
+            _merge_service_secret_fields(incoming_section, existing_section)
+
+
+async def _list_workflows_for_model_configuration_migration(
+    organization_id: int,
+) -> list[WorkflowModel]:
+    async with db_client.async_session() as session:
+        result = await session.execute(
+            select(WorkflowModel)
+            .options(selectinload(WorkflowModel.definitions))
+            .where(WorkflowModel.organization_id == organization_id)
+        )
+        return list(result.scalars().unique().all())
+
+
+def _merge_service_secret_fields(incoming: dict, existing: dict):
+    if (
+        incoming.get("provider") is not None
+        and existing.get("provider") is not None
+        and incoming.get("provider") != existing.get("provider")
+    ):
+        return
+    for secret_field in SERVICE_SECRET_FIELDS:
+        if secret_field not in existing:
+            continue
+        incoming_secret = incoming.get(secret_field)
+        existing_secret = existing[secret_field]
+        if incoming_secret is None:
+            incoming[secret_field] = existing_secret
+        elif contains_masked_key(incoming_secret):
+            incoming[secret_field] = resolve_masked_api_keys(
+                incoming_secret,
+                existing_secret,
+            )
+
+
+def _raise_if_masked_secret(value):
+    if isinstance(value, dict):
+        for key, nested in value.items():
+            if key in SERVICE_SECRET_FIELDS and contains_masked_key(nested):
+                raise ValueError(
+                    f"The {key} appears to be masked. Please provide the actual "
+                    "value, not the masked value."
+                )
+            _raise_if_masked_secret(nested)
+    elif isinstance(value, list):
+        for item in value:
+            _raise_if_masked_secret(item)
+
+
+def _mask_secret_fields(value):
+    if isinstance(value, dict):
+        for key, nested in list(value.items()):
+            if key in SERVICE_SECRET_FIELDS and nested:
+                value[key] = _mask_secret_value(nested)
+            else:
+                _mask_secret_fields(nested)
+    elif isinstance(value, list):
+        for item in value:
+            _mask_secret_fields(item)
+
+
+def _mask_secret_value(value):
+    if isinstance(value, list):
+        return [mask_key(item) for item in value]
+    return mask_key(value)
+
+
+def _has_model_services(configuration: EffectiveAIModelConfiguration) -> bool:
+    return any(
+        service is not None
+        for service in (
+            configuration.llm,
+            configuration.tts,
+            configuration.stt,
+            configuration.embeddings,
+            configuration.realtime,
+        )
+    )
+
+
+def _convert_any_dograh_legacy_configuration(
+    configuration: EffectiveAIModelConfiguration,
+    dograh_key: str,
+) -> OrganizationAIModelConfigurationV2:
+    speed = getattr(configuration.tts, "speed", 1.0)
+    if speed not in DOGRAH_SPEED_OPTIONS:
+        speed = 1.0
+    return OrganizationAIModelConfigurationV2(
+        mode="dograh",
+        dograh=DograhManagedAIModelConfiguration(
+            api_key=dograh_key,
+            voice=getattr(configuration.tts, "voice", DOGRAH_DEFAULT_VOICE)
+            or DOGRAH_DEFAULT_VOICE,
+            speed=speed,
+            language=getattr(configuration.stt, "language", DOGRAH_DEFAULT_LANGUAGE)
+            or DOGRAH_DEFAULT_LANGUAGE,
+        ),
+    )
+
+
+def _first_dograh_api_key(configuration: EffectiveAIModelConfiguration) -> str | None:
+    for service in (
+        configuration.llm,
+        configuration.tts,
+        configuration.stt,
+        configuration.embeddings,
+        configuration.realtime,
+    ):
+        if service is None or _provider(service) != ServiceProviders.DOGRAH:
+            continue
+        try:
+            return _single_api_key(service)
+        except ValueError:
+            continue
+    return None
+
+
+def _provider(service):
+    return getattr(service, "provider", None)
+
+
+def _single_api_key(service) -> str:
+    if hasattr(service, "get_all_api_keys"):
+        keys = service.get_all_api_keys()
+        if len(keys) != 1:
+            raise ValueError("Expected exactly one API key")
+        return keys[0]
+    key = getattr(service, "api_key", None)
+    if not key:
+        raise ValueError("Expected an API key")
+    return key

api/services/configuration/check_validity.py

@@ -8,8 +8,8 @@ from groq import Groq
 #     from pyneuphonic import Neuphonic
 # except ImportError:
 #     Neuphonic = None
-from api.schemas.user_configuration import (
-    UserConfiguration,
+from api.schemas.ai_model_configuration import (
+    EffectiveAIModelConfiguration,
 )
 from api.services.configuration.registry import ServiceConfig, ServiceProviders
 from api.services.mps_service_key_client import mps_service_key_client
@@ -64,7 +64,7 @@ class UserConfigurationValidator:
 
     async def validate(
         self,
-        configuration: UserConfiguration,
+        configuration: EffectiveAIModelConfiguration,
         organization_id: Optional[int] = None,
         created_by: Optional[str] = None,
     ) -> APIKeyStatusResponse:
@@ -75,21 +75,21 @@ class UserConfigurationValidator:
         status_list = []
 
         status_list.extend(self._validate_service(configuration.llm, "llm"))
-        status_list.extend(self._validate_service(configuration.stt, "stt"))
-        status_list.extend(self._validate_service(configuration.tts, "tts"))
-        # Embeddings is optional - only validate if configured
-        status_list.extend(
-            self._validate_service(
-                configuration.embeddings, "embeddings", required=False
-            )
-        )
-        # Realtime is optional - only validate if is_realtime is enabled
         if configuration.is_realtime:
             status_list.extend(
                 self._validate_service(
                     configuration.realtime, "realtime", required=True
                 )
             )
+        else:
+            status_list.extend(self._validate_service(configuration.stt, "stt"))
+            status_list.extend(self._validate_service(configuration.tts, "tts"))
+        # Embeddings is optional - only validate if configured
+        status_list.extend(
+            self._validate_service(
+                configuration.embeddings, "embeddings", required=False
+            )
+        )
 
         if status_list:
             raise ValueError(status_list)

api/services/configuration/masking.py

@@ -12,7 +12,7 @@ The rules are simple:
 import copy
 from typing import Any, Dict, Optional
 
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.configuration.registry import ServiceConfig
 from api.services.integrations import get_node_secret_fields
 
@@ -31,7 +31,7 @@ def contains_masked_key(value: str | list[str] | None) -> bool:
     return any(MASK_MARKER in k for k in keys)
 
 
-def check_for_masked_keys(config: "UserConfiguration") -> None:
+def check_for_masked_keys(config: "EffectiveAIModelConfiguration") -> None:
     """Raise ValueError if any service in *config* still has a masked secret."""
     for field in ("llm", "tts", "stt", "embeddings", "realtime"):
         service = getattr(config, field, None)
@@ -111,7 +111,7 @@ def resolve_masked_api_keys(
 
 
 # ---------------------------------------------------------------------------
-# High-level helpers for UserConfiguration objects
+# High-level helpers for EffectiveAIModelConfiguration objects
 # ---------------------------------------------------------------------------
 
 
@@ -129,7 +129,7 @@ def _mask_service(service_cfg: Optional[ServiceConfig]) -> Optional[Dict[str, An
     return data
 
 
-def mask_user_config(config: UserConfiguration) -> Dict[str, Any]:
+def mask_user_config(config: EffectiveAIModelConfiguration) -> Dict[str, Any]:
     """Return a JSON-serialisable dict of *config* with every api_key masked."""
 
     return {
@@ -155,21 +155,35 @@ def mask_workflow_configurations(config: Optional[Dict]) -> Optional[Dict]:
 
     masked = copy.deepcopy(config)
     model_overrides = masked.get("model_overrides")
-    if not isinstance(model_overrides, dict):
-        return masked
+    if isinstance(model_overrides, dict):
+        for section in MODEL_OVERRIDE_FIELDS:
+            override = model_overrides.get(section)
+            if not isinstance(override, dict):
+                continue
+            for secret_field in SERVICE_SECRET_FIELDS:
+                raw = override.get(secret_field)
+                if raw:
+                    override[secret_field] = _mask_secret_value(raw)
 
-    for section in MODEL_OVERRIDE_FIELDS:
-        override = model_overrides.get(section)
-        if not isinstance(override, dict):
-            continue
-        for secret_field in SERVICE_SECRET_FIELDS:
-            raw = override.get(secret_field)
-            if raw:
-                override[secret_field] = _mask_secret_value(raw)
+    v2_override = masked.get("model_configuration_v2_override")
+    if isinstance(v2_override, dict):
+        _mask_nested_service_secrets(v2_override)
 
     return masked
 
 
+def _mask_nested_service_secrets(value):
+    if isinstance(value, dict):
+        for key, nested in list(value.items()):
+            if key in SERVICE_SECRET_FIELDS and nested:
+                value[key] = _mask_secret_value(nested)
+            else:
+                _mask_nested_service_secrets(nested)
+    elif isinstance(value, list):
+        for item in value:
+            _mask_nested_service_secrets(item)
+
+
 # ---------------------------------------------------------------------------
 # Workflow definition helpers – mask / merge node API keys
 # ---------------------------------------------------------------------------

api/services/configuration/merge.py

@@ -7,7 +7,7 @@ stored, while honouring masked API keys.
 import copy
 from typing import Dict
 
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.configuration.masking import (
     MODEL_OVERRIDE_FIELDS,
     SERVICE_SECRET_FIELDS,
@@ -66,9 +66,9 @@ def _merge_service_secret_fields(
 
 
 def merge_user_configurations(
-    existing: UserConfiguration, incoming_partial: Dict[str, dict]
-) -> UserConfiguration:
-    """Merge *incoming_partial* onto *existing* and return a new UserConfiguration.
+    existing: EffectiveAIModelConfiguration, incoming_partial: Dict[str, dict]
+) -> EffectiveAIModelConfiguration:
+    """Merge *incoming_partial* onto *existing* and return a new EffectiveAIModelConfiguration.
 
     *incoming_partial* is the body of the PUT request (already `model_dump()`ed or
     extracted via Pydantic `model_dump`).
@@ -113,14 +113,14 @@ def merge_user_configurations(
     if "timezone" in incoming_partial:
         merged["timezone"] = incoming_partial["timezone"]
 
-    # Onboarding gate flags — overwrite only when supplied (set once on submit/skip).
+    # Onboarding gate flags: overwrite only when supplied.
     if "onboarding_completed_at" in incoming_partial:
         merged["onboarding_completed_at"] = incoming_partial["onboarding_completed_at"]
 
     if "onboarding_skipped" in incoming_partial:
         merged["onboarding_skipped"] = incoming_partial["onboarding_skipped"]
 
-    return UserConfiguration.model_validate(merged)
+    return EffectiveAIModelConfiguration.model_validate(merged)
 
 
 def merge_workflow_configuration_secrets(

api/services/configuration/registry.py

@@ -911,7 +911,7 @@ class DograhTTSService(BaseTTSConfiguration):
     speed: float = Field(default=1.0, ge=0.5, le=2.0, description="Speed of the voice.")
 
 
-CARTESIA_TTS_MODELS = ["sonic-3"]
+CARTESIA_TTS_MODELS = ["sonic-3.5", "sonic-3"]
 
 
 @register_tts
@@ -919,7 +919,7 @@ class CartesiaTTSConfiguration(BaseTTSConfiguration):
     model_config = CARTESIA_PROVIDER_MODEL_CONFIG
     provider: Literal[ServiceProviders.CARTESIA] = ServiceProviders.CARTESIA
     model: str = Field(
-        default="sonic-3",
+        default="sonic-3.5",
         description="Cartesia TTS model.",
         json_schema_extra={"examples": CARTESIA_TTS_MODELS},
     )
@@ -1472,11 +1472,26 @@ class AzureOpenAIEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
     )
 
 
+DOGRAH_EMBEDDING_MODELS = ["default"]
+
+
+@register_embeddings
+class DograhEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
+    model_config = DOGRAH_PROVIDER_MODEL_CONFIG
+    provider: Literal[ServiceProviders.DOGRAH] = ServiceProviders.DOGRAH
+    model: str = Field(
+        default="default",
+        description="Dograh-managed embedding model.",
+        json_schema_extra={"examples": DOGRAH_EMBEDDING_MODELS},
+    )
+
+
 EmbeddingsConfig = Annotated[
     Union[
         OpenAIEmbeddingsConfiguration,
         OpenRouterEmbeddingsConfiguration,
         AzureOpenAIEmbeddingsConfiguration,
+        DograhEmbeddingsConfiguration,
     ],
     Field(discriminator="provider"),
 ]

api/services/configuration/resolve.py

@@ -4,13 +4,13 @@ from __future__ import annotations
 
 import copy
 
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.configuration.registry import (
     REGISTRY,
     ServiceType,
 )
 
-# Maps override key → (UserConfiguration field, ServiceType for registry lookup)
+# Maps override key → (EffectiveAIModelConfiguration field, ServiceType for registry lookup)
 _SECTION_MAP: dict[str, ServiceType] = {
     "llm": ServiceType.LLM,
     "tts": ServiceType.TTS,
@@ -36,7 +36,7 @@ _SECRET_FIELDS = ("api_key", "credentials", "aws_access_key", "aws_secret_key")
 
 def enrich_overrides_with_api_keys(
     model_overrides: dict,
-    user_config: UserConfiguration,
+    user_config: EffectiveAIModelConfiguration,
 ) -> dict:
     """Copy API keys from the global config into model_overrides where missing.
 
@@ -74,9 +74,9 @@ def enrich_overrides_with_api_keys(
 
 
 def resolve_effective_config(
-    user_config: UserConfiguration,
+    user_config: EffectiveAIModelConfiguration,
     model_overrides: dict | None,
-) -> UserConfiguration:
+) -> EffectiveAIModelConfiguration:
     """Deep-merge workflow model_overrides onto global user config.
 
     - If model_overrides is None or empty, returns a copy of user_config unchanged.

api/services/gen_ai/embedding/openai_service.py

@@ -38,6 +38,7 @@ class OpenAIEmbeddingService(BaseEmbeddingService):
         api_key: Optional[str] = None,
         model_id: str = DEFAULT_MODEL_ID,
         base_url: Optional[str] = None,
+        default_headers: Optional[Dict[str, str]] = None,
     ):
         """Initialize the OpenAI embedding service.
 
@@ -60,6 +61,8 @@ class OpenAIEmbeddingService(BaseEmbeddingService):
                     field_name="base_url",
                 )
                 client_kwargs["base_url"] = base_url
+            if default_headers:
+                client_kwargs["default_headers"] = default_headers
             self.client = AsyncOpenAI(**client_kwargs)
             logger.info(f"OpenAI embedding service initialized with model: {model_id}")
         else:

api/services/managed_model_services.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from typing import Any
+
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
+from api.services.configuration.registry import ServiceProviders
+
+MPS_CORRELATION_ID_CONTEXT_KEY = "mps_correlation_id"
+
+
+def uses_managed_model_services_v2(
+    ai_model_config: EffectiveAIModelConfiguration | None,
+) -> bool:
+    if (
+        ai_model_config is None
+        or getattr(ai_model_config, "managed_service_version", None) != 2
+    ):
+        return False
+
+    return any(
+        _is_dograh_service(getattr(ai_model_config, section_name, None))
+        for section_name in ("llm", "tts", "stt", "embeddings")
+    )
+
+
+def get_mps_correlation_id(initial_context: dict[str, Any] | None) -> str | None:
+    if not initial_context:
+        return None
+    correlation_id = initial_context.get(MPS_CORRELATION_ID_CONTEXT_KEY)
+    if correlation_id is None:
+        return None
+    return str(correlation_id)
+
+
+async def ensure_mps_correlation_id(
+    *,
+    ai_model_config: EffectiveAIModelConfiguration,
+    workflow_run_id: int,
+    initial_context: dict[str, Any] | None,
+) -> str | None:
+    existing = get_mps_correlation_id(initial_context)
+    if existing:
+        return existing
+
+    if not uses_managed_model_services_v2(ai_model_config):
+        return None
+
+    raise ValueError(
+        "Managed model services v2 requires workflow run authorization before "
+        f"the run starts. Missing correlation id for workflow_run_id={workflow_run_id}."
+    )
+
+
+def _is_dograh_service(service: Any) -> bool:
+    provider = getattr(service, "provider", None)
+    return (
+        provider == ServiceProviders.DOGRAH or provider == ServiceProviders.DOGRAH.value
+    )
+
+
+def get_dograh_service_api_key(
+    ai_model_config: EffectiveAIModelConfiguration,
+) -> str | None:
+    for section_name in ("llm", "tts", "stt", "embeddings"):
+        service = getattr(ai_model_config, section_name, None)
+        if not _is_dograh_service(service):
+            continue
+
+        if hasattr(service, "get_all_api_keys"):
+            keys = service.get_all_api_keys()
+            if keys:
+                return keys[0]
+
+        api_key = getattr(service, "api_key", None)
+        if isinstance(api_key, str) and api_key:
+            return api_key
+
+    return None

api/services/mps_billing.py

@@ -0,0 +1,23 @@
+from typing import Optional
+
+from api.constants import DEPLOYMENT_MODE
+from api.services.mps_service_key_client import mps_service_key_client
+
+
+async def ensure_hosted_mps_billing_account_v2(
+    organization_id: int,
+    *,
+    created_by: Optional[str] = None,
+) -> Optional[dict]:
+    """Ensure hosted orgs have an MPS billing v2 account.
+
+    OSS deployments use legacy per-key quota accounting and do not create MPS
+    billing accounts.
+    """
+    if DEPLOYMENT_MODE == "oss":
+        return None
+
+    return await mps_service_key_client.ensure_billing_account_v2(
+        organization_id=organization_id,
+        created_by=created_by,
+    )

api/services/mps_service_key_client.py

@@ -4,6 +4,7 @@ This client communicates with the Model Proxy Service (MPS) for service key mana
 Service keys are stored and managed entirely in MPS, not in the local database.
 """
 
+import asyncio
 from typing import List, Optional
 
 import httpx
@@ -353,6 +354,278 @@ class MPSServiceKeyClient:
                     response=response,
                 )
 
+    async def create_credit_purchase_url(
+        self,
+        organization_id: int,
+        created_by: Optional[str] = None,
+        return_url: Optional[str] = None,
+        billing_details: Optional[dict] = None,
+    ) -> dict:
+        """Create a short-lived MPS checkout URL for adding organization credits."""
+        payload = {
+            "created_by": created_by,
+            "return_url": return_url,
+            "billing_details": billing_details or {},
+        }
+
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.post(
+                f"{self.base_url}/api/v1/billing/accounts/{organization_id}/checkout-sessions",
+                json=payload,
+                headers=self._get_headers(
+                    organization_id=organization_id,
+                    created_by=created_by,
+                ),
+            )
+
+            if response.status_code == 200:
+                return response.json()
+
+            logger.error(
+                "Failed to create MPS credit purchase URL: "
+                f"{response.status_code} - {response.text}"
+            )
+            raise httpx.HTTPStatusError(
+                f"Failed to create MPS credit purchase URL: {response.text}",
+                request=response.request,
+                response=response,
+            )
+
+    async def get_credit_ledger(
+        self,
+        organization_id: int,
+        page: int = 1,
+        limit: int = 50,
+        created_by: Optional[str] = None,
+    ) -> dict:
+        """Get the MPS v2 billing account balance and recent credit ledger."""
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.get(
+                f"{self.base_url}/api/v1/billing/accounts/{organization_id}/ledger",
+                params={"page": page, "limit": limit},
+                headers=self._get_headers(
+                    organization_id=organization_id,
+                    created_by=created_by,
+                ),
+            )
+
+            if response.status_code == 200:
+                return response.json()
+
+            logger.error(
+                "Failed to get MPS credit ledger: "
+                f"{response.status_code} - {response.text}"
+            )
+            raise httpx.HTTPStatusError(
+                f"Failed to get MPS credit ledger: {response.text}",
+                request=response.request,
+                response=response,
+            )
+
+    async def get_billing_account_status(
+        self,
+        organization_id: int,
+        created_by: Optional[str] = None,
+    ) -> Optional[dict]:
+        """Get an existing MPS v2 billing account without creating one."""
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.get(
+                f"{self.base_url}/api/v1/billing/accounts/{organization_id}/status",
+                headers=self._get_headers(
+                    organization_id=organization_id,
+                    created_by=created_by,
+                ),
+            )
+
+            if response.status_code == 200:
+                return response.json()
+
+            logger.error(
+                "Failed to get MPS billing account status: "
+                f"{response.status_code} - {response.text}"
+            )
+            raise httpx.HTTPStatusError(
+                f"Failed to get MPS billing account status: {response.text}",
+                request=response.request,
+                response=response,
+            )
+
+    async def ensure_billing_account_v2(
+        self,
+        organization_id: int,
+        created_by: Optional[str] = None,
+    ) -> dict:
+        """Create or return the MPS v2 billing account for an organization."""
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.get(
+                f"{self.base_url}/api/v1/billing/accounts/{organization_id}/balance",
+                headers=self._get_headers(
+                    organization_id=organization_id,
+                    created_by=created_by,
+                ),
+            )
+
+            if response.status_code == 200:
+                return response.json()
+
+            logger.error(
+                "Failed to ensure MPS billing account v2: "
+                f"{response.status_code} - {response.text}"
+            )
+            raise httpx.HTTPStatusError(
+                f"Failed to ensure MPS billing account v2: {response.text}",
+                request=response.request,
+                response=response,
+            )
+
+    async def authorize_workflow_run_start(
+        self,
+        *,
+        organization_id: int,
+        workflow_run_id: int | None = None,
+        service_key: Optional[str] = None,
+        require_correlation_id: bool = False,
+        minimum_credits: float | None = None,
+        metadata: Optional[dict] = None,
+        created_by: Optional[str] = None,
+    ) -> dict:
+        """Authorize a hosted workflow run and optionally mint its MPS correlation."""
+        payload = {
+            "workflow_run_id": workflow_run_id,
+            "service_key": service_key,
+            "require_correlation_id": require_correlation_id,
+            "metadata": metadata or {},
+        }
+        if minimum_credits is not None:
+            payload["minimum_credits"] = minimum_credits
+
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.post(
+                f"{self.base_url}/api/v1/billing/accounts/{organization_id}/run-authorization",
+                json=payload,
+                headers=self._get_headers(
+                    organization_id=organization_id,
+                    created_by=created_by,
+                ),
+            )
+
+            if response.status_code == 200:
+                return response.json()
+
+            logger.error(
+                "Failed to authorize MPS workflow run start: "
+                f"{response.status_code} - {response.text}"
+            )
+            raise httpx.HTTPStatusError(
+                f"Failed to authorize MPS workflow run start: {response.text}",
+                request=response.request,
+                response=response,
+            )
+
+    async def create_correlation_id(
+        self,
+        *,
+        service_key: str,
+        workflow_run_id: int | None = None,
+    ) -> dict:
+        """Mint a server-generated correlation ID for managed model services."""
+        payload: dict[str, int] = {}
+        if workflow_run_id is not None:
+            payload["workflow_run_id"] = workflow_run_id
+
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.post(
+                f"{self.base_url}/api/v1/service-keys/correlation-id/self",
+                json=payload,
+                headers={
+                    "Authorization": f"Bearer {service_key}",
+                    "Content-Type": "application/json",
+                },
+            )
+
+            if response.status_code == 200:
+                return response.json()
+
+            logger.error(
+                "Failed to create correlation ID: "
+                f"{response.status_code} - {response.text}"
+            )
+            raise httpx.HTTPStatusError(
+                f"Failed to create correlation ID: {response.text}",
+                request=response.request,
+                response=response,
+            )
+
+    async def report_platform_usage(
+        self,
+        *,
+        organization_id: int,
+        correlation_id: Optional[str] = None,
+        duration_seconds: Optional[float] = None,
+        workflow_run_id: int | None = None,
+        metadata: Optional[dict] = None,
+        max_attempts: int = 3,
+    ) -> dict:
+        """Report hosted Dograh platform usage for a completed workflow run."""
+        if DEPLOYMENT_MODE == "oss":
+            raise ValueError("OSS deployments must not report platform usage to MPS")
+        if not correlation_id and duration_seconds is None:
+            raise ValueError(
+                "Platform usage reports require correlation_id or duration_seconds"
+            )
+
+        payload: dict = {
+            "metadata": metadata or {},
+        }
+        if correlation_id:
+            payload["correlation_id"] = correlation_id
+        if duration_seconds is not None:
+            payload["duration_seconds"] = duration_seconds
+        if workflow_run_id is not None:
+            payload["workflow_run_id"] = workflow_run_id
+
+        max_attempts = max(1, max_attempts)
+        last_response: httpx.Response | None = None
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            for attempt in range(1, max_attempts + 1):
+                response = await client.post(
+                    (
+                        f"{self.base_url}/api/v1/billing/accounts/"
+                        f"{organization_id}/platform-usage"
+                    ),
+                    json=payload,
+                    headers=self._get_headers(organization_id=organization_id),
+                )
+                last_response = response
+
+                if response.status_code == 200:
+                    return response.json()
+
+                should_retry = (
+                    response.status_code == 409
+                    and "usage_not_ready" in response.text
+                    and attempt < max_attempts
+                )
+                if should_retry:
+                    await asyncio.sleep(attempt)
+                    continue
+
+                logger.error(
+                    "Failed to report platform usage: "
+                    f"{response.status_code} - {response.text}"
+                )
+                raise httpx.HTTPStatusError(
+                    f"Failed to report platform usage: {response.text}",
+                    request=response.request,
+                    response=response,
+                )
+
+        raise httpx.HTTPStatusError(
+            "Failed to report platform usage",
+            request=last_response.request,
+            response=last_response,
+        )
+
     async def transcribe_audio(
         self,
         audio_data: bytes,

api/services/organization_context.py

@@ -0,0 +1,50 @@
+from typing import Literal, Optional
+
+from pydantic import BaseModel
+
+from api.db import db_client
+from api.db.models import UserModel
+from api.services.configuration.ai_model_configuration import (
+    get_resolved_ai_model_configuration,
+)
+
+
+class OrganizationModelServicesContext(BaseModel):
+    config_source: Literal["organization_v2", "legacy_user_v1", "empty"]
+    has_model_configuration_v2: bool
+    managed_service_version: Optional[int] = None
+    uses_managed_service_v2: bool
+
+
+class OrganizationContextResponse(BaseModel):
+    organization_id: Optional[int] = None
+    organization_provider_id: Optional[str] = None
+    model_services: OrganizationModelServicesContext
+
+
+async def get_organization_context(user: UserModel) -> OrganizationContextResponse:
+    organization_id = user.selected_organization_id
+    organization = (
+        await db_client.get_organization_by_id(organization_id)
+        if organization_id
+        else None
+    )
+
+    resolved = await get_resolved_ai_model_configuration(
+        user_id=user.id,
+        organization_id=organization_id,
+    )
+    managed_service_version = resolved.effective.managed_service_version
+
+    return OrganizationContextResponse(
+        organization_id=organization_id,
+        organization_provider_id=organization.provider_id if organization else None,
+        model_services=OrganizationModelServicesContext(
+            config_source=resolved.source,
+            has_model_configuration_v2=resolved.source == "organization_v2",
+            managed_service_version=managed_service_version,
+            uses_managed_service_v2=(
+                resolved.source == "organization_v2" and managed_service_version == 2
+            ),
+        ),
+    )

api/services/organization_preferences.py

@@ -0,0 +1,62 @@
+from inspect import isawaitable
+
+from loguru import logger
+from pydantic import ValidationError
+
+from api.db import db_client
+from api.enums import OrganizationConfigurationKey
+from api.schemas.organization_preferences import OrganizationPreferences
+
+
+async def get_organization_preferences(
+    organization_id: int | None,
+    db=None,
+) -> OrganizationPreferences:
+    if organization_id is None:
+        return OrganizationPreferences()
+
+    db = db or db_client
+    row = await _get_configuration(
+        db,
+        organization_id,
+        OrganizationConfigurationKey.ORGANIZATION_PREFERENCES.value,
+    )
+    if row is None:
+        row = await _get_configuration(
+            db,
+            organization_id,
+            OrganizationConfigurationKey.MODEL_CONFIGURATION_PREFERENCES.value,
+        )
+    return _parse_preferences(row.value if row is not None else None, organization_id)
+
+
+async def upsert_organization_preferences(
+    organization_id: int,
+    preferences: OrganizationPreferences,
+) -> OrganizationPreferences:
+    await db_client.upsert_configuration(
+        organization_id,
+        OrganizationConfigurationKey.ORGANIZATION_PREFERENCES.value,
+        preferences.model_dump(mode="json", exclude_none=True),
+    )
+    return preferences
+
+
+async def _get_configuration(db, organization_id: int, key: str):
+    row = db.get_configuration(organization_id, key)
+    if isawaitable(row):
+        row = await row
+    return row
+
+
+def _parse_preferences(value, organization_id: int) -> OrganizationPreferences:
+    if not value or not isinstance(value, dict):
+        return OrganizationPreferences()
+    try:
+        return OrganizationPreferences.model_validate(value)
+    except ValidationError as exc:
+        logger.warning(
+            "Invalid organization preferences for organization "
+            f"{organization_id}: {exc}. Returning defaults."
+        )
+        return OrganizationPreferences()

api/services/pipecat/pre_call_fetch.py

@@ -15,6 +15,29 @@ from api.utils.credential_auth import build_auth_header
 PRE_CALL_FETCH_TIMEOUT_SECONDS = 10
 
 
+def _extract_initial_context(response_data: Dict[str, Any]) -> Dict[str, Any]:
+    """Pull the context variables out of a pre-call fetch response.
+
+    The canonical key is ``initial_context``. The legacy ``dynamic_variables``
+    key is still accepted for backward compatibility, so existing endpoints
+    keep working; ``initial_context`` takes precedence when both are present.
+
+    Either key may appear at the top level or nested under ``call_inbound``:
+        {"call_inbound": {"initial_context": {...}}} | {"initial_context": {...}}
+        {"call_inbound": {"dynamic_variables": {...}}} | {"dynamic_variables": {...}}
+    """
+    container = response_data.get("call_inbound")
+    if not isinstance(container, dict):
+        container = response_data
+
+    for key in ("initial_context", "dynamic_variables"):
+        value = container.get(key)
+        if isinstance(value, dict):
+            return value
+
+    return {}
+
+
 async def execute_pre_call_fetch(
     *,
     url: str,
@@ -77,24 +100,16 @@ async def execute_pre_call_fetch(
                     )
                     return {}
 
-                # Extract dynamic_variables from Retell-compatible response
-                # Supports: {call_inbound: {dynamic_variables: {...}}}
-                #       or: {dynamic_variables: {...}}
-                dynamic_vars = {}
-                call_inbound = response_data.get("call_inbound")
-                if isinstance(call_inbound, dict):
-                    dynamic_vars = call_inbound.get("dynamic_variables", {})
-                elif "dynamic_variables" in response_data:
-                    dynamic_vars = response_data["dynamic_variables"]
-
-                if not isinstance(dynamic_vars, dict):
-                    dynamic_vars = {}
+                # Extract the variables to merge into initial_context. Prefers
+                # the canonical `initial_context` key, falling back to the
+                # legacy `dynamic_variables` key for backward compatibility.
+                initial_context_vars = _extract_initial_context(response_data)
 
                 logger.info(
                     f"Pre-call fetch: success ({response.status_code}), "
-                    f"dynamic_variables keys: {list(dynamic_vars.keys())}"
+                    f"initial_context keys: {list(initial_context_vars.keys())}"
                 )
-                return dynamic_vars
+                return initial_context_vars
             else:
                 logger.warning(
                     f"Pre-call fetch: HTTP {response.status_code} - "

api/services/pipecat/run_pipeline.py

@@ -162,15 +162,13 @@ async def run_pipeline_telephony(
         workflow_id: Workflow being executed.
         workflow_run_id: Workflow run row.
         user_id: Owner of the workflow.
-        call_id: Provider call identifier (stored in cost_info for billing).
+        call_id: Provider call identifier.
         transport_kwargs: Provider-specific kwargs forwarded to the transport
             factory (e.g. stream_sid + call_sid for Twilio).
     """
     logger.debug(f"Running {provider_name} pipeline for workflow_run {workflow_run_id}")
     set_current_run_id(workflow_run_id)
 
-    await db_client.update_workflow_run(workflow_run_id, cost_info={"call_id": call_id})
-
     workflow = await db_client.get_workflow(workflow_id, user_id)
     if workflow:
         set_current_org_id(workflow.organization_id)
@@ -195,14 +193,17 @@ async def run_pipeline_telephony(
     # Resolve effective user config here so the transport can tune its
     # bot-stopped-speaking fallback based on is_realtime; pass the resolved
     # values into _run_pipeline so it doesn't fetch them again.
-    from api.services.configuration.resolve import resolve_effective_config
+    from api.services.configuration.ai_model_configuration import (
+        get_effective_ai_model_configuration_for_workflow,
+    )
 
-    user_config = await db_client.get_user_configurations(user_id)
     run_configs = (
         (workflow_run.definition.workflow_configurations or {}) if workflow_run else {}
     )
-    user_config = resolve_effective_config(
-        user_config, run_configs.get("model_overrides")
+    user_config = await get_effective_ai_model_configuration_for_workflow(
+        user_id=user_id,
+        organization_id=workflow.organization_id if workflow else None,
+        workflow_configurations=run_configs,
     )
     is_realtime = bool(user_config.is_realtime and user_config.realtime is not None)
 
@@ -272,15 +273,18 @@ async def run_pipeline_smallwebrtc(
     # Resolve workflow_run + effective user_config here so the transport can
     # tune its bot-stopped-speaking fallback based on is_realtime. _run_pipeline
     # reuses these via kwargs so we don't fetch twice.
-    from api.services.configuration.resolve import resolve_effective_config
+    from api.services.configuration.ai_model_configuration import (
+        get_effective_ai_model_configuration_for_workflow,
+    )
 
     workflow_run = await db_client.get_workflow_run(workflow_run_id, user_id)
-    user_config = await db_client.get_user_configurations(user_id)
     run_configs = (
         (workflow_run.definition.workflow_configurations or {}) if workflow_run else {}
     )
-    user_config = resolve_effective_config(
-        user_config, run_configs.get("model_overrides")
+    user_config = await get_effective_ai_model_configuration_for_workflow(
+        user_id=user_id,
+        organization_id=workflow.organization_id if workflow else None,
+        workflow_configurations=run_configs,
     )
     is_realtime = bool(user_config.is_realtime and user_config.realtime is not None)
 
@@ -334,7 +338,7 @@ async def _run_pipeline(
     if workflow_run.is_completed:
         raise HTTPException(status_code=400, detail="Workflow run already completed")
 
-    merged_call_context_vars = workflow_run.initial_context
+    merged_call_context_vars = dict(workflow_run.initial_context or {})
     # If there is some extra call_context_vars, fold them in. Persistence
     # happens once below, after runtime_configuration is also resolved.
     if call_context_vars:
@@ -380,15 +384,31 @@ async def _run_pipeline(
     # Resolve model overrides from the version onto global user config (skip
     # when the caller already resolved it).
     if resolved_user_config is None:
-        from api.services.configuration.resolve import resolve_effective_config
+        from api.services.configuration.ai_model_configuration import (
+            get_effective_ai_model_configuration_for_workflow,
+        )
 
-        user_config = await db_client.get_user_configurations(user_id)
-        user_config = resolve_effective_config(
-            user_config, run_configs.get("model_overrides")
+        user_config = await get_effective_ai_model_configuration_for_workflow(
+            user_id=user_id,
+            organization_id=workflow.organization_id,
+            workflow_configurations=run_configs,
         )
     else:
         user_config = resolved_user_config
 
+    from api.services.managed_model_services import (
+        MPS_CORRELATION_ID_CONTEXT_KEY,
+        ensure_mps_correlation_id,
+    )
+
+    mps_correlation_id = await ensure_mps_correlation_id(
+        ai_model_config=user_config,
+        workflow_run_id=workflow_run_id,
+        initial_context=merged_call_context_vars,
+    )
+    if mps_correlation_id:
+        merged_call_context_vars[MPS_CORRELATION_ID_CONTEXT_KEY] = mps_correlation_id
+
     # Detect realtime mode (speech-to-speech services like OpenAI Realtime, Gemini Live)
     is_realtime = user_config.is_realtime and user_config.realtime is not None
 
@@ -400,11 +420,23 @@ async def _run_pipeline(
         # Realtime services don't implement run_inference, so create a
         # separate text LLM for variable extraction and other out-of-band
         # inference calls.
-        inference_llm = create_llm_service(user_config)
+        inference_llm = create_llm_service(
+            user_config,
+            correlation_id=mps_correlation_id,
+        )
     else:
-        stt = create_stt_service(user_config, audio_config, keyterms=keyterms)
-        tts = create_tts_service(user_config, audio_config)
-        llm = create_llm_service(user_config)
+        stt = create_stt_service(
+            user_config,
+            audio_config,
+            keyterms=keyterms,
+            correlation_id=mps_correlation_id,
+        )
+        tts = create_tts_service(
+            user_config,
+            audio_config,
+            correlation_id=mps_correlation_id,
+        )
+        llm = create_llm_service(user_config, correlation_id=mps_correlation_id)
         inference_llm = None
 
     # Stamp the providers/models actually resolved for this run onto
@@ -508,10 +540,17 @@ async def _run_pipeline(
     embeddings_endpoint = None
     embeddings_api_version = None
     if user_config and user_config.embeddings:
+        from api.services.configuration.ai_model_configuration import (
+            apply_managed_embeddings_base_url,
+        )
+
         embeddings_api_key = user_config.embeddings.api_key
         embeddings_model = user_config.embeddings.model
         embeddings_provider = getattr(user_config.embeddings, "provider", None)
-        embeddings_base_url = getattr(user_config.embeddings, "base_url", None)
+        embeddings_base_url = apply_managed_embeddings_base_url(
+            provider=embeddings_provider,
+            base_url=getattr(user_config.embeddings, "base_url", None),
+        )
         embeddings_endpoint = getattr(user_config.embeddings, "endpoint", None)
         embeddings_api_version = getattr(user_config.embeddings, "api_version", None)
 
@@ -679,7 +718,10 @@ async def _run_pipeline(
         # Create a separate LLM instance for the voicemail sub-pipeline
         # (can't share with main pipeline as it would mess up frame linking)
         if voicemail_config.get("use_workflow_llm", True):
-            voicemail_llm = create_llm_service(user_config)
+            voicemail_llm = create_llm_service(
+                user_config,
+                correlation_id=mps_correlation_id,
+            )
         else:
             voicemail_llm = create_llm_service_from_provider(
                 provider=voicemail_config.get("provider", "openai"),

api/services/pipecat/service_factory.py

@@ -78,7 +78,10 @@ def _validate_runtime_service_url(url: str, field_name: str) -> None:
 
 
 def create_stt_service(
-    user_config, audio_config: "AudioConfig", keyterms: list[str] | None = None
+    user_config,
+    audio_config: "AudioConfig",
+    keyterms: list[str] | None = None,
+    correlation_id: str | None = None,
 ):
     """Create and return appropriate STT service based on user configuration
 
@@ -160,6 +163,7 @@ def create_stt_service(
         return DograhSTTService(
             base_url=base_url,
             api_key=user_config.stt.api_key,
+            correlation_id=correlation_id,
             settings=DograhSTTSettings(
                 model=user_config.stt.model,
                 language=language,
@@ -286,7 +290,9 @@ def create_stt_service(
         )
 
 
-def create_tts_service(user_config, audio_config: "AudioConfig"):
+def create_tts_service(
+    user_config, audio_config: "AudioConfig", correlation_id: str | None = None
+):
     """Create and return appropriate TTS service based on user configuration
 
     Args:
@@ -404,6 +410,7 @@ def create_tts_service(user_config, audio_config: "AudioConfig"):
         return DograhTTSService(
             base_url=base_url,
             api_key=user_config.tts.api_key,
+            correlation_id=correlation_id,
             settings=DograhTTSSettings(
                 model=user_config.tts.model,
                 voice=user_config.tts.voice,
@@ -564,6 +571,7 @@ def create_llm_service_from_provider(
     model: str,
     api_key: str | None,
     *,
+    correlation_id: str | None = None,
     base_url: str | None = None,
     endpoint: str | None = None,
     aws_access_key: str | None = None,
@@ -637,6 +645,7 @@ def create_llm_service_from_provider(
         return DograhLLMService(
             base_url=f"{MPS_API_URL}/api/v1/llm",
             api_key=api_key,
+            correlation_id=correlation_id,
             settings=OpenAILLMSettings(model=model),
         )
     elif provider == ServiceProviders.AWS_BEDROCK.value:
@@ -851,7 +860,7 @@ def create_realtime_llm_service(user_config, audio_config: "AudioConfig"):
         )
 
 
-def create_llm_service(user_config):
+def create_llm_service(user_config, correlation_id: str | None = None):
     """Create and return appropriate LLM service based on user configuration."""
     provider = user_config.llm.provider
     model = user_config.llm.model
@@ -880,4 +889,10 @@ def create_llm_service(user_config):
     elif provider == ServiceProviders.SARVAM.value:
         kwargs["temperature"] = user_config.llm.temperature
 
-    return create_llm_service_from_provider(provider, model, api_key, **kwargs)
+    return create_llm_service_from_provider(
+        provider,
+        model,
+        api_key,
+        correlation_id=correlation_id,
+        **kwargs,
+    )

api/services/pricing/README.md

@@ -1,76 +0,0 @@
-# Pricing Module
-
-This module contains pricing models and registries for different AI services used in workflow cost calculations.
-
-## Structure
-
-```
-pricing/
-├── __init__.py           # Main module exports
-├── models.py            # Base pricing model classes
-├── llm.py              # LLM pricing configurations
-├── tts.py              # TTS pricing configurations  
-├── stt.py              # STT pricing configurations
-├── registry.py         # Combined pricing registry
-└── README.md           # This file
-```
-
-## Pricing Models
-
-### TokenPricingModel
-Used for LLM services that charge based on tokens:
-- `prompt_token_price`: Cost per prompt token
-- `completion_token_price`: Cost per completion token
-- `cache_read_discount`: Discount for cache read tokens (default 50%)
-- `cache_creation_multiplier`: Premium for cache creation tokens (default 25%)
-
-### CharacterPricingModel
-Used for TTS services that charge based on character count:
-- `character_price`: Cost per character
-
-### TimePricingModel
-Used for STT services that charge based on time:
-- `second_price`: Cost per second
-
-## Adding New Pricing
-
-### Adding a New LLM Model
-Edit `llm.py` and add the model to the appropriate provider:
-
-```python
-ServiceProviders.OPENAI: {
-    "new-model": TokenPricingModel(
-        prompt_token_price=Decimal("2.00") / 1000000,
-        completion_token_price=Decimal("8.00") / 1000000,
-    ),
-    # ... existing models
-}
-```
-
-### Adding a New Provider
-1. Add pricing configurations to the appropriate service file (llm.py, tts.py, stt.py)
-2. The registry will automatically include them
-
-### Adding a New Service Type
-1. Create a new pricing file (e.g., `image.py`)
-2. Define the pricing models
-3. Import and add to `registry.py`
-
-## Usage
-
-The pricing registry is automatically imported and used by the cost calculator:
-
-```python
-from api.services.pricing import PRICING_REGISTRY
-from api.services.workflow.cost_calculator import cost_calculator
-
-# The cost calculator uses the pricing registry automatically
-result = cost_calculator.calculate_total_cost(usage_info)
-```
-
-## Maintenance
-
-- Update pricing when providers change their rates
-- All prices should use `Decimal` for precision
-- Include comments with current pricing from provider documentation
-- Test changes with existing test suite 

api/services/pricing/__init__.py

@@ -1,9 +0,0 @@
-"""
-Pricing module for workflow cost calculation.
-
-This module contains pricing models and registries for different AI services.
-"""
-
-from .registry import PRICING_REGISTRY
-
-__all__ = ["PRICING_REGISTRY"]

api/services/pricing/cost_calculator.py

@@ -1,228 +0,0 @@
-"""
-Cost Calculator for Workflow Runs
-
-This module provides a comprehensive cost calculation system for workflow runs based on usage metrics
-from different AI service providers (OpenAI, Groq, Deepgram, etc.).
-
-Features:
-- Token-based pricing for LLM services with cache optimization support
-- Character-based pricing for TTS services
-- Time-based pricing for STT services
-- Configurable pricing models that can be updated
-- Support for multiple providers and models
-- Automatic provider inference from model names
-- JSON serialization support for database storage
-
-Usage:
-    from api.tasks.cost_calculator import cost_calculator
-
-    usage_info = {
-        "llm": {
-            "processor_name|||gpt-4o": {
-                "prompt_tokens": 1000,
-                "completion_tokens": 500,
-                "total_tokens": 1500,
-                "cache_read_input_tokens": 0,
-                "cache_creation_input_tokens": 0
-            }
-        },
-        "tts": {
-            "processor_name|||aura-2-helena-en": 2000  # character count
-        }
-    }
-
-    cost_breakdown = cost_calculator.calculate_total_cost(usage_info)
-    print(f"Total cost: ${cost_breakdown['total']:.6f}")
-"""
-
-from decimal import Decimal
-from typing import Any, Dict, Optional, Tuple
-
-from api.services.configuration.registry import ServiceProviders
-from api.services.pricing import PRICING_REGISTRY
-from api.services.pricing.models import (
-    PricingModel,
-)
-
-
-class CostCalculator:
-    """Main cost calculator class"""
-
-    def __init__(self, pricing_registry: Dict = None):
-        self.pricing_registry = pricing_registry or PRICING_REGISTRY
-
-    def get_pricing_model(
-        self, service_type: str, provider: str, model: str
-    ) -> Optional[PricingModel]:
-        """Get pricing model for a specific service, provider, and model"""
-        try:
-            service_pricing = self.pricing_registry.get(service_type, {})
-
-            # Try to get pricing for the specific provider
-            provider_pricing = service_pricing.get(provider, {})
-            pricing_model = provider_pricing.get(model) or provider_pricing.get(
-                "default"
-            )
-
-            if pricing_model:
-                return pricing_model
-
-            # If not found, try the "default" provider for this service type
-            default_provider_pricing = service_pricing.get("default", {})
-            return default_provider_pricing.get(model) or default_provider_pricing.get(
-                "default"
-            )
-
-        except (KeyError, AttributeError):
-            return None
-
-    def calculate_llm_cost(
-        self, provider: str, model: str, usage: Dict[str, int]
-    ) -> Decimal:
-        """Calculate cost for LLM usage"""
-        pricing_model = self.get_pricing_model("llm", provider, model)
-        if not pricing_model:
-            return Decimal("0")
-        return pricing_model.calculate_cost(usage)
-
-    def calculate_tts_cost(
-        self, provider: str, model: str, character_count: int
-    ) -> Decimal:
-        """Calculate cost for TTS usage"""
-        pricing_model = self.get_pricing_model("tts", provider, model)
-        if not pricing_model:
-            return Decimal("0")
-        return pricing_model.calculate_cost(character_count)
-
-    def calculate_stt_cost(self, provider: str, model: str, seconds: float) -> Decimal:
-        """Calculate cost for STT usage"""
-        pricing_model = self.get_pricing_model("stt", provider, model)
-        if not pricing_model:
-            return Decimal("0")
-        return pricing_model.calculate_cost(seconds)
-
-    def calculate_total_cost(self, usage_info: Dict) -> Dict[str, Any]:
-        llm_cost_total = Decimal("0")
-        tts_cost_total = Decimal("0")
-        stt_cost_total = Decimal("0")
-
-        # Calculate LLM costs
-        llm_usage = usage_info.get("llm", {})
-        for key, usage in llm_usage.items():
-            processor, model = self._parse_key(key)
-            # Try to determine provider from processor name or model
-            provider = self._infer_provider_from_model(model, "llm")
-            cost = self.calculate_llm_cost(provider, model, usage)
-            llm_cost_total += cost
-
-        # Calculate TTS costs
-        tts_usage = usage_info.get("tts", {})
-        for key, character_count in tts_usage.items():
-            processor, model = self._parse_key(key)
-            # Handle the case where model is "None" - infer from processor
-            if model.lower() in ["none", "null", ""]:
-                provider = self._infer_provider_from_processor(processor, "tts")
-                model = "default"  # Use default model for the provider
-            else:
-                provider = self._infer_provider_from_model(model, "tts")
-            cost = self.calculate_tts_cost(provider, model, character_count)
-            tts_cost_total += cost
-
-        # Calculate STT costs from explicit stt usage
-        stt_usage = usage_info.get("stt", {})
-        for key, seconds in stt_usage.items():
-            processor, model = self._parse_key(key)
-            provider = self._infer_provider_from_model(model, "stt")
-            cost = self.calculate_stt_cost(provider, model, seconds)
-            stt_cost_total += cost
-
-        total_cost = llm_cost_total + tts_cost_total + stt_cost_total
-
-        return {
-            "llm_cost": float(llm_cost_total),
-            "tts_cost": float(tts_cost_total),
-            "stt_cost": float(stt_cost_total),
-            "total": float(total_cost),
-        }
-
-    def _parse_key(self, key) -> Tuple[str, str]:
-        """Parse key which is in format 'processor|||model'"""
-        if isinstance(key, str) and "|||" in key:
-            parts = key.split("|||", 1)
-            return parts[0], parts[1]
-        else:
-            # Fallback for backwards compatibility or malformed keys
-            return str(key), "unknown"
-
-    def _infer_provider_from_model(self, model: str, service_type: str) -> str:
-        """Infer provider from model name"""
-        if not model:
-            return "unknown"
-
-        model_lower = model.lower()
-
-        # OpenAI models
-        if any(keyword in model_lower for keyword in ["gpt", "whisper", "openai"]):
-            return ServiceProviders.OPENAI
-
-        # Groq models
-        if any(keyword in model_lower for keyword in ["groq"]):
-            return ServiceProviders.GROQ
-
-        # Elevenlabs models
-        if any(keyword in model_lower for keyword in ["eleven"]):
-            return ServiceProviders.ELEVENLABS
-
-        # Deepgram models
-        if any(
-            keyword in model_lower
-            for keyword in ["deepgram", "nova", "phonecall", "general"]
-        ):
-            return ServiceProviders.DEEPGRAM
-
-        # Default to first available provider for the service type
-        service_providers = self.pricing_registry.get(service_type, {})
-        if service_providers:
-            return list(service_providers.keys())[0]
-
-        return "unknown"
-
-    def _infer_provider_from_processor(self, processor: str, service_type: str) -> str:
-        """Infer provider from processor name"""
-        if not processor:
-            return "unknown"
-
-        processor_lower = processor.lower()
-
-        # OpenAI processors
-        if any(keyword in processor_lower for keyword in ["openai", "gpt"]):
-            return ServiceProviders.OPENAI
-
-        # Groq processors
-        if any(keyword in processor_lower for keyword in ["groq"]):
-            return ServiceProviders.GROQ
-
-        # Deepgram processors
-        if any(keyword in processor_lower for keyword in ["deepgram"]):
-            return ServiceProviders.DEEPGRAM
-
-        # Default to first available provider for the service type
-        service_providers = self.pricing_registry.get(service_type, {})
-        if service_providers:
-            return list(service_providers.keys())[0]
-
-        return "unknown"
-
-    def update_pricing(
-        self, service_type: str, provider: str, model: str, pricing_model: PricingModel
-    ):
-        """Update pricing for a specific service/provider/model combination"""
-        if service_type not in self.pricing_registry:
-            self.pricing_registry[service_type] = {}
-        if provider not in self.pricing_registry[service_type]:
-            self.pricing_registry[service_type][provider] = {}
-        self.pricing_registry[service_type][provider][model] = pricing_model
-
-
-# Global cost calculator instance
-cost_calculator = CostCalculator()

api/services/pricing/embeddings.py

@@ -1,44 +0,0 @@
-"""
-Embeddings pricing models for different providers.
-
-Prices are per token for embedding models.
-"""
-
-from decimal import Decimal
-from typing import Dict
-
-from api.services.configuration.registry import ServiceProviders
-
-from .models import PricingModel
-
-
-class EmbeddingPricingModel(PricingModel):
-    """Pricing model for token-based embedding services."""
-
-    def __init__(self, token_price: Decimal):
-        """Initialize with price per token.
-
-        Args:
-            token_price: Cost per token for embedding
-        """
-        self.token_price = token_price
-
-    def calculate_cost(self, token_count: int) -> Decimal:
-        """Calculate cost for embedding token usage."""
-        return Decimal(token_count) * self.token_price
-
-
-# Embeddings pricing registry
-EMBEDDINGS_PRICING: Dict[str, Dict[str, EmbeddingPricingModel]] = {
-    ServiceProviders.OPENAI: {
-        "text-embedding-3-small": EmbeddingPricingModel(
-            token_price=Decimal("0.02") / 1_000_000,  # $0.02 per 1M tokens
-        ),
-        "text-embedding-3-large": EmbeddingPricingModel(
-            token_price=Decimal("0.13") / 1_000_000,  # $0.13 per 1M tokens
-        ),
-        "text-embedding-ada-002": EmbeddingPricingModel(
-            token_price=Decimal("0.10") / 1_000_000,  # $0.10 per 1M tokens (legacy)
-        ),
-    },
-}

api/services/pricing/llm.py

@@ -1,143 +0,0 @@
-"""
-LLM pricing models for different providers.
-
-Prices are per 1000 tokens for most models, with some newer models priced per million tokens.
-"""
-
-from decimal import Decimal
-from typing import Dict
-
-from api.services.configuration.registry import ServiceProviders
-
-from .models import TokenPricingModel
-
-# LLM pricing registry
-LLM_PRICING: Dict[str, Dict[str, TokenPricingModel]] = {
-    ServiceProviders.OPENAI: {
-        "gpt-3.5-turbo": TokenPricingModel(
-            prompt_token_price=Decimal("0.0015") / 1000,  # $0.0015 per 1K tokens
-            completion_token_price=Decimal("0.002") / 1000,  # $0.002 per 1K tokens
-        ),
-        "gpt-4": TokenPricingModel(
-            prompt_token_price=Decimal("0.03") / 1000,  # $0.03 per 1K tokens
-            completion_token_price=Decimal("0.06") / 1000,  # $0.06 per 1K tokens
-        ),
-        "gpt-4.1": TokenPricingModel(
-            prompt_token_price=Decimal("2.00") / 1000000,  # $2.00 per 1M tokens
-            completion_token_price=Decimal("8.00") / 1000000,  # $8.00 per 1M tokens
-        ),
-        "gpt-4.1-mini": TokenPricingModel(
-            prompt_token_price=Decimal("0.40") / 1000000,  # $0.40 per 1M tokens
-            completion_token_price=Decimal("1.60") / 1000000,  # $1.60 per 1M tokens
-        ),
-        "gpt-4.1-nano": TokenPricingModel(
-            prompt_token_price=Decimal("0.10") / 1000000,  # $0.10 per 1M tokens
-            completion_token_price=Decimal("0.40") / 1000000,  # $0.40 per 1M tokens
-        ),
-        "gpt-4.5-preview": TokenPricingModel(
-            prompt_token_price=Decimal("75.00") / 1000000,  # $75.00 per 1M tokens
-            completion_token_price=Decimal("150.00") / 1000000,  # $150.00 per 1M tokens
-        ),
-        "gpt-4o": TokenPricingModel(
-            prompt_token_price=Decimal("2.50") / 1000000,  # $2.50 per 1M tokens - FIXED
-            completion_token_price=Decimal("10.00")
-            / 1000000,  # $10.00 per 1M tokens - FIXED
-        ),
-        "gpt-4o-audio-preview": TokenPricingModel(
-            prompt_token_price=Decimal("2.50") / 1000000,  # $2.50 per 1M tokens
-            completion_token_price=Decimal("10.00") / 1000000,  # $10.00 per 1M tokens
-        ),
-        "gpt-4o-realtime-preview": TokenPricingModel(
-            prompt_token_price=Decimal("5.00") / 1000000,  # $5.00 per 1M tokens
-            completion_token_price=Decimal("20.00") / 1000000,  # $20.00 per 1M tokens
-        ),
-        "gpt-4o-mini": TokenPricingModel(
-            prompt_token_price=Decimal("0.15") / 1000000,  # $0.15 per 1M tokens
-            completion_token_price=Decimal("0.60") / 1000000,  # $0.60 per 1M tokens
-        ),
-        "gpt-4o-mini-audio-preview": TokenPricingModel(
-            prompt_token_price=Decimal("0.15") / 1000000,  # $0.15 per 1M tokens
-            completion_token_price=Decimal("0.60") / 1000000,  # $0.60 per 1M tokens
-        ),
-        "gpt-4o-mini-realtime-preview": TokenPricingModel(
-            prompt_token_price=Decimal("0.60") / 1000000,  # $0.60 per 1M tokens
-            completion_token_price=Decimal("2.40") / 1000000,  # $2.40 per 1M tokens
-        ),
-        "gpt-4o-search-preview": TokenPricingModel(
-            prompt_token_price=Decimal("2.50") / 1000000,  # $2.50 per 1M tokens
-            completion_token_price=Decimal("10.00") / 1000000,  # $10.00 per 1M tokens
-        ),
-        "gpt-4o-mini-search-preview": TokenPricingModel(
-            prompt_token_price=Decimal("0.15") / 1000000,  # $0.15 per 1M tokens
-            completion_token_price=Decimal("0.60") / 1000000,  # $0.60 per 1M tokens
-        ),
-        "o1": TokenPricingModel(
-            prompt_token_price=Decimal("15.00") / 1000000,  # $15.00 per 1M tokens
-            completion_token_price=Decimal("60.00") / 1000000,  # $60.00 per 1M tokens
-        ),
-        "o1-pro": TokenPricingModel(
-            prompt_token_price=Decimal("150.00") / 1000000,  # $150.00 per 1M tokens
-            completion_token_price=Decimal("600.00") / 1000000,  # $600.00 per 1M tokens
-        ),
-        "o1-mini": TokenPricingModel(
-            prompt_token_price=Decimal("1.10") / 1000000,  # $1.10 per 1M tokens
-            completion_token_price=Decimal("4.40") / 1000000,  # $4.40 per 1M tokens
-        ),
-        "o3": TokenPricingModel(
-            prompt_token_price=Decimal("10.00") / 1000000,  # $10.00 per 1M tokens
-            completion_token_price=Decimal("40.00") / 1000000,  # $40.00 per 1M tokens
-        ),
-        "o3-mini": TokenPricingModel(
-            prompt_token_price=Decimal("1.10") / 1000000,  # $1.10 per 1M tokens
-            completion_token_price=Decimal("4.40") / 1000000,  # $4.40 per 1M tokens
-        ),
-        "o4-mini": TokenPricingModel(
-            prompt_token_price=Decimal("1.10") / 1000000,  # $1.10 per 1M tokens
-            completion_token_price=Decimal("4.40") / 1000000,  # $4.40 per 1M tokens
-        ),
-        "computer-use-preview": TokenPricingModel(
-            prompt_token_price=Decimal("3.00") / 1000000,  # $3.00 per 1M tokens
-            completion_token_price=Decimal("12.00") / 1000000,  # $12.00 per 1M tokens
-        ),
-        "gpt-image-1": TokenPricingModel(
-            prompt_token_price=Decimal("5.00") / 1000000,  # $5.00 per 1M tokens
-            completion_token_price=Decimal("0") / 1000000,  # No output pricing shown
-        ),
-        "codex-mini-latest": TokenPricingModel(
-            prompt_token_price=Decimal("1.50") / 1000000,  # $1.50 per 1M tokens
-            completion_token_price=Decimal("6.00") / 1000000,  # $6.00 per 1M tokens
-        ),
-        # Transcription models
-        "gpt-4o-transcribe": TokenPricingModel(
-            prompt_token_price=Decimal("2.50") / 1000000,  # $2.50 per 1M tokens
-            completion_token_price=Decimal("10.00") / 1000000,  # $10.00 per 1M tokens
-        ),
-        "gpt-4o-mini-transcribe": TokenPricingModel(
-            prompt_token_price=Decimal("1.25") / 1000000,  # $1.25 per 1M tokens
-            completion_token_price=Decimal("5.00") / 1000000,  # $5.00 per 1M tokens
-        ),
-        # TTS models with token-based pricing
-        "gpt-4o-mini-tts": TokenPricingModel(
-            prompt_token_price=Decimal("0.60") / 1000000,  # $0.60 per 1M tokens
-            completion_token_price=Decimal("0")
-            / 1000000,  # No completion tokens for TTS
-        ),
-    },
-    ServiceProviders.GROQ: {
-        "llama-3.3-70b-versatile": TokenPricingModel(
-            prompt_token_price=Decimal("0.00059") / 1000,  # $0.00059 per 1K tokens
-            completion_token_price=Decimal("0.00079") / 1000,  # $0.00079 per 1K tokens
-        ),
-        "deepseek-r1-distill-llama-70b": TokenPricingModel(
-            prompt_token_price=Decimal("0.00059") / 1000,  # Assuming similar pricing
-            completion_token_price=Decimal("0.00079") / 1000,
-        ),
-    },
-    ServiceProviders.AZURE: {
-        "gpt-4.1-mini": TokenPricingModel(
-            prompt_token_price=Decimal("0.44") / 1000000,  # $0.40 per 1M tokens
-            completion_token_price=Decimal("8.80")
-            / 1000000,  # $1.60 per 1M tokens if using data zone
-        )
-    },
-}

api/services/pricing/models.py

@@ -1,89 +0,0 @@
-"""
-Base pricing models for different service types.
-"""
-
-from decimal import Decimal
-from enum import Enum
-from typing import Any, Dict
-
-
-class CostType(Enum):
-    LLM_TOKENS = "llm_tokens"
-    TTS_CHARACTERS = "tts_characters"
-    STT_SECONDS = "stt_seconds"
-
-
-class PricingModel:
-    """Base class for pricing models"""
-
-    def calculate_cost(self, usage: Any) -> Decimal:
-        """Calculate cost based on usage"""
-        raise NotImplementedError
-
-
-class TokenPricingModel(PricingModel):
-    """Pricing model for token-based services (LLM)"""
-
-    def __init__(
-        self,
-        prompt_token_price: Decimal,
-        completion_token_price: Decimal,
-        cache_read_discount: Decimal = Decimal("0.5"),  # 50% discount for cache reads
-        cache_creation_multiplier: Decimal = Decimal(
-            "1.25"
-        ),  # 25% premium for cache creation
-    ):
-        self.prompt_token_price = prompt_token_price
-        self.completion_token_price = completion_token_price
-        self.cache_read_discount = cache_read_discount
-        self.cache_creation_multiplier = cache_creation_multiplier
-
-    def calculate_cost(self, usage: Dict[str, int]) -> Decimal:
-        """Calculate cost for LLM token usage"""
-        prompt_tokens = usage.get("prompt_tokens", 0)
-        completion_tokens = usage.get("completion_tokens", 0)
-        cache_read_tokens = usage.get("cache_read_input_tokens") or 0
-        cache_creation_tokens = usage.get("cache_creation_input_tokens") or 0
-
-        # Base cost
-        prompt_cost = Decimal(prompt_tokens) * self.prompt_token_price
-        completion_cost = Decimal(completion_tokens) * self.completion_token_price
-
-        # Cache adjustments
-        cache_read_savings = (
-            Decimal(cache_read_tokens)
-            * self.prompt_token_price
-            * self.cache_read_discount
-        )
-        cache_creation_premium = (
-            Decimal(cache_creation_tokens)
-            * self.prompt_token_price
-            * (self.cache_creation_multiplier - 1)
-        )
-
-        total_cost = (
-            prompt_cost + completion_cost - cache_read_savings + cache_creation_premium
-        )
-        return max(total_cost, Decimal("0"))  # Ensure non-negative
-
-
-class CharacterPricingModel(PricingModel):
-    """Pricing model for character-based services (TTS)"""
-
-    def __init__(self, character_price: Decimal):
-        self.character_price = character_price
-
-    def calculate_cost(self, character_count: int) -> Decimal:
-        """Calculate cost for TTS character usage"""
-        return Decimal(character_count) * self.character_price
-
-
-class TimePricingModel(PricingModel):
-    """Pricing model for time-based services (STT)"""
-
-    def __init__(self, second_price: Decimal):
-        self.second_price = second_price
-
-    def calculate_cost(self, seconds: float) -> Decimal:
-        """Calculate cost for STT time usage"""
-        return Decimal(str(seconds)) * self.second_price

api/services/pricing/registry.py

@@ -1,18 +0,0 @@
-"""
-Main pricing registry that combines all service type pricing models.
-"""
-
-from typing import Dict
-
-from .embeddings import EMBEDDINGS_PRICING
-from .llm import LLM_PRICING
-from .stt import STT_PRICING
-from .tts import TTS_PRICING
-
-# Combined pricing registry for all service types
-PRICING_REGISTRY: Dict = {
-    "llm": LLM_PRICING,
-    "tts": TTS_PRICING,
-    "stt": STT_PRICING,
-    "embeddings": EMBEDDINGS_PRICING,
-}

api/services/pricing/run_usage_response.py

@@ -1,13 +0,0 @@
-"""Format workflow run usage for public API responses."""
-
-
-def format_public_usage_info(usage_info: dict | None) -> dict | None:
-    if not usage_info:
-        return None
-
-    return {
-        "llm": usage_info.get("llm") or {},
-        "tts": usage_info.get("tts") or {},
-        "stt": usage_info.get("stt") or {},
-        "call_duration_seconds": usage_info.get("call_duration_seconds"),
-    }

api/services/pricing/stt.py

@@ -1,26 +0,0 @@
-"""
-STT (Speech-to-Text) pricing models for different providers.
-
-Prices are per second for STT services.
-"""
-
-from decimal import Decimal
-from typing import Dict
-
-from api.services.configuration.registry import ServiceProviders
-
-from .models import TimePricingModel
-
-# STT pricing registry
-STT_PRICING: Dict[str, Dict[str, TimePricingModel]] = {
-    ServiceProviders.DEEPGRAM: {
-        "nova-3-general": TimePricingModel(Decimal("0.0077") / 60),
-        "nova-2": TimePricingModel(Decimal("0.0058") / 60),
-        "default": TimePricingModel(Decimal("0.0077") / 60),
-    },
-    ServiceProviders.OPENAI: {
-        "gpt-4o-transcribe": TimePricingModel(Decimal("0.015") / 60),
-        "default": TimePricingModel(Decimal("0.015") / 60),
-    },
-    "default": {"default": TimePricingModel(Decimal("0.0077") / 60)},
-}

api/services/pricing/tts.py

@@ -1,30 +0,0 @@
-"""
-TTS (Text-to-Speech) pricing models for different providers.
-
-Prices are per character for TTS services.
-"""
-
-from decimal import Decimal
-from typing import Dict
-
-from api.services.configuration.registry import ServiceProviders
-
-from .models import CharacterPricingModel
-
-# TTS pricing registry
-TTS_PRICING: Dict[str, Dict[str, CharacterPricingModel]] = {
-    ServiceProviders.OPENAI: {
-        "gpt-4o-mini-tts": CharacterPricingModel(Decimal("0.6") / 1_00_00_000),
-        "default": CharacterPricingModel(Decimal("0.6") / 1_00_00_000),
-    },
-    ServiceProviders.DEEPGRAM: {
-        "aura-2": CharacterPricingModel(Decimal("0.030") / 1_000),
-        "aura-1": CharacterPricingModel(Decimal("0.015") / 1_000),
-        "default": CharacterPricingModel(Decimal("0.030") / 1_000),
-    },
-    ServiceProviders.ELEVENLABS: {
-        # 6400 usd per 250*1e6 characters
-        "default": CharacterPricingModel(Decimal("0.0256") / 1_000)
-    },
-    "default": {"default": CharacterPricingModel(Decimal("0.030") / 1_000)},
-}

api/services/pricing/workflow_run_cost.py

@@ -1,230 +0,0 @@
-from decimal import Decimal
-
-from loguru import logger
-
-from api.db import db_client
-from api.enums import WorkflowRunMode
-from api.services.pricing.cost_calculator import cost_calculator
-from api.services.telephony.factory import get_telephony_provider_for_run
-
-
-async def _fetch_telephony_cost(workflow_run) -> dict | None:
-    """Fetch telephony call cost. Returns a dict with cost_usd and provider_name, or None."""
-    if (
-        workflow_run.mode
-        not in [WorkflowRunMode.TWILIO.value, WorkflowRunMode.VONAGE.value]
-        or not workflow_run.cost_info
-    ):
-        return None
-
-    call_id = workflow_run.cost_info.get("call_id")
-    if not call_id:
-        logger.warning(f"call_id not found in cost_info")
-        return None
-
-    provider_name = workflow_run.mode.lower() if workflow_run.mode else ""
-
-    workflow = await db_client.get_workflow_by_id(workflow_run.workflow_id)
-    if not workflow:
-        logger.warning("Workflow not found for workflow run")
-        raise Exception("Workflow not found")
-
-    provider = await get_telephony_provider_for_run(
-        workflow_run, workflow.organization_id
-    )
-    call_cost_info = await provider.get_call_cost(call_id)
-
-    if call_cost_info.get("status") == "error":
-        logger.error(
-            f"Failed to fetch {provider_name} call cost: {call_cost_info.get('error')}"
-        )
-        return None
-
-    cost_usd = call_cost_info.get("cost_usd", 0.0)
-    logger.info(
-        f"{provider_name.title()} call cost: ${cost_usd:.6f} USD for call {call_id}"
-    )
-    return {"cost_usd": cost_usd, "provider_name": provider_name}
-
-
-async def _update_organization_usage(
-    org, dograh_tokens: float, duration_seconds: float, charge_usd: float | None
-) -> None:
-    """Update organization usage after a workflow run."""
-    org_id = org.id
-    await db_client.update_usage_after_run(
-        org_id, dograh_tokens, duration_seconds, charge_usd
-    )
-    if charge_usd is not None:
-        logger.info(
-            f"Updated organization usage with ${charge_usd:.2f} USD ({dograh_tokens} Dograh Tokens) and {duration_seconds}s duration for org {org_id}"
-        )
-    else:
-        logger.info(
-            f"Updated organization usage with {dograh_tokens} Dograh Tokens and {duration_seconds}s duration for org {org_id}"
-        )
-
-
-async def _get_pricing_organization(workflow_run):
-    workflow = getattr(workflow_run, "workflow", None)
-    organization_id = getattr(workflow, "organization_id", None)
-    if organization_id is None and workflow and workflow.user:
-        organization_id = workflow.user.selected_organization_id
-    if organization_id is None:
-        return None
-    return await db_client.get_organization_by_id(organization_id)
-
-
-async def _build_usage_cost_snapshot(
-    usage_info: dict | None,
-    *,
-    workflow_run=None,
-    include_telephony_cost: bool = False,
-    organization=None,
-    calculated_at: str | None = None,
-) -> dict | None:
-    if not usage_info:
-        logger.warning("No usage info available for workflow run")
-        return None
-
-    cost_breakdown = cost_calculator.calculate_total_cost(usage_info)
-
-    if include_telephony_cost and workflow_run is not None:
-        try:
-            telephony_cost = await _fetch_telephony_cost(workflow_run)
-            if telephony_cost:
-                telephony_cost_usd = telephony_cost["cost_usd"]
-                provider_name = telephony_cost["provider_name"]
-                cost_breakdown["telephony_call"] = telephony_cost_usd
-                cost_breakdown[f"{provider_name}_call"] = telephony_cost_usd
-                cost_breakdown["total"] = (
-                    float(cost_breakdown["total"]) + telephony_cost_usd
-                )
-        except Exception as e:
-            logger.error(f"Failed to fetch telephony call cost: {e}")
-            # Don't fail the whole cost calculation if telephony API fails
-
-    total_cost_usd = Decimal(str(cost_breakdown["total"]))
-    dograh_tokens = float(total_cost_usd * Decimal("100"))
-
-    if organization is None and workflow_run is not None:
-        organization = await _get_pricing_organization(workflow_run)
-
-    charge_usd = None
-    if organization and organization.price_per_second_usd:
-        duration_seconds = usage_info.get("call_duration_seconds", 0)
-        charge_usd = float(
-            Decimal(str(duration_seconds))
-            * Decimal(str(organization.price_per_second_usd))
-        )
-
-    cost_info = {
-        "cost_breakdown": cost_breakdown,
-        "total_cost_usd": float(total_cost_usd),
-        "dograh_token_usage": dograh_tokens,
-        "calculated_at": calculated_at
-        or (workflow_run.created_at.isoformat() if workflow_run is not None else None),
-        "call_duration_seconds": usage_info.get("call_duration_seconds", 0),
-    }
-
-    if charge_usd is not None:
-        cost_info["charge_usd"] = charge_usd
-        cost_info["price_per_second_usd"] = organization.price_per_second_usd
-
-    return cost_info
-
-
-async def build_workflow_run_cost_info(workflow_run) -> dict | None:
-    cost_info = await _build_usage_cost_snapshot(
-        workflow_run.usage_info,
-        workflow_run=workflow_run,
-        include_telephony_cost=True,
-        calculated_at=workflow_run.created_at.isoformat(),
-    )
-    if cost_info is None:
-        return None
-    return {
-        **(workflow_run.cost_info or {}),
-        **cost_info,
-    }
-
-
-async def save_workflow_run_cost_info(
-    workflow_run_id: int, cost_info: dict | None
-) -> None:
-    if cost_info is None:
-        return
-    await db_client.update_workflow_run(run_id=workflow_run_id, cost_info=cost_info)
-
-
-async def apply_workflow_run_usage_to_organization(
-    workflow_run, cost_info: dict | None
-) -> None:
-    if cost_info is None:
-        return
-
-    org = await _get_pricing_organization(workflow_run)
-    if not org:
-        return
-
-    await _update_organization_usage(
-        org,
-        float(cost_info.get("dograh_token_usage") or 0),
-        float(cost_info.get("call_duration_seconds") or 0),
-        cost_info.get("charge_usd"),
-    )
-
-
-async def apply_usage_delta_to_organization(
-    workflow_run, usage_info: dict | None
-) -> dict | None:
-    org = await _get_pricing_organization(workflow_run)
-    if not org:
-        return None
-
-    cost_info = await _build_usage_cost_snapshot(usage_info, organization=org)
-    if cost_info is None:
-        return None
-
-    await _update_organization_usage(
-        org,
-        float(cost_info.get("dograh_token_usage") or 0),
-        float(cost_info.get("call_duration_seconds") or 0),
-        cost_info.get("charge_usd"),
-    )
-    return cost_info
-
-
-async def calculate_workflow_run_cost(workflow_run_id: int):
-    logger.debug("Calculating cost for workflow run")
-
-    workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
-    if not workflow_run:
-        logger.warning("Workflow run not found")
-        return
-
-    try:
-        cost_info = await build_workflow_run_cost_info(workflow_run)
-        if cost_info is None:
-            return
-
-        await save_workflow_run_cost_info(workflow_run_id, cost_info)
-
-        try:
-            await apply_workflow_run_usage_to_organization(workflow_run, cost_info)
-        except Exception as e:
-            org = await _get_pricing_organization(workflow_run)
-            if org:
-                logger.error(
-                    f"Failed to update organization usage for org {org.id}: {e}"
-                )
-            else:
-                logger.error(f"Failed to update organization usage: {e}")
-            # Don't fail the whole cost calculation if usage update fails
-
-        logger.info(
-            f"Calculated cost for workflow run: ${cost_info['total_cost_usd']:.6f} USD ({cost_info['dograh_token_usage']} Dograh Tokens)"
-        )
-    except Exception as e:
-        logger.error(f"Error calculating cost for workflow run: {e}")
-        raise

api/services/quota_service.py

@@ -5,15 +5,38 @@ across different endpoints (WebRTC signaling, telephony, public API triggers).
 """
 
 from dataclasses import dataclass
+from typing import Any
 
 from loguru import logger
 
+from api.constants import DEPLOYMENT_MODE
 from api.db import db_client
 from api.db.models import UserModel
+from api.services.configuration.ai_model_configuration import (
+    get_effective_ai_model_configuration_for_workflow,
+)
 from api.services.configuration.registry import ServiceProviders
-from api.services.configuration.resolve import resolve_effective_config
+from api.services.managed_model_services import (
+    MPS_CORRELATION_ID_CONTEXT_KEY,
+    get_dograh_service_api_key,
+    uses_managed_model_services_v2,
+)
 from api.services.mps_service_key_client import mps_service_key_client
 
+MINIMUM_DOGRAH_CREDITS_FOR_CALL = 0.10
+
+LEGACY_QUOTA_EXCEEDED_MESSAGE = (
+    "You have exhausted your trial credits. "
+    "Please email founders@dograh.com for additional Dograh credits "
+    "or change providers in Models configurations."
+)
+
+BILLING_V2_QUOTA_EXCEEDED_MESSAGE = (
+    "You have exhausted your Dograh credits. "
+    "Please purchase more credits from /billing "
+    "or change providers in Models configurations."
+)
+
 
 @dataclass
 class QuotaCheckResult:
@@ -24,104 +47,359 @@ class QuotaCheckResult:
     error_code: str = ""
 
 
-async def check_dograh_quota(
-    user: UserModel, workflow_id: int | None = None
-) -> QuotaCheckResult:
-    """Check if user has sufficient Dograh quota for making a call.
-
-    This function checks if the user is using any Dograh services (LLM, STT, TTS)
-    and validates that they have sufficient credits remaining.
-
-    When ``workflow_id`` is provided, the workflow's per-workflow
-    ``model_overrides`` are merged onto the user's global config so the quota
-    check runs against the credentials that will actually be used for the call
-    (rather than always falling back to the user's defaults).
-
-    Args:
-        user: The user to check quota for
-        workflow_id: Optional workflow whose ``model_overrides`` should be
-            applied when resolving the effective service config.
-
-    Returns:
-        QuotaCheckResult with has_quota=True if user has sufficient quota or
-        is not using Dograh services, or has_quota=False with error_message
-        if quota is insufficient.
-    """
+def _safe_float(value: Any, default: float = 0.0) -> float:
     try:
-        # Get user configurations
-        user_config = await db_client.get_user_configurations(user.id)
+        return float(value)
+    except (TypeError, ValueError):
+        return default
 
-        if workflow_id is not None:
-            workflow = await db_client.get_workflow_by_id(workflow_id)
-            if workflow:
-                model_overrides = (workflow.workflow_configurations or {}).get(
-                    "model_overrides"
+
+def _insufficient_billing_v2_quota_result() -> QuotaCheckResult:
+    return QuotaCheckResult(
+        has_quota=False,
+        error_code="insufficient_credits",
+        error_message=BILLING_V2_QUOTA_EXCEEDED_MESSAGE,
+    )
+
+
+def _insufficient_legacy_quota_result() -> QuotaCheckResult:
+    return QuotaCheckResult(
+        has_quota=False,
+        error_code="quota_exceeded",
+        error_message=LEGACY_QUOTA_EXCEEDED_MESSAGE,
+    )
+
+
+def _service_uses_dograh(service: Any) -> bool:
+    provider = getattr(service, "provider", None)
+    return (
+        provider == ServiceProviders.DOGRAH or provider == ServiceProviders.DOGRAH.value
+    )
+
+
+def _dograh_api_keys(user_config: Any) -> set[str]:
+    api_keys: set[str] = set()
+    for section_name in ("llm", "stt", "tts", "embeddings"):
+        service = getattr(user_config, section_name, None)
+        if not _service_uses_dograh(service):
+            continue
+        if hasattr(service, "get_all_api_keys"):
+            all_api_keys = [
+                api_key
+                for api_key in service.get_all_api_keys()
+                if isinstance(api_key, str) and api_key
+            ]
+            if all_api_keys:
+                api_keys.update(all_api_keys)
+                continue
+        api_key = getattr(service, "api_key", None)
+        if api_key:
+            api_keys.add(api_key)
+    return api_keys
+
+
+async def _store_run_correlation_id(
+    workflow_run_id: int | None,
+    correlation_id: str | None,
+) -> None:
+    if not workflow_run_id or not correlation_id:
+        return
+
+    workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
+    if not workflow_run:
+        logger.warning(
+            "Could not store MPS correlation id for missing workflow run {}",
+            workflow_run_id,
+        )
+        return
+
+    initial_context = dict(workflow_run.initial_context or {})
+    if initial_context.get(MPS_CORRELATION_ID_CONTEXT_KEY) == correlation_id:
+        return
+
+    initial_context[MPS_CORRELATION_ID_CONTEXT_KEY] = correlation_id
+    await db_client.update_workflow_run(
+        workflow_run_id,
+        initial_context=initial_context,
+    )
+
+
+async def _authorize_hosted_workflow_run_start(
+    *,
+    workflow_owner: UserModel,
+    organization_id: int | None,
+    workflow_id: int | None,
+    workflow_run_id: int | None,
+    user_config: Any,
+) -> tuple[QuotaCheckResult, bool]:
+    """Authorize hosted v2 billing and return whether MPS handled enforcement."""
+    if DEPLOYMENT_MODE == "oss" or organization_id is None:
+        return QuotaCheckResult(has_quota=True), False
+
+    requires_correlation = bool(
+        workflow_run_id and uses_managed_model_services_v2(user_config)
+    )
+    service_key = (
+        get_dograh_service_api_key(user_config) if requires_correlation else None
+    )
+    if requires_correlation and not service_key:
+        return (
+            QuotaCheckResult(
+                has_quota=False,
+                error_code="invalid_service_key",
+                error_message=(
+                    "You have invalid keys in your model configuration. "
+                    "Please validate the service keys."
+                ),
+            ),
+            True,
+        )
+
+    try:
+        authorization = await mps_service_key_client.authorize_workflow_run_start(
+            organization_id=organization_id,
+            workflow_run_id=workflow_run_id,
+            service_key=service_key,
+            require_correlation_id=requires_correlation,
+            minimum_credits=MINIMUM_DOGRAH_CREDITS_FOR_CALL,
+            created_by=(
+                str(workflow_owner.provider_id)
+                if workflow_owner.provider_id is not None
+                else None
+            ),
+            metadata={
+                "dograh_user_id": str(workflow_owner.id),
+                "workflow_id": workflow_id,
+            },
+        )
+    except Exception as e:
+        logger.error(
+            "Failed to authorize workflow start with MPS for org {}: {}",
+            organization_id,
+            e,
+        )
+        return (
+            QuotaCheckResult(
+                has_quota=False,
+                error_code="quota_check_failed",
+                error_message="Could not verify Dograh credits. Please try again.",
+            ),
+            True,
+        )
+
+    billing_mode = authorization.get("billing_mode")
+    if billing_mode != "v2":
+        return QuotaCheckResult(has_quota=True), False
+
+    remaining = _safe_float(authorization.get("remaining_credits"))
+    if (
+        not authorization.get("allowed", False)
+        or remaining < MINIMUM_DOGRAH_CREDITS_FOR_CALL
+    ):
+        logger.warning(
+            "Insufficient Dograh billing v2 credits for org {}: {:.2f} credits remaining",
+            organization_id,
+            remaining,
+        )
+        return _insufficient_billing_v2_quota_result(), True
+
+    try:
+        await _store_run_correlation_id(
+            workflow_run_id,
+            authorization.get("correlation_id"),
+        )
+    except Exception as e:
+        logger.error(
+            "Failed to store MPS correlation id for workflow_run_id {}: {}",
+            workflow_run_id,
+            e,
+        )
+        return (
+            QuotaCheckResult(
+                has_quota=False,
+                error_code="quota_check_failed",
+                error_message="Could not verify Dograh credits. Please try again.",
+            ),
+            True,
+        )
+    logger.info(
+        "Dograh billing v2 run authorization passed for org {}: {:.2f} credits remaining",
+        organization_id,
+        remaining,
+    )
+    return QuotaCheckResult(has_quota=True), True
+
+
+async def _authorize_legacy_dograh_keys(
+    *,
+    dograh_api_keys: set[str],
+    organization_id: int | None,
+    workflow_owner: UserModel,
+) -> QuotaCheckResult:
+    for api_key in dograh_api_keys:
+        try:
+            usage = await mps_service_key_client.check_service_key_usage(
+                api_key,
+                organization_id=organization_id,
+                created_by=workflow_owner.provider_id,
+            )
+            remaining = usage.get("remaining_credits", 0.0)
+
+            # Require at least $0.10 for a short call
+            if remaining < MINIMUM_DOGRAH_CREDITS_FOR_CALL:
+                logger.warning(
+                    f"Insufficient Dograh credits for key ...{api_key[-8:]}: "
+                    f"${remaining:.2f} remaining"
                 )
-                if model_overrides:
-                    user_config = resolve_effective_config(user_config, model_overrides)
+                return _insufficient_legacy_quota_result()
 
-        # Check if user is using any Dograh service
-        using_dograh = False
-        dograh_api_keys = set()
-
-        if user_config.llm and user_config.llm.provider == ServiceProviders.DOGRAH:
-            using_dograh = True
-            dograh_api_keys.add(user_config.llm.api_key)
-
-        if user_config.stt and user_config.stt.provider == ServiceProviders.DOGRAH:
-            using_dograh = True
-            dograh_api_keys.add(user_config.stt.api_key)
-
-        if user_config.tts and user_config.tts.provider == ServiceProviders.DOGRAH:
-            using_dograh = True
-            dograh_api_keys.add(user_config.tts.api_key)
-
-        # If not using Dograh, quota check passes
-        if not using_dograh:
-            return QuotaCheckResult(has_quota=True)
-
-        # Check quota for ALL Dograh keys
-        for api_key in dograh_api_keys:
-            try:
-                usage = await mps_service_key_client.check_service_key_usage(
-                    api_key, created_by=user.provider_id
-                )
-                remaining = usage.get("remaining_credits", 0.0)
-
-                # Require at least $0.10 for a short call
-                if remaining < 0.10:
-                    logger.warning(
-                        f"Insufficient Dograh credits for key ...{api_key[-8:]}: "
-                        f"${remaining:.2f} remaining"
-                    )
-                    return QuotaCheckResult(
-                        has_quota=False,
-                        error_code="quota_exceeded",
-                        error_message=(
-                            "You have exhausted your trial credits. "
-                            "Please email founders@dograh.com for additional Dograh credits "
-                            "or change providers in Models configurations."
-                        ),
-                    )
-
-                logger.info(
-                    f"Dograh quota check passed for key ...{api_key[-8:]}: "
-                    f"{remaining:.2f} credits remaining"
-                )
-            except Exception as e:
-                logger.error(f"Failed to check quota for Dograh key: {str(e)}")
-                error_str = str(e)
-                if "404" in error_str or "not found" in error_str.lower():
-                    return QuotaCheckResult(
-                        has_quota=False,
-                        error_code="invalid_service_key",
-                        error_message="You have invalid keys in your model configuration. Please validate the service keys.",
-                    )
+            logger.info(
+                f"Dograh quota check passed for key ...{api_key[-8:]}: "
+                f"{remaining:.2f} credits remaining"
+            )
+        except Exception as e:
+            logger.error(f"Failed to check quota for Dograh key: {str(e)}")
+            error_str = str(e)
+            if "404" in error_str or "not found" in error_str.lower():
                 return QuotaCheckResult(
                     has_quota=False,
-                    error_code="quota_check_failed",
-                    error_message="Could not verify Dograh credits. Please try again.",
+                    error_code="invalid_service_key",
+                    error_message="You have invalid keys in your model configuration. Please validate the service keys.",
                 )
+            return QuotaCheckResult(
+                has_quota=False,
+                error_code="quota_check_failed",
+                error_message="Could not verify Dograh credits. Please try again.",
+            )
+
+    return QuotaCheckResult(has_quota=True)
+
+
+async def _authorize_oss_managed_v2_correlation(
+    *,
+    workflow_id: int,
+    workflow_run_id: int | None,
+    user_config: Any,
+) -> QuotaCheckResult:
+    if not workflow_run_id or not uses_managed_model_services_v2(user_config):
+        return QuotaCheckResult(has_quota=True)
+
+    service_key = get_dograh_service_api_key(user_config)
+    if not service_key:
+        return QuotaCheckResult(
+            has_quota=False,
+            error_code="invalid_service_key",
+            error_message=(
+                "You have invalid keys in your model configuration. "
+                "Please validate the service keys."
+            ),
+        )
+
+    try:
+        response = await mps_service_key_client.create_correlation_id(
+            service_key=service_key,
+            workflow_run_id=workflow_run_id,
+        )
+        await _store_run_correlation_id(
+            workflow_run_id,
+            response.get("correlation_id"),
+        )
+    except Exception as e:
+        logger.error(
+            "Failed to authorize OSS managed v2 workflow start for workflow {} run {}: {}",
+            workflow_id,
+            workflow_run_id,
+            e,
+        )
+        return QuotaCheckResult(
+            has_quota=False,
+            error_code="quota_check_failed",
+            error_message="Could not verify Dograh credits. Please try again.",
+        )
+
+    return QuotaCheckResult(has_quota=True)
+
+
+async def authorize_workflow_run_start(
+    *,
+    workflow_id: int,
+    workflow_run_id: int | None = None,
+    actor_user: UserModel | None = None,
+) -> QuotaCheckResult:
+    """Authorize a workflow run before any billable call/text runtime starts.
+
+    The workflow organization is the billing subject for hosted v2. The workflow
+    owner is used only to resolve the effective model configuration and legacy
+    service-key metadata.
+    """
+    try:
+        workflow = await db_client.get_workflow_by_id(workflow_id)
+        if not workflow:
+            return QuotaCheckResult(
+                has_quota=False,
+                error_code="workflow_not_found",
+                error_message="Workflow not found",
+            )
+
+        actor_org_id = getattr(actor_user, "selected_organization_id", None)
+        if actor_org_id is not None and actor_org_id != workflow.organization_id:
+            logger.warning(
+                "Workflow start authorization denied: actor org {} does not match workflow {} org {}",
+                actor_org_id,
+                workflow_id,
+                workflow.organization_id,
+            )
+            return QuotaCheckResult(
+                has_quota=False,
+                error_code="workflow_not_found",
+                error_message="Workflow not found",
+            )
+
+        workflow_owner = await db_client.get_user_by_id(workflow.user_id)
+        if not workflow_owner:
+            return QuotaCheckResult(
+                has_quota=False,
+                error_code="user_not_found",
+                error_message="User not found",
+            )
+
+        user_config = await get_effective_ai_model_configuration_for_workflow(
+            user_id=workflow_owner.id,
+            organization_id=workflow.organization_id,
+            workflow_configurations=workflow.workflow_configurations,
+        )
+
+        if DEPLOYMENT_MODE != "oss":
+            hosted_result, hosted_enforced = await _authorize_hosted_workflow_run_start(
+                workflow_owner=workflow_owner,
+                organization_id=workflow.organization_id,
+                workflow_id=workflow.id,
+                workflow_run_id=workflow_run_id,
+                user_config=user_config,
+            )
+            if hosted_enforced or not hosted_result.has_quota:
+                return hosted_result
+
+        dograh_api_keys = _dograh_api_keys(user_config)
+        if not dograh_api_keys:
+            return QuotaCheckResult(has_quota=True)
+
+        legacy_result = await _authorize_legacy_dograh_keys(
+            dograh_api_keys=dograh_api_keys,
+            organization_id=(
+                None if DEPLOYMENT_MODE == "oss" else workflow.organization_id
+            ),
+            workflow_owner=workflow_owner,
+        )
+        if not legacy_result.has_quota:
+            return legacy_result
+
+        if DEPLOYMENT_MODE == "oss":
+            return await _authorize_oss_managed_v2_correlation(
+                workflow_id=workflow.id,
+                workflow_run_id=workflow_run_id,
+                user_config=user_config,
+            )
 
         return QuotaCheckResult(has_quota=True)
 
@@ -129,30 +407,3 @@ async def check_dograh_quota(
         logger.error(f"Error during quota check: {str(e)}")
         # On unexpected error, allow the call to proceed
         return QuotaCheckResult(has_quota=True)
-
-
-async def check_dograh_quota_by_user_id(
-    user_id: int, workflow_id: int | None = None
-) -> QuotaCheckResult:
-    """Check Dograh quota by user ID.
-
-    Convenience function that fetches the user and then checks quota. When
-    ``workflow_id`` is provided, the workflow's ``model_overrides`` are
-    applied so the quota check evaluates the credentials that will actually
-    be used for the call.
-
-    Args:
-        user_id: The ID of the user to check quota for
-        workflow_id: Optional workflow whose per-workflow overrides should
-            be applied to the user's config before checking quota.
-
-    Returns:
-        QuotaCheckResult with quota status
-    """
-    user = await db_client.get_user_by_id(user_id)
-    if not user:
-        return QuotaCheckResult(
-            has_quota=False,
-            error_message="User not found",
-        )
-    return await check_dograh_quota(user, workflow_id=workflow_id)

api/services/reports/run_report.py

@@ -53,7 +53,7 @@ def build_run_report_csv(runs: List[Any]) -> io.StringIO:
     for run in runs:
         initial = run.initial_context or {}
         gathered = run.gathered_context or {}
-        cost = run.cost_info or {}
+        usage = run.usage_info or {}
 
         call_tags = gathered.get("call_tags", [])
         if isinstance(call_tags, list):
@@ -67,7 +67,7 @@ def build_run_report_csv(runs: List[Any]) -> io.StringIO:
             run.created_at.isoformat() if run.created_at else "",
             initial.get("phone_number", ""),
             gathered.get("mapped_call_disposition", ""),
-            cost.get("call_duration_seconds", ""),
+            usage.get("call_duration_seconds", ""),
         ]
 
         extracted = gathered.get("extracted_variables", {})

api/services/telephony/ari_manager.py

@@ -26,7 +26,7 @@ from loguru import logger
 from api.constants import REDIS_URL
 from api.db import db_client
 from api.enums import CallType, WorkflowRunMode
-from api.services.quota_service import check_dograh_quota_by_user_id
+from api.services.quota_service import authorize_workflow_run_start
 from api.services.telephony.call_transfer_manager import get_call_transfer_manager
 from api.services.telephony.transfer_event_protocol import (
     TransferEvent,
@@ -564,19 +564,7 @@ class ARIConnection:
 
             user_id = workflow.user_id
 
-            # 3. Check quota (apply per-workflow model_overrides).
-            quota_result = await check_dograh_quota_by_user_id(
-                user_id, workflow_id=inbound_workflow_id
-            )
-            if not quota_result.has_quota:
-                logger.warning(
-                    f"[ARI org={self.organization_id}] Quota exceeded for user {user_id} "
-                    f"— hanging up inbound call {channel_id}"
-                )
-                await self._delete_channel(channel_id)
-                return
-
-            # 4. Create workflow run
+            # 3. Create workflow run
             call_id = channel_id
             workflow_run = await db_client.create_workflow_run(
                 name=f"ARI Inbound {caller_number}",
@@ -602,6 +590,20 @@ class ARIConnection:
                 f"(caller={caller_number}, called={called_number})"
             )
 
+            # 4. Check quota after the run exists so hosted v2 can mint and
+            # store the MPS correlation id before the pipeline starts.
+            quota_result = await authorize_workflow_run_start(
+                workflow_id=inbound_workflow_id,
+                workflow_run_id=workflow_run.id,
+            )
+            if not quota_result.has_quota:
+                logger.warning(
+                    f"[ARI org={self.organization_id}] Quota exceeded for user {user_id} "
+                    f"— hanging up inbound call {channel_id}"
+                )
+                await self._delete_channel(channel_id)
+                return
+
             # 5. Answer the inbound channel
             await self._answer_channel(channel_id)
 

api/services/telephony/providers/cloudonix/routes.py

@@ -103,7 +103,8 @@ async def handle_cloudonix_cdr(request: Request):
         return {"status": "error", "message": "Missing domain field"}
 
     # Extract call_id to find workflow run
-    call_id = cdr_data.get("session").get("token")
+    session = cdr_data.get("session")
+    call_id = session.get("token") if isinstance(session, dict) else None
     logger.info(f"Cloudonix CDR data for call id {call_id} - {cdr_data}")
     if not call_id:
         logger.warning("Cloudonix CDR missing call_id field")

api/services/telephony/providers/vobiz/routes.py

@@ -6,9 +6,8 @@ provider registry — see ProviderSpec.router.
 
 import json
 from datetime import UTC, datetime
-from typing import Optional
 
-from fastapi import APIRouter, Header, Request
+from fastapi import APIRouter, HTTPException, Request
 from loguru import logger
 from pipecat.utils.run_context import set_current_run_id
 from starlette.responses import HTMLResponse
@@ -29,6 +28,30 @@ from api.utils.telephony_helper import (
 router = APIRouter()
 
 
+async def _verify_vobiz_callback(
+    provider,
+    webhook_url: str,
+    callback_data: dict,
+    headers: dict,
+    raw_body: str,
+    *,
+    log_prefix: str,
+) -> None:
+    """Verify a Vobiz callback signature, failing closed.
+
+    Vobiz signs every callback, so a missing signature header is an invalid
+    request — ``provider.verify_inbound_signature`` returns ``False`` for both
+    missing and forged signatures. Reject with HTTP 403 (per Vobiz's
+    callback-validation docs) so the caller never reaches status processing.
+    """
+    is_valid = await provider.verify_inbound_signature(
+        webhook_url, callback_data, headers, raw_body
+    )
+    if not is_valid:
+        logger.warning(f"{log_prefix} Invalid or missing Vobiz callback signature")
+        raise HTTPException(status_code=403, detail="Invalid webhook signature")
+
+
 @router.post("/vobiz-xml", include_in_schema=False)
 async def handle_vobiz_xml_webhook(
     workflow_id: int, user_id: int, workflow_run_id: int, organization_id: int
@@ -65,8 +88,6 @@ async def handle_vobiz_xml_webhook(
 async def handle_vobiz_hangup_callback(
     workflow_run_id: int,
     request: Request,
-    x_vobiz_signature: Optional[str] = Header(None),
-    x_vobiz_timestamp: Optional[str] = Header(None),
 ):
     """Handle Vobiz hangup callback (sent when call ends).
 
@@ -75,82 +96,23 @@ async def handle_vobiz_hangup_callback(
     """
     set_current_run_id(workflow_run_id)
 
-    # Logging all headers and body to understand what Vobiz actually sends
     all_headers = dict(request.headers)
-    logger.info(
-        f"[run {workflow_run_id}] Vobiz hangup callback - Headers: {json.dumps(all_headers)}"
-    )
 
     # Parse the callback data from the raw body so signed webhooks can verify
     # the exact bytes Vobiz sent without draining the request stream first.
     callback_data, raw_body = await parse_webhook_request(request)
 
-    # TODO: Remove this debug logging after Vobiz team clarifies webhook authentication
-    logger.info(
-        f"[run {workflow_run_id}] Vobiz hangup callback - Body: {json.dumps(callback_data)}"
-    )
     logger.info(
         f"[run {workflow_run_id}] Received Vobiz hangup callback {json.dumps(callback_data)}"
     )
 
-    # Verify signature if Vobiz provided any supported signature header.
-    has_vobiz_signature = any(
-        header in all_headers
-        for header in (
-            "x-vobiz-signature-v3",
-            "x-vobiz-signature-ma-v3",
-            "x-vobiz-signature-v2",
-            "x-vobiz-signature-ma-v2",
-        )
-    )
-    if has_vobiz_signature:
-        # We need the workflow run to get organization for provider credentials
-        workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
-        if not workflow_run:
-            logger.warning(
-                f"[run {workflow_run_id}] Workflow run not found for signature verification"
-            )
-            return {"status": "error", "reason": "workflow_run_not_found"}
-
-        workflow = await db_client.get_workflow_by_id(workflow_run.workflow_id)
-        if not workflow:
-            logger.warning(
-                f"[run {workflow_run_id}] Workflow not found for signature verification"
-            )
-            return {"status": "error", "reason": "workflow_not_found"}
-
-        provider = await get_telephony_provider_for_run(
-            workflow_run, workflow.organization_id
-        )
-
-        # Verify signature
-        backend_endpoint, _ = await get_backend_endpoints()
-        webhook_url = f"{backend_endpoint}/api/v1/telephony/vobiz/hangup-callback/{workflow_run_id}"
-
-        is_valid = await provider.verify_inbound_signature(
-            webhook_url,
-            callback_data,
-            all_headers,
-            raw_body,
-        )
-
-        if not is_valid:
-            logger.warning(
-                f"[run {workflow_run_id}] Invalid Vobiz hangup callback signature"
-            )
-            return {"status": "error", "reason": "invalid_signature"}
-
-        logger.info(f"[run {workflow_run_id}] Vobiz hangup callback signature verified")
-    else:
-        # Get workflow run for processing (signature verification already got it if needed)
-        workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
+    workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
     if not workflow_run:
         logger.warning(
             f"[run {workflow_run_id}] Workflow run not found for Vobiz hangup callback"
         )
         return {"status": "ignored", "reason": "workflow_run_not_found"}
 
-    # Get workflow and provider
     workflow = await db_client.get_workflow_by_id(workflow_run.workflow_id)
     if not workflow:
         logger.warning(f"[run {workflow_run_id}] Workflow not found")
@@ -160,6 +122,21 @@ async def handle_vobiz_hangup_callback(
         workflow_run, workflow.organization_id
     )
 
+    # Fail closed: Vobiz signs every callback, so reject unsigned/forged ones
+    # before they can mutate call state.
+    backend_endpoint, _ = await get_backend_endpoints()
+    webhook_url = (
+        f"{backend_endpoint}/api/v1/telephony/vobiz/hangup-callback/{workflow_run_id}"
+    )
+    await _verify_vobiz_callback(
+        provider,
+        webhook_url,
+        callback_data,
+        all_headers,
+        raw_body,
+        log_prefix=f"[run {workflow_run_id}]",
+    )
+
     logger.debug(
         f"[run {workflow_run_id}] Processing Vobiz hangup with provider: {provider.PROVIDER_NAME}"
     )
@@ -167,10 +144,6 @@ async def handle_vobiz_hangup_callback(
     # Parse the callback data into generic format
     parsed_data = provider.parse_status_callback(callback_data)
 
-    logger.debug(
-        f"[run {workflow_run_id}] Parsed Vobiz callback data: {json.dumps(parsed_data)}"
-    )
-
     # Create StatusCallbackRequest from parsed data
     status_update = StatusCallbackRequest(
         call_id=parsed_data["call_id"],
@@ -194,8 +167,6 @@ async def handle_vobiz_hangup_callback(
 async def handle_vobiz_ring_callback(
     workflow_run_id: int,
     request: Request,
-    x_vobiz_signature: Optional[str] = Header(None),
-    x_vobiz_timestamp: Optional[str] = Header(None),
 ):
     """Handle Vobiz ring callback (sent when call starts ringing).
 
@@ -204,84 +175,46 @@ async def handle_vobiz_ring_callback(
     """
     set_current_run_id(workflow_run_id)
 
-    # Logging all headers and body to understand what Vobiz actually sends
     all_headers = dict(request.headers)
-    logger.info(
-        f"[run {workflow_run_id}] Vobiz ring callback - Headers: {json.dumps(all_headers)}"
-    )
 
     # Parse the callback data from the raw body so signed webhooks can verify
     # the exact bytes Vobiz sent without draining the request stream first.
     callback_data, raw_body = await parse_webhook_request(request)
 
-    # TODO: Remove this debug logging after Vobiz team clarifies webhook authentication
-    logger.info(
-        f"[run {workflow_run_id}] Vobiz ring callback - Body: {json.dumps(callback_data)}"
-    )
-
     logger.info(
         f"[run {workflow_run_id}] Received Vobiz ring callback {json.dumps(callback_data)}"
     )
 
-    # Verify signature if Vobiz provided any supported signature header.
-    has_vobiz_signature = any(
-        header in all_headers
-        for header in (
-            "x-vobiz-signature-v3",
-            "x-vobiz-signature-ma-v3",
-            "x-vobiz-signature-v2",
-            "x-vobiz-signature-ma-v2",
-        )
-    )
-    if has_vobiz_signature:
-        # We need the workflow run to get organization for provider credentials
-        workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
-        if not workflow_run:
-            logger.warning(
-                f"[run {workflow_run_id}] Workflow run not found for signature verification"
-            )
-            return {"status": "error", "reason": "workflow_run_not_found"}
-
-        workflow = await db_client.get_workflow_by_id(workflow_run.workflow_id)
-        if not workflow:
-            logger.warning(
-                f"[run {workflow_run_id}] Workflow not found for signature verification"
-            )
-            return {"status": "error", "reason": "workflow_not_found"}
-
-        provider = await get_telephony_provider_for_run(
-            workflow_run, workflow.organization_id
-        )
-
-        # Verify signature
-        backend_endpoint, _ = await get_backend_endpoints()
-        webhook_url = (
-            f"{backend_endpoint}/api/v1/telephony/vobiz/ring-callback/{workflow_run_id}"
-        )
-
-        is_valid = await provider.verify_inbound_signature(
-            webhook_url,
-            callback_data,
-            all_headers,
-            raw_body,
-        )
-
-        if not is_valid:
-            logger.warning(
-                f"[run {workflow_run_id}] Invalid Vobiz ring callback signature"
-            )
-            return {"status": "error", "reason": "invalid_signature"}
-
-        logger.info(f"[run {workflow_run_id}] Vobiz ring callback signature verified")
-    else:
-        # Get workflow run for processing (signature verification already got it if needed)
-        workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
+    workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
     if not workflow_run:
         logger.warning(
             f"[run {workflow_run_id}] Workflow run not found for Vobiz ring callback"
         )
         return {"status": "ignored", "reason": "workflow_run_not_found"}
 
+    workflow = await db_client.get_workflow_by_id(workflow_run.workflow_id)
+    if not workflow:
+        logger.warning(f"[run {workflow_run_id}] Workflow not found")
+        return {"status": "ignored", "reason": "workflow_not_found"}
+
+    provider = await get_telephony_provider_for_run(
+        workflow_run, workflow.organization_id
+    )
+
+    # Fail closed: reject unsigned/forged ring callbacks before logging them.
+    backend_endpoint, _ = await get_backend_endpoints()
+    webhook_url = (
+        f"{backend_endpoint}/api/v1/telephony/vobiz/ring-callback/{workflow_run_id}"
+    )
+    await _verify_vobiz_callback(
+        provider,
+        webhook_url,
+        callback_data,
+        all_headers,
+        raw_body,
+        log_prefix=f"[run {workflow_run_id}]",
+    )
+
     # Log the ringing event
     telephony_callback_logs = workflow_run.logs.get("telephony_status_callbacks", [])
     ring_log = {
@@ -308,15 +241,10 @@ async def handle_vobiz_ring_callback(
 async def handle_vobiz_hangup_callback_by_workflow(
     workflow_id: int,
     request: Request,
-    x_vobiz_signature: Optional[str] = Header(None),
-    x_vobiz_timestamp: Optional[str] = Header(None),
 ):
     """Handle Vobiz hangup callback with workflow_id - finds workflow run by call_id."""
 
     all_headers = dict(request.headers)
-    logger.info(
-        f"[workflow {workflow_id}] Vobiz hangup callback - Headers: {json.dumps(all_headers)}"
-    )
 
     try:
         callback_data, raw_body = await parse_webhook_request(request)
@@ -364,35 +292,18 @@ async def handle_vobiz_hangup_callback_by_workflow(
         workflow_run, workflow.organization_id
     )
 
-    has_vobiz_signature = any(
-        header in all_headers
-        for header in (
-            "x-vobiz-signature-v3",
-            "x-vobiz-signature-ma-v3",
-            "x-vobiz-signature-v2",
-            "x-vobiz-signature-ma-v2",
-        )
+    # Fail closed: Vobiz signs every callback, so reject unsigned/forged ones
+    # before they can mutate call state.
+    backend_endpoint, _ = await get_backend_endpoints()
+    webhook_url = f"{backend_endpoint}/api/v1/telephony/vobiz/hangup-callback/workflow/{workflow_id}"
+    await _verify_vobiz_callback(
+        provider,
+        webhook_url,
+        callback_data,
+        all_headers,
+        raw_body,
+        log_prefix=f"[workflow {workflow_id}]",
     )
-    if has_vobiz_signature:
-        backend_endpoint, _ = await get_backend_endpoints()
-        webhook_url = f"{backend_endpoint}/api/v1/telephony/vobiz/hangup-callback/workflow/{workflow_id}"
-
-        is_valid = await provider.verify_inbound_signature(
-            webhook_url,
-            callback_data,
-            all_headers,
-            raw_body,
-        )
-
-        if not is_valid:
-            logger.warning(
-                f"[workflow {workflow_id}] Invalid Vobiz hangup callback signature"
-            )
-            return {"status": "error", "message": "invalid_signature"}
-
-        logger.info(
-            f"[workflow {workflow_id}] Vobiz hangup callback signature verified"
-        )
 
     try:
         parsed_data = provider.parse_status_callback(callback_data)

api/services/telephony/providers/vonage/routes.py

@@ -66,34 +66,6 @@ async def handle_vonage_events(
         logger.error(f"[run {workflow_run_id}] Workflow run not found")
         return {"status": "error", "message": "Workflow run not found"}
 
-    # For a completed call that includes cost info, capture it immediately
-    if event_data.get("status") == "completed":
-        # Vonage sometimes includes price info in the webhook
-        if "price" in event_data or "rate" in event_data:
-            try:
-                if workflow_run.cost_info:
-                    # Store immediate cost info if available
-                    cost_info = workflow_run.cost_info.copy()
-                    if "price" in event_data:
-                        cost_info["vonage_webhook_price"] = float(event_data["price"])
-                    if "rate" in event_data:
-                        cost_info["vonage_webhook_rate"] = float(event_data["rate"])
-                    if "duration" in event_data:
-                        cost_info["vonage_webhook_duration"] = int(
-                            event_data["duration"]
-                        )
-
-                    await db_client.update_workflow_run(
-                        run_id=workflow_run_id, cost_info=cost_info
-                    )
-                    logger.info(
-                        f"[run {workflow_run_id}] Captured Vonage cost info from webhook"
-                    )
-            except Exception as e:
-                logger.error(
-                    f"[run {workflow_run_id}] Failed to capture Vonage cost from webhook: {e}"
-                )
-
     # Get workflow and provider
     workflow = await db_client.get_workflow_by_id(workflow_run.workflow_id)
     if not workflow:

api/services/telephony/status_processor.py

@@ -114,11 +114,13 @@ class StatusCallbackRequest(BaseModel):
             "NOANSWER": "no-answer",
         }
 
-        disposition = data.get("disposition", "")
+        disposition = data.get("disposition") or ""
         status = disposition_map.get(disposition.upper(), disposition.lower())
+        session = data.get("session")
+        call_id = session.get("token") if isinstance(session, dict) else ""
 
         return cls(
-            call_id=data.get("session").get("token"),
+            call_id=call_id or "",
             status=status,
             from_number=data.get("from"),
             to_number=data.get("to"),

api/services/workflow/pipecat_engine.py

@@ -35,6 +35,7 @@ import asyncio
 
 from loguru import logger
 
+from api.services.managed_model_services import MPS_CORRELATION_ID_CONTEXT_KEY
 from api.services.workflow import pipecat_engine_callbacks as engine_callbacks
 from api.services.workflow.mcp_tool_session import McpToolSession
 from api.services.workflow.pipecat_engine_context_composer import (
@@ -382,6 +383,9 @@ class PipecatEngine:
                     embeddings_provider=self._embeddings_provider,
                     embeddings_endpoint=self._embeddings_endpoint,
                     embeddings_api_version=self._embeddings_api_version,
+                    correlation_id=self._call_context_vars.get(
+                        MPS_CORRELATION_ID_CONTEXT_KEY
+                    ),
                     tracing_context=self._get_otel_context(),
                 )
 

api/services/workflow/qa/llm_config.py

@@ -2,7 +2,6 @@
 
 import random
 
-from api.db import db_client
 from api.db.models import WorkflowRunModel
 from api.services.workflow.dto import QANodeData
 
@@ -43,7 +42,7 @@ async def resolve_llm_config(
 async def resolve_user_llm_config(
     workflow_run: WorkflowRunModel,
 ) -> tuple[str, str, str, dict]:
-    """Resolve the user's configured LLM (from UserConfiguration).
+    """Resolve the user's configured LLM (from EffectiveAIModelConfiguration).
 
     Returns:
         (provider, model, api_key, service_kwargs) tuple
@@ -54,7 +53,27 @@ async def resolve_user_llm_config(
 
     llm_config: dict = {}
     if user_id:
-        user_configuration = await db_client.get_user_configurations(user_id)
+        from api.services.configuration.ai_model_configuration import (
+            get_effective_ai_model_configuration_for_workflow,
+        )
+
+        workflow_configurations = {}
+        if workflow_run.definition:
+            workflow_configurations = (
+                workflow_run.definition.workflow_configurations or {}
+            )
+        elif workflow_run.workflow:
+            workflow_configurations = (
+                workflow_run.workflow.workflow_configurations or {}
+            )
+
+        user_configuration = await get_effective_ai_model_configuration_for_workflow(
+            user_id=user_id,
+            organization_id=workflow_run.workflow.organization_id
+            if workflow_run.workflow
+            else None,
+            workflow_configurations=workflow_configurations,
+        )
         llm_config = user_configuration.model_dump(exclude_none=True).get("llm", {})
 
     provider = llm_config.get("provider", "openai")

api/services/workflow/run_usage_response.py

@@ -0,0 +1,41 @@
+"""Format workflow run usage for public API responses."""
+
+
+def format_public_usage_info(usage_info: dict | None) -> dict | None:
+    if not usage_info:
+        return None
+
+    return {
+        "llm": usage_info.get("llm") or {},
+        "tts": usage_info.get("tts") or {},
+        "stt": usage_info.get("stt") or {},
+        "call_duration_seconds": usage_info.get("call_duration_seconds"),
+    }
+
+
+def format_public_cost_info(
+    cost_info: dict | None, usage_info: dict | None
+) -> dict | None:
+    """Return the legacy response shape without doing local cost accounting."""
+    duration = None
+    if usage_info and usage_info.get("call_duration_seconds") is not None:
+        duration = int(round(usage_info.get("call_duration_seconds") or 0))
+    elif cost_info and cost_info.get("call_duration_seconds") is not None:
+        duration = int(round(cost_info.get("call_duration_seconds") or 0))
+
+    dograh_token_usage = 0
+    if cost_info:
+        if "dograh_token_usage" in cost_info:
+            dograh_token_usage = cost_info.get("dograh_token_usage") or 0
+        elif "total_cost_usd" in cost_info:
+            dograh_token_usage = round(
+                float(cost_info.get("total_cost_usd", 0)) * 100, 2
+            )
+
+    if duration is None and dograh_token_usage == 0:
+        return None
+
+    return {
+        "dograh_token_usage": dograh_token_usage,
+        "call_duration_seconds": duration,
+    }

api/services/workflow/text_chat_runner.py

@@ -32,7 +32,6 @@ from pipecat.utils.run_context import set_current_org_id
 
 from api.db import db_client
 from api.enums import WorkflowRunMode, WorkflowRunState
-from api.services.configuration.resolve import resolve_effective_config
 from api.services.pipecat.audio_config import create_audio_config
 from api.services.pipecat.pipeline_builder import create_pipeline_task
 from api.services.pipecat.pipeline_metrics_aggregator import (
@@ -410,14 +409,31 @@ async def execute_text_chat_pending_turn(
     run_definition = workflow_run.definition
     run_configs = run_definition.workflow_configurations or {}
 
-    user_config = await db_client.get_user_configurations(workflow_run.workflow.user.id)
-    user_config = resolve_effective_config(
-        user_config, run_configs.get("model_overrides")
+    from api.services.configuration.ai_model_configuration import (
+        get_effective_ai_model_configuration_for_workflow,
+    )
+
+    user_config = await get_effective_ai_model_configuration_for_workflow(
+        user_id=workflow_run.workflow.user.id,
+        organization_id=workflow.organization_id,
+        workflow_configurations=run_configs,
     )
     if user_config.llm is None:
         raise ValueError("Text chat requires an LLM configuration")
 
-    llm = create_llm_service(user_config)
+    from api.services.managed_model_services import (
+        MPS_CORRELATION_ID_CONTEXT_KEY,
+        ensure_mps_correlation_id,
+    )
+
+    base_initial_context = dict(workflow_run.initial_context or {})
+    mps_correlation_id = await ensure_mps_correlation_id(
+        ai_model_config=user_config,
+        workflow_run_id=workflow_run_id,
+        initial_context=base_initial_context,
+    )
+
+    llm = create_llm_service(user_config, correlation_id=mps_correlation_id)
     inference_llm = llm
 
     runtime_configuration = {
@@ -425,9 +441,15 @@ async def execute_text_chat_pending_turn(
         "llm_model": user_config.llm.model,
     }
     initial_context = {
-        **(workflow_run.initial_context or {}),
+        **base_initial_context,
         "runtime_configuration": runtime_configuration,
     }
+    if mps_correlation_id:
+        initial_context[MPS_CORRELATION_ID_CONTEXT_KEY] = mps_correlation_id
+    await db_client.update_workflow_run(
+        workflow_run_id,
+        initial_context=initial_context,
+    )
 
     workflow_graph = WorkflowGraph(
         ReactFlowDTO.model_validate(run_definition.workflow_json)
@@ -466,9 +488,17 @@ async def execute_text_chat_pending_turn(
     embeddings_model = None
     embeddings_base_url = None
     if user_config.embeddings:
+        from api.services.configuration.ai_model_configuration import (
+            apply_managed_embeddings_base_url,
+        )
+
         embeddings_api_key = user_config.embeddings.api_key
         embeddings_model = user_config.embeddings.model
-        embeddings_base_url = getattr(user_config.embeddings, "base_url", None)
+        embeddings_provider = getattr(user_config.embeddings, "provider", None)
+        embeddings_base_url = apply_managed_embeddings_base_url(
+            provider=embeddings_provider,
+            base_url=getattr(user_config.embeddings, "base_url", None),
+        )
 
     has_recordings = await db_client.has_active_recordings(workflow.organization_id)
     context_compaction_enabled = (workflow.workflow_configurations or {}).get(
@@ -606,8 +636,10 @@ async def execute_text_chat_pending_turn(
                 "Transportless text chat pipeline failed while closing run {}",
                 workflow_run_id,
             )
+            await engine.close_mcp_sessions()
             await engine.cleanup()
             raise
+        await engine.close_mcp_sessions()
         await engine.cleanup()
 
     gathered_context = await engine.get_gathered_context()

api/services/workflow/text_chat_session_service.py

@@ -4,17 +4,11 @@ from datetime import UTC, datetime
 from typing import Any
 from uuid import uuid4
 
-from loguru import logger
-
 from api.db import db_client
 from api.db.models import WorkflowRunTextSessionModel
 from api.db.workflow_run_text_session_client import (
     WorkflowRunTextSessionRevisionConflictError,
 )
-from api.services.pricing.workflow_run_cost import (
-    apply_usage_delta_to_organization,
-    build_workflow_run_cost_info,
-)
 from api.services.workflow.text_chat_logs import (
     build_text_chat_realtime_feedback_events,
 )
@@ -261,20 +255,6 @@ async def execute_pending_text_chat_turn(
         state=execution.state,
         is_completed=execution.is_completed,
     )
-    workflow_run = await db_client.get_workflow_run_by_id(run_id)
-    if workflow_run:
-        try:
-            # Apply the per-turn delta so org usage tracks cumulative run cost
-            # without replaying the full session totals on every turn.
-            await apply_usage_delta_to_organization(workflow_run, execution.usage)
-        except Exception as e:
-            logger.error(
-                f"Failed to update organization usage for text chat run {run_id}: {e}"
-            )
-
-        cost_info = await build_workflow_run_cost_info(workflow_run)
-        if cost_info is not None:
-            await db_client.update_workflow_run(run_id, cost_info=cost_info)
 
     return await _reload_text_chat_session(run_id)
 

api/services/workflow/tools/knowledge_base.py

@@ -29,6 +29,7 @@ async def retrieve_from_knowledge_base(
     embeddings_provider: Optional[str] = None,
     embeddings_endpoint: Optional[str] = None,
     embeddings_api_version: Optional[str] = None,
+    correlation_id: Optional[str] = None,
     tracing_context=None,
 ) -> Dict[str, Any]:
     """Retrieve relevant information from the knowledge base using vector similarity search.
@@ -75,6 +76,7 @@ async def retrieve_from_knowledge_base(
                 embeddings_provider,
                 embeddings_endpoint,
                 embeddings_api_version,
+                correlation_id,
             )
 
         # Create span with parent context
@@ -115,6 +117,7 @@ async def retrieve_from_knowledge_base(
                         embeddings_provider,
                         embeddings_endpoint,
                         embeddings_api_version,
+                        correlation_id,
                     )
 
                     # Add result metadata to span
@@ -192,6 +195,7 @@ async def retrieve_from_knowledge_base(
                 embeddings_provider,
                 embeddings_endpoint,
                 embeddings_api_version,
+                correlation_id,
             )
     else:
         # Tracing is disabled - perform retrieval without tracing
@@ -206,6 +210,7 @@ async def retrieve_from_knowledge_base(
             embeddings_provider,
             embeddings_endpoint,
             embeddings_api_version,
+            correlation_id,
         )
 
 
@@ -220,6 +225,7 @@ async def _perform_retrieval(
     embeddings_provider: Optional[str] = None,
     embeddings_endpoint: Optional[str] = None,
     embeddings_api_version: Optional[str] = None,
+    correlation_id: Optional[str] = None,
 ) -> Dict[str, Any]:
     """Internal function to perform the actual retrieval operation.
 
@@ -272,11 +278,20 @@ async def _perform_retrieval(
                     api_version=embeddings_api_version or "2024-02-15-preview",
                 )
             else:
+                default_headers = None
+                if (
+                    embeddings_provider == ServiceProviders.DOGRAH.value
+                    and correlation_id
+                ):
+                    default_headers = {
+                        "X-Dograh-Correlation-Id": correlation_id,
+                    }
                 embedding_service = OpenAIEmbeddingService(
                     db_client=db_client,
                     api_key=embeddings_api_key,
                     model_id=embeddings_model or "text-embedding-3-small",
                     base_url=embeddings_base_url,
+                    default_headers=default_headers,
                 )
 
             results = await embedding_service.search_similar_chunks(

api/services/workflow_run_billing.py

@@ -0,0 +1,111 @@
+"""Workflow-run billing hooks.
+
+Dograh does not rate or deduct credits locally. MPS owns credit accounting.
+For hosted deployments, Dograh reports completed platform usage to MPS.
+When a server-minted MPS correlation id exists, MPS uses model-service usage
+as the canonical duration. Otherwise Dograh reports the completed run duration.
+"""
+
+from typing import Any
+
+from loguru import logger
+
+from api.constants import DEPLOYMENT_MODE
+from api.db import db_client
+from api.services.managed_model_services import get_mps_correlation_id
+from api.services.mps_service_key_client import mps_service_key_client
+
+
+def _workflow_run_organization_id(workflow_run) -> int | None:
+    workflow = getattr(workflow_run, "workflow", None)
+    return getattr(workflow, "organization_id", None)
+
+
+def _duration_seconds_from_usage_info(workflow_run) -> float | None:
+    usage_info: dict[str, Any] = getattr(workflow_run, "usage_info", None) or {}
+    duration = usage_info.get("call_duration_seconds")
+    try:
+        duration_seconds = float(duration)
+    except (TypeError, ValueError):
+        return None
+
+    return duration_seconds if duration_seconds > 0 else None
+
+
+async def _organization_uses_mps_billing_v2(organization_id: int) -> bool:
+    account = await mps_service_key_client.get_billing_account_status(
+        organization_id=organization_id
+    )
+    return bool(account and account.get("billing_mode") == "v2")
+
+
+async def report_workflow_run_platform_usage(workflow_run) -> None:
+    """Report hosted platform usage for a completed workflow run to MPS."""
+    if DEPLOYMENT_MODE == "oss":
+        return
+
+    if not getattr(workflow_run, "is_completed", False):
+        return
+
+    organization_id = _workflow_run_organization_id(workflow_run)
+    if organization_id is None:
+        logger.warning(
+            "Skipping platform usage report for workflow run {}: no organization_id",
+            workflow_run.id,
+        )
+        return
+
+    correlation_id = get_mps_correlation_id(
+        getattr(workflow_run, "initial_context", None)
+    )
+    duration_seconds = (
+        None if correlation_id else _duration_seconds_from_usage_info(workflow_run)
+    )
+    if not correlation_id and duration_seconds is None:
+        logger.warning(
+            "Skipping platform usage report for workflow run {}: no billable duration",
+            workflow_run.id,
+        )
+        return
+
+    try:
+        if not await _organization_uses_mps_billing_v2(organization_id):
+            return
+
+        result = await mps_service_key_client.report_platform_usage(
+            organization_id=organization_id,
+            correlation_id=correlation_id,
+            duration_seconds=duration_seconds,
+            workflow_run_id=workflow_run.id,
+            metadata={
+                "source": "workflow_run_completion",
+                "workflow_id": getattr(workflow_run, "workflow_id", None),
+                "duration_source": (
+                    "mps_correlation" if correlation_id else "dograh_usage_info"
+                ),
+            },
+        )
+        logger.info(
+            "Reported platform usage for workflow run {} to MPS: {}",
+            workflow_run.id,
+            result,
+        )
+    except Exception as e:
+        logger.error(
+            "Failed to report platform usage for workflow run {}: {}",
+            workflow_run.id,
+            e,
+        )
+
+
+async def report_completed_workflow_run_platform_usage(workflow_run_id: int) -> None:
+    """Load a completed workflow run and report platform usage to MPS."""
+    workflow_run = await db_client.get_workflow_run_by_id(workflow_run_id)
+    if not workflow_run:
+        logger.warning(
+            "Skipping platform usage report: workflow run {} not found",
+            workflow_run_id,
+        )
+        return
+
+    await report_workflow_run_platform_usage(workflow_run)

api/tasks/arq.py

@@ -45,10 +45,8 @@ from api.tasks.campaign_tasks import (
 )
 from api.tasks.knowledge_base_processing import process_knowledge_base_document
 from api.tasks.run_integrations import run_integrations_post_workflow_run
-from api.tasks.s3_upload import (
-    process_workflow_completion,
-    upload_voicemail_audio_to_s3,
-)
+from api.tasks.s3_upload import upload_voicemail_audio_to_s3
+from api.tasks.workflow_completion import process_workflow_completion
 
 
 class WorkerSettings:

api/tasks/knowledge_base_processing.py

@@ -157,15 +157,31 @@ async def process_knowledge_base_document(
         embeddings_endpoint = None
         embeddings_api_version = None
         if document.created_by:
-            user_config = await db_client.get_user_configurations(document.created_by)
-            if user_config.embeddings:
-                embeddings_provider = getattr(user_config.embeddings, "provider", None)
-                embeddings_api_key = user_config.embeddings.api_key
-                embeddings_model = user_config.embeddings.model
-                embeddings_base_url = getattr(user_config.embeddings, "base_url", None)
-                embeddings_endpoint = getattr(user_config.embeddings, "endpoint", None)
+            from api.services.configuration.ai_model_configuration import (
+                apply_managed_embeddings_base_url,
+                get_resolved_ai_model_configuration,
+            )
+
+            resolved_config = await get_resolved_ai_model_configuration(
+                user_id=document.created_by,
+                organization_id=document.organization_id,
+            )
+            effective_config = resolved_config.effective
+            if effective_config.embeddings:
+                embeddings_provider = getattr(
+                    effective_config.embeddings, "provider", None
+                )
+                embeddings_api_key = effective_config.embeddings.api_key
+                embeddings_model = effective_config.embeddings.model
+                embeddings_base_url = apply_managed_embeddings_base_url(
+                    provider=embeddings_provider,
+                    base_url=getattr(effective_config.embeddings, "base_url", None),
+                )
+                embeddings_endpoint = getattr(
+                    effective_config.embeddings, "endpoint", None
+                )
                 embeddings_api_version = getattr(
-                    user_config.embeddings, "api_version", None
+                    effective_config.embeddings, "api_version", None
                 )
                 logger.info(
                     f"Using user embeddings config: provider={embeddings_provider}, "

api/tasks/s3_upload.py

@@ -1,13 +1,9 @@
 import os
-from typing import Optional
 
 from loguru import logger
 from pipecat.utils.run_context import set_current_run_id
 
-from api.db import db_client
-from api.services.pricing.workflow_run_cost import calculate_workflow_run_cost
-from api.services.storage import get_current_storage_backend, storage_fs
-from api.tasks.run_integrations import run_integrations_post_workflow_run
+from api.services.storage import storage_fs
 
 
 async def upload_voicemail_audio_to_s3(
@@ -69,110 +65,3 @@ async def upload_voicemail_audio_to_s3(
                 logger.warning(
                     f"Failed to clean up temp voicemail audio file {temp_file_path}: {e}"
                 )
-
-
-async def process_workflow_completion(
-    _ctx,
-    workflow_run_id: int,
-    audio_temp_path: Optional[str] = None,
-    transcript_temp_path: Optional[str] = None,
-):
-    """Process workflow completion: upload artifacts and run integrations.
-
-    This task combines audio upload, transcript upload, and webhook integrations
-    into a single sequential task to ensure integrations run after uploads complete.
-
-    Args:
-        _ctx: ARQ context (unused)
-        workflow_run_id: The workflow run ID
-        audio_temp_path: Optional path to temp audio file
-        transcript_temp_path: Optional path to temp transcript file
-    """
-    run_id = str(workflow_run_id)
-    set_current_run_id(run_id)
-
-    logger.info(f"Processing workflow completion for run {workflow_run_id}")
-
-    storage_backend = get_current_storage_backend()
-
-    # Step 1: Upload audio if provided
-    if audio_temp_path:
-        try:
-            if os.path.exists(audio_temp_path):
-                file_size = os.path.getsize(audio_temp_path)
-                logger.debug(f"Audio file size: {file_size} bytes")
-
-                recording_url = f"recordings/{workflow_run_id}.wav"
-                logger.info(
-                    f"Uploading audio to {storage_backend.name} - workflow_run_id: {workflow_run_id}"
-                )
-
-                await storage_fs.aupload_file(audio_temp_path, recording_url)
-                await db_client.update_workflow_run(
-                    run_id=workflow_run_id,
-                    recording_url=recording_url,
-                    storage_backend=storage_backend.value,
-                )
-                logger.info(f"Successfully uploaded audio: {recording_url}")
-            else:
-                logger.warning(f"Audio temp file not found: {audio_temp_path}")
-        except Exception as e:
-            logger.error(f"Error uploading audio for workflow {workflow_run_id}: {e}")
-        finally:
-            if audio_temp_path and os.path.exists(audio_temp_path):
-                try:
-                    os.remove(audio_temp_path)
-                    logger.debug(f"Cleaned up temp audio file: {audio_temp_path}")
-                except Exception as e:
-                    logger.warning(f"Failed to clean up temp audio file: {e}")
-
-    # Step 2: Upload transcript if provided
-    if transcript_temp_path:
-        try:
-            if os.path.exists(transcript_temp_path):
-                file_size = os.path.getsize(transcript_temp_path)
-                logger.debug(f"Transcript file size: {file_size} bytes")
-
-                transcript_url = f"transcripts/{workflow_run_id}.txt"
-                logger.info(
-                    f"Uploading transcript to {storage_backend.name} - workflow_run_id: {workflow_run_id}"
-                )
-
-                await storage_fs.aupload_file(transcript_temp_path, transcript_url)
-                await db_client.update_workflow_run(
-                    run_id=workflow_run_id,
-                    transcript_url=transcript_url,
-                    storage_backend=storage_backend.value,
-                )
-                logger.info(f"Successfully uploaded transcript: {transcript_url}")
-            else:
-                logger.warning(
-                    f"Transcript temp file not found: {transcript_temp_path}"
-                )
-        except Exception as e:
-            logger.error(
-                f"Error uploading transcript for workflow {workflow_run_id}: {e}"
-            )
-        finally:
-            if transcript_temp_path and os.path.exists(transcript_temp_path):
-                try:
-                    os.remove(transcript_temp_path)
-                    logger.debug(
-                        f"Cleaned up temp transcript file: {transcript_temp_path}"
-                    )
-                except Exception as e:
-                    logger.warning(f"Failed to clean up temp transcript file: {e}")
-
-    # Step 3: Run integrations including QA analysis (after uploads are complete)
-    try:
-        await run_integrations_post_workflow_run(_ctx, workflow_run_id)
-    except Exception as e:
-        logger.error(f"Error running integrations for workflow {workflow_run_id}: {e}")
-
-    # Step 4: Calculate cost after integrations (so QA token usage is included)
-    try:
-        await calculate_workflow_run_cost(workflow_run_id)
-    except Exception as e:
-        logger.error(f"Error calculating cost for workflow {workflow_run_id}: {e}")
-
-    logger.info(f"Completed workflow completion processing for run {workflow_run_id}")

api/tasks/workflow_completion.py

@@ -0,0 +1,121 @@
+import os
+from typing import Optional
+
+from loguru import logger
+from pipecat.utils.run_context import set_current_run_id
+
+from api.db import db_client
+from api.services.storage import get_current_storage_backend, storage_fs
+from api.services.workflow_run_billing import (
+    report_completed_workflow_run_platform_usage,
+)
+from api.tasks.run_integrations import run_integrations_post_workflow_run
+
+
+async def process_workflow_completion(
+    _ctx,
+    workflow_run_id: int,
+    audio_temp_path: Optional[str] = None,
+    transcript_temp_path: Optional[str] = None,
+):
+    """Process workflow completion: upload artifacts and run integrations.
+
+    This task combines audio upload, transcript upload, and webhook integrations
+    into a single sequential task to ensure integrations run after uploads complete.
+
+    Args:
+        _ctx: ARQ context (unused)
+        workflow_run_id: The workflow run ID
+        audio_temp_path: Optional path to temp audio file
+        transcript_temp_path: Optional path to temp transcript file
+    """
+    run_id = str(workflow_run_id)
+    set_current_run_id(run_id)
+
+    logger.info(f"Processing workflow completion for run {workflow_run_id}")
+
+    storage_backend = get_current_storage_backend()
+
+    # Step 1: Upload audio if provided
+    if audio_temp_path:
+        try:
+            if os.path.exists(audio_temp_path):
+                file_size = os.path.getsize(audio_temp_path)
+                logger.debug(f"Audio file size: {file_size} bytes")
+
+                recording_url = f"recordings/{workflow_run_id}.wav"
+                logger.info(
+                    f"Uploading audio to {storage_backend.name} - workflow_run_id: {workflow_run_id}"
+                )
+
+                await storage_fs.aupload_file(audio_temp_path, recording_url)
+                await db_client.update_workflow_run(
+                    run_id=workflow_run_id,
+                    recording_url=recording_url,
+                    storage_backend=storage_backend.value,
+                )
+                logger.info(f"Successfully uploaded audio: {recording_url}")
+            else:
+                logger.warning(f"Audio temp file not found: {audio_temp_path}")
+        except Exception as e:
+            logger.error(f"Error uploading audio for workflow {workflow_run_id}: {e}")
+        finally:
+            if audio_temp_path and os.path.exists(audio_temp_path):
+                try:
+                    os.remove(audio_temp_path)
+                    logger.debug(f"Cleaned up temp audio file: {audio_temp_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to clean up temp audio file: {e}")
+
+    # Step 2: Upload transcript if provided
+    if transcript_temp_path:
+        try:
+            if os.path.exists(transcript_temp_path):
+                file_size = os.path.getsize(transcript_temp_path)
+                logger.debug(f"Transcript file size: {file_size} bytes")
+
+                transcript_url = f"transcripts/{workflow_run_id}.txt"
+                logger.info(
+                    f"Uploading transcript to {storage_backend.name} - workflow_run_id: {workflow_run_id}"
+                )
+
+                await storage_fs.aupload_file(transcript_temp_path, transcript_url)
+                await db_client.update_workflow_run(
+                    run_id=workflow_run_id,
+                    transcript_url=transcript_url,
+                    storage_backend=storage_backend.value,
+                )
+                logger.info(f"Successfully uploaded transcript: {transcript_url}")
+            else:
+                logger.warning(
+                    f"Transcript temp file not found: {transcript_temp_path}"
+                )
+        except Exception as e:
+            logger.error(
+                f"Error uploading transcript for workflow {workflow_run_id}: {e}"
+            )
+        finally:
+            if transcript_temp_path and os.path.exists(transcript_temp_path):
+                try:
+                    os.remove(transcript_temp_path)
+                    logger.debug(
+                        f"Cleaned up temp transcript file: {transcript_temp_path}"
+                    )
+                except Exception as e:
+                    logger.warning(f"Failed to clean up temp transcript file: {e}")
+
+    # Step 3: Run integrations including QA analysis (after uploads are complete)
+    try:
+        await run_integrations_post_workflow_run(_ctx, workflow_run_id)
+    except Exception as e:
+        logger.error(f"Error running integrations for workflow {workflow_run_id}: {e}")
+
+    # Step 4: Notify MPS after completion. MPS owns credit accounting.
+    try:
+        await report_completed_workflow_run_platform_usage(workflow_run_id)
+    except Exception as e:
+        logger.error(
+            f"Error reporting platform usage for workflow {workflow_run_id}: {e}"
+        )
+
+    logger.info(f"Completed workflow completion processing for run {workflow_run_id}")

api/tests/integrations/_run_pipeline_helpers.py

@@ -203,7 +203,7 @@ async def create_workflow_run_rows(
     Returns:
         Tuple of (workflow_run, user, workflow).
     """
-    from api.schemas.user_configuration import UserConfiguration
+    from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 
     org = OrganizationModel(provider_id=f"test-org-{provider_id_suffix}")
     async_session.add(org)
@@ -218,7 +218,7 @@ async def create_workflow_run_rows(
 
     await db_session.update_user_configuration(
         user_id=user.id,
-        configuration=UserConfiguration.model_validate(USER_CONFIGURATION),
+        configuration=EffectiveAIModelConfiguration.model_validate(USER_CONFIGURATION),
     )
 
     workflow = await db_session.create_workflow(

api/tests/telephony/cloudonix/test_routes.py

@@ -0,0 +1,119 @@
+"""Regression tests for Cloudonix CDR webhook handling.
+
+A Cloudonix CDR webhook is a public, unauthenticated endpoint that parses
+arbitrary external JSON. A partial / malformed payload (missing ``session``,
+or a ``null`` ``session`` / ``disposition``) must produce a graceful error
+response, not an unhandled ``AttributeError`` (HTTP 500).
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+from starlette.requests import Request
+
+from api.services.telephony.providers.cloudonix.routes import handle_cloudonix_cdr
+from api.services.telephony.status_processor import StatusCallbackRequest
+
+
+def _json_request(body: bytes) -> Request:
+    async def receive():
+        return {"type": "http.request", "body": body, "more_body": False}
+
+    return Request(
+        {
+            "type": "http",
+            "method": "POST",
+            "scheme": "https",
+            "server": ("example.test", 443),
+            "path": "/api/v1/telephony/cloudonix/cdr",
+            "query_string": b"",
+            "headers": [(b"content-type", b"application/json")],
+        },
+        receive,
+    )
+
+
+@pytest.mark.asyncio
+async def test_cdr_route_handles_payload_without_session():
+    """A CDR payload missing the ``session`` object returns a graceful error
+    instead of raising ``AttributeError`` on ``None.get("token")``."""
+    request = _json_request(b'{"domain": "acme.cloudonix.io", "disposition": "ANSWER"}')
+
+    with patch(
+        "api.services.telephony.providers.cloudonix.routes.db_client"
+    ) as db_client:
+        db_client.get_workflow_run_by_call_id = AsyncMock(return_value=None)
+
+        result = await handle_cloudonix_cdr(request)
+
+    assert result == {"status": "error", "message": "Missing call_id field"}
+
+
+@pytest.mark.asyncio
+async def test_cdr_route_handles_null_session():
+    """A CDR payload with an explicit ``null`` session is handled gracefully."""
+    request = _json_request(b'{"domain": "acme.cloudonix.io", "session": null}')
+
+    with patch(
+        "api.services.telephony.providers.cloudonix.routes.db_client"
+    ) as db_client:
+        db_client.get_workflow_run_by_call_id = AsyncMock(return_value=None)
+
+        result = await handle_cloudonix_cdr(request)
+
+    assert result == {"status": "error", "message": "Missing call_id field"}
+
+
+@pytest.mark.asyncio
+async def test_cdr_route_handles_string_session():
+    """A CDR payload with a non-object session is handled gracefully."""
+    request = _json_request(b'{"domain": "acme.cloudonix.io", "session": "abc"}')
+
+    with patch(
+        "api.services.telephony.providers.cloudonix.routes.db_client"
+    ) as db_client:
+        db_client.get_workflow_run_by_call_id = AsyncMock(return_value=None)
+
+        result = await handle_cloudonix_cdr(request)
+
+    assert result == {"status": "error", "message": "Missing call_id field"}
+
+
+def test_from_cloudonix_cdr_tolerates_missing_session_and_disposition():
+    """``from_cloudonix_cdr`` must not crash on a partial CDR payload."""
+    # Missing both session and disposition.
+    req = StatusCallbackRequest.from_cloudonix_cdr({"domain": "acme.cloudonix.io"})
+    assert req.call_id == ""
+    assert req.status == ""
+
+    # Explicit null values.
+    req = StatusCallbackRequest.from_cloudonix_cdr(
+        {"session": None, "disposition": None}
+    )
+    assert req.call_id == ""
+    assert req.status == ""
+
+
+def test_from_cloudonix_cdr_tolerates_string_session():
+    """``from_cloudonix_cdr`` treats a non-object session as missing call_id."""
+    req = StatusCallbackRequest.from_cloudonix_cdr(
+        {"session": "abc", "disposition": "ANSWER"}
+    )
+    assert req.call_id == ""
+    assert req.status == "completed"
+
+
+def test_from_cloudonix_cdr_maps_disposition_and_session_token():
+    """Normal, well-formed CDR payloads still map correctly."""
+    req = StatusCallbackRequest.from_cloudonix_cdr(
+        {
+            "session": {"token": "abc123"},
+            "disposition": "BUSY",
+            "from": "+15551230001",
+            "to": "+15551230002",
+            "billsec": 12,
+        }
+    )
+    assert req.call_id == "abc123"
+    assert req.status == "busy"
+    assert req.duration == "12"

api/tests/telephony/vobiz/test_routes.py

@@ -6,11 +6,13 @@ from unittest.mock import AsyncMock, patch
 from urllib.parse import urlencode
 
 import pytest
+from fastapi import HTTPException
 from starlette.requests import Request
 
 from api.services.telephony.providers.vobiz.provider import VobizProvider
 from api.services.telephony.providers.vobiz.routes import (
     handle_vobiz_hangup_callback,
+    handle_vobiz_hangup_callback_by_workflow,
     handle_vobiz_ring_callback,
 )
 
@@ -225,3 +227,154 @@ async def test_vobiz_verify_inbound_signature_rejects_missing_signature():
         {},
         {},
     )
+
+
+@pytest.mark.asyncio
+async def test_vobiz_hangup_callback_rejects_missing_signature():
+    """An unsigned hangup callback must be rejected before status processing."""
+    provider = _provider()
+    form_data = {
+        "CallUUID": "call-123",
+        "CallStatus": "completed",
+        "From": "15551230001",
+        "To": "15551230002",
+        "Direction": "outbound",
+        "Duration": "12",
+    }
+    # No x-vobiz-signature-* headers — the callback is unsigned.
+    request = _request(
+        path="/api/v1/telephony/vobiz/hangup-callback/123",
+        form_data=form_data,
+    )
+
+    with (
+        patch("api.services.telephony.providers.vobiz.routes.db_client") as db_client,
+        patch(
+            "api.services.telephony.providers.vobiz.routes.get_telephony_provider_for_run",
+            new_callable=AsyncMock,
+            return_value=provider,
+        ),
+        patch(
+            "api.services.telephony.providers.vobiz.routes.get_backend_endpoints",
+            new_callable=AsyncMock,
+            return_value=("https://example.test", "wss://example.test"),
+        ),
+        patch(
+            "api.services.telephony.providers.vobiz.routes._process_status_update",
+            new_callable=AsyncMock,
+        ) as process_status,
+    ):
+        db_client.get_workflow_run_by_id = AsyncMock(
+            return_value=SimpleNamespace(workflow_id=7)
+        )
+        db_client.get_workflow_by_id = AsyncMock(
+            return_value=SimpleNamespace(organization_id=11)
+        )
+
+        with pytest.raises(HTTPException) as exc_info:
+            await handle_vobiz_hangup_callback(
+                workflow_run_id=123,
+                request=request,
+            )
+
+    assert exc_info.value.status_code == 403
+    process_status.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_vobiz_ring_callback_rejects_missing_signature():
+    """An unsigned ring callback must be rejected before it is logged."""
+    provider = _provider()
+    form_data = {
+        "CallUUID": "call-123",
+        "CallStatus": "ringing",
+        "From": "15551230001",
+        "To": "15551230002",
+    }
+    # No x-vobiz-signature-* headers — the callback is unsigned.
+    request = _request(
+        path="/api/v1/telephony/vobiz/ring-callback/123",
+        form_data=form_data,
+    )
+
+    workflow_run = SimpleNamespace(workflow_id=7, logs={})
+
+    with (
+        patch("api.services.telephony.providers.vobiz.routes.db_client") as db_client,
+        patch(
+            "api.services.telephony.providers.vobiz.routes.get_telephony_provider_for_run",
+            new_callable=AsyncMock,
+            return_value=provider,
+        ),
+        patch(
+            "api.services.telephony.providers.vobiz.routes.get_backend_endpoints",
+            new_callable=AsyncMock,
+            return_value=("https://example.test", "wss://example.test"),
+        ),
+    ):
+        db_client.get_workflow_run_by_id = AsyncMock(return_value=workflow_run)
+        db_client.get_workflow_by_id = AsyncMock(
+            return_value=SimpleNamespace(organization_id=11)
+        )
+        db_client.update_workflow_run = AsyncMock()
+
+        with pytest.raises(HTTPException) as exc_info:
+            await handle_vobiz_ring_callback(
+                workflow_run_id=123,
+                request=request,
+            )
+
+    assert exc_info.value.status_code == 403
+    db_client.update_workflow_run.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_vobiz_hangup_callback_by_workflow_rejects_missing_signature():
+    """An unsigned by-workflow hangup callback must be rejected before processing."""
+    provider = _provider()
+    form_data = {
+        "CallUUID": "call-123",
+        "CallStatus": "completed",
+        "From": "15551230001",
+        "To": "15551230002",
+        "Direction": "outbound",
+        "Duration": "12",
+    }
+    # No x-vobiz-signature-* headers — the callback is unsigned.
+    request = _request(
+        path="/api/v1/telephony/vobiz/hangup-callback/workflow/7",
+        form_data=form_data,
+    )
+
+    with (
+        patch("api.services.telephony.providers.vobiz.routes.db_client") as db_client,
+        patch(
+            "api.services.telephony.providers.vobiz.routes.get_telephony_provider_for_run",
+            new_callable=AsyncMock,
+            return_value=provider,
+        ),
+        patch(
+            "api.services.telephony.providers.vobiz.routes.get_backend_endpoints",
+            new_callable=AsyncMock,
+            return_value=("https://example.test", "wss://example.test"),
+        ),
+        patch(
+            "api.services.telephony.providers.vobiz.routes._process_status_update",
+            new_callable=AsyncMock,
+        ) as process_status,
+    ):
+        db_client.get_workflow_by_id = AsyncMock(
+            return_value=SimpleNamespace(organization_id=11)
+        )
+        db_client.get_workflow_run_by_call_id = AsyncMock(
+            return_value=SimpleNamespace(id=123, workflow_id=7)
+        )
+
+        with pytest.raises(HTTPException) as exc_info:
+            await handle_vobiz_hangup_callback_by_workflow(
+                workflow_id=7,
+                request=request,
+            )
+
+    assert exc_info.value.status_code == 403
+    process_status.assert_not_awaited()

api/tests/test_ai_model_configuration_v2.py

@@ -0,0 +1,459 @@
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+from pydantic import ValidationError
+
+from api.schemas.ai_model_configuration import (
+    DograhManagedAIModelConfiguration,
+    EffectiveAIModelConfiguration,
+    OrganizationAIModelConfigurationResponse,
+    OrganizationAIModelConfigurationV2,
+    compile_ai_model_configuration_v2,
+)
+from api.services.configuration.ai_model_configuration import (
+    WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY,
+    check_for_masked_keys_in_ai_model_configuration_v2,
+    convert_legacy_ai_model_configuration_to_v2,
+    mask_ai_model_configuration_v2,
+    merge_ai_model_configuration_v2_secrets,
+    migrate_workflow_configuration_model_override_to_v2,
+)
+from api.services.configuration.check_validity import UserConfigurationValidator
+from api.services.configuration.masking import mask_key
+from api.services.configuration.registry import (
+    DeepgramSTTConfiguration,
+    DograhLLMService,
+    DograhSTTService,
+    DograhTTSService,
+    ElevenlabsTTSConfiguration,
+    GoogleLLMService,
+    GoogleRealtimeLLMConfiguration,
+    OpenAIEmbeddingsConfiguration,
+    OpenAILLMService,
+)
+
+
+def test_dograh_v2_compiles_to_effective_managed_pipeline_with_embeddings():
+    config = OrganizationAIModelConfigurationV2(
+        mode="dograh",
+        dograh=DograhManagedAIModelConfiguration(
+            api_key="mps-secret",
+            voice="default",
+            speed=1.2,
+            language="multi",
+        ),
+    )
+
+    effective = compile_ai_model_configuration_v2(config)
+
+    assert effective.is_realtime is False
+    assert effective.llm.provider == "dograh"
+    assert effective.llm.model == "default"
+    assert effective.tts.provider == "dograh"
+    assert effective.tts.speed == 1.2
+    assert effective.stt.provider == "dograh"
+    assert effective.stt.language == "multi"
+    assert effective.embeddings.provider == "dograh"
+    assert effective.embeddings.model == "default"
+    assert effective.managed_service_version == 2
+
+
+def test_dograh_v2_rejects_non_predefined_speed():
+    with pytest.raises(ValidationError):
+        OrganizationAIModelConfigurationV2(
+            mode="dograh",
+            dograh=DograhManagedAIModelConfiguration(
+                api_key="mps-secret",
+                speed=1.5,
+            ),
+        )
+
+
+def test_byok_v2_rejects_dograh_provider():
+    with pytest.raises(ValidationError):
+        OrganizationAIModelConfigurationV2.model_validate(
+            {
+                "mode": "byok",
+                "byok": {
+                    "mode": "pipeline",
+                    "pipeline": {
+                        "llm": {
+                            "provider": "dograh",
+                            "api_key": "mps-secret",
+                            "model": "default",
+                        },
+                        "tts": {
+                            "provider": "dograh",
+                            "api_key": "mps-secret",
+                            "model": "default",
+                            "voice": "default",
+                        },
+                        "stt": {
+                            "provider": "dograh",
+                            "api_key": "mps-secret",
+                            "model": "default",
+                        },
+                    },
+                },
+            }
+        )
+
+
+@pytest.mark.asyncio
+async def test_byok_realtime_validator_does_not_require_stt_or_tts():
+    config = OrganizationAIModelConfigurationV2.model_validate(
+        {
+            "mode": "byok",
+            "byok": {
+                "mode": "realtime",
+                "realtime": {
+                    "realtime": {
+                        "provider": "google_realtime",
+                        "api_key": "google-realtime-key",
+                        "model": "gemini-3.1-flash-live-preview",
+                        "voice": "Puck",
+                        "language": "en",
+                    },
+                    "llm": {
+                        "provider": "google",
+                        "api_key": "google-llm-key",
+                        "model": "gemini-2.0-flash",
+                    },
+                },
+            },
+        }
+    )
+    effective = compile_ai_model_configuration_v2(config)
+
+    assert effective.is_realtime is True
+    assert effective.stt is None
+    assert effective.tts is None
+    assert await UserConfigurationValidator().validate(effective) == {
+        "status": [{"model": "all", "message": "ok"}]
+    }
+
+
+@pytest.mark.asyncio
+async def test_pipeline_validator_requires_stt_and_tts_when_not_realtime():
+    effective = EffectiveAIModelConfiguration(
+        llm=GoogleLLMService(
+            provider="google",
+            api_key="google-llm-key",
+            model="gemini-2.0-flash",
+        ),
+        realtime=GoogleRealtimeLLMConfiguration(
+            provider="google_realtime",
+            api_key="google-realtime-key",
+            model="gemini-3.1-flash-live-preview",
+            voice="Puck",
+            language="en",
+        ),
+        is_realtime=False,
+    )
+
+    with pytest.raises(ValueError) as exc_info:
+        await UserConfigurationValidator().validate(effective)
+
+    assert exc_info.value.args[0] == [
+        {"model": "stt", "message": "API key is missing"},
+        {"model": "tts", "message": "API key is missing"},
+    ]
+
+
+def test_masked_dograh_key_is_preserved_when_saving_same_mode():
+    existing = OrganizationAIModelConfigurationV2(
+        mode="dograh",
+        dograh=DograhManagedAIModelConfiguration(api_key="mps-real-secret"),
+    )
+    incoming = OrganizationAIModelConfigurationV2(
+        mode="dograh",
+        dograh=DograhManagedAIModelConfiguration(api_key=mask_key("mps-real-secret")),
+    )
+
+    merged = merge_ai_model_configuration_v2_secrets(incoming, existing)
+
+    assert merged.dograh.api_key == "mps-real-secret"
+    check_for_masked_keys_in_ai_model_configuration_v2(merged)
+
+
+def test_masked_v2_configuration_masks_nested_service_keys():
+    config = OrganizationAIModelConfigurationV2(
+        mode="byok",
+        byok={
+            "mode": "pipeline",
+            "pipeline": {
+                "llm": {
+                    "provider": "openai",
+                    "api_key": "sk-real-secret",
+                    "model": "gpt-4.1",
+                },
+                "tts": {
+                    "provider": "elevenlabs",
+                    "api_key": "el-real-secret",
+                    "model": "eleven_flash_v2_5",
+                    "voice": "Rachel",
+                },
+                "stt": {
+                    "provider": "deepgram",
+                    "api_key": "dg-real-secret",
+                    "model": "nova-3-general",
+                },
+            },
+        },
+    )
+
+    masked = mask_ai_model_configuration_v2(config)
+
+    assert masked["byok"]["pipeline"]["llm"]["api_key"] == mask_key("sk-real-secret")
+    assert masked["byok"]["pipeline"]["tts"]["api_key"] == mask_key("el-real-secret")
+    assert masked["byok"]["pipeline"]["stt"]["api_key"] == mask_key("dg-real-secret")
+
+
+def test_legacy_all_dograh_pipeline_converts_to_dograh_v2():
+    legacy = EffectiveAIModelConfiguration(
+        llm=DograhLLMService(
+            provider="dograh",
+            api_key=["mps-secret"],
+            model="default",
+        ),
+        tts=DograhTTSService(
+            provider="dograh",
+            api_key=["mps-secret"],
+            model="default",
+            voice="default",
+            speed=1.0,
+        ),
+        stt=DograhSTTService(
+            provider="dograh",
+            api_key=["mps-secret"],
+            model="default",
+            language="multi",
+        ),
+    )
+
+    config = convert_legacy_ai_model_configuration_to_v2(legacy)
+
+    assert config.mode == "dograh"
+    assert config.dograh.api_key == "mps-secret"
+
+
+def test_legacy_mixed_dograh_pipeline_converts_to_dograh_v2():
+    legacy = EffectiveAIModelConfiguration(
+        llm=OpenAILLMService(
+            provider="openai",
+            api_key="sk-llm",
+            model="gpt-4.1",
+        ),
+        tts=DograhTTSService(
+            provider="dograh",
+            api_key="mps-tts",
+            model="default",
+            voice="default",
+        ),
+        stt=DograhSTTService(
+            provider="dograh",
+            api_key="mps-stt",
+            model="default",
+        ),
+        embeddings=OpenAIEmbeddingsConfiguration(
+            provider="openai",
+            api_key="sk-emb",
+            model="text-embedding-3-small",
+        ),
+    )
+
+    config = convert_legacy_ai_model_configuration_to_v2(legacy)
+
+    assert config.mode == "dograh"
+    assert config.dograh.api_key == "mps-tts"
+    assert config.dograh.voice == "default"
+
+
+def test_legacy_byok_pipeline_converts_to_byok_v2():
+    legacy = EffectiveAIModelConfiguration(
+        llm=OpenAILLMService(
+            provider="openai",
+            api_key="sk-llm",
+            model="gpt-4.1",
+        ),
+        tts=ElevenlabsTTSConfiguration(
+            provider="elevenlabs",
+            api_key="el-tts",
+            model="eleven_flash_v2_5",
+            voice="Rachel",
+        ),
+        stt=DeepgramSTTConfiguration(
+            provider="deepgram",
+            api_key="dg-stt",
+            model="nova-3-general",
+        ),
+        embeddings=OpenAIEmbeddingsConfiguration(
+            provider="openai",
+            api_key="sk-emb",
+            model="text-embedding-3-small",
+        ),
+    )
+
+    config = convert_legacy_ai_model_configuration_to_v2(legacy)
+
+    assert config.mode == "byok"
+    assert config.byok.mode == "pipeline"
+    assert config.byok.pipeline.llm.provider == "openai"
+    assert config.byok.pipeline.tts.provider == "elevenlabs"
+
+
+def test_workflow_model_override_migration_removes_v1_override_and_sets_v2():
+    base = EffectiveAIModelConfiguration(
+        llm=OpenAILLMService(
+            provider="openai",
+            api_key="sk-llm",
+            model="gpt-4.1",
+        ),
+        tts=ElevenlabsTTSConfiguration(
+            provider="elevenlabs",
+            api_key="el-tts",
+            model="eleven_flash_v2_5",
+            voice="Rachel",
+        ),
+        stt=DeepgramSTTConfiguration(
+            provider="deepgram",
+            api_key="dg-stt",
+            model="nova-3-general",
+        ),
+    )
+    workflow_configurations = {
+        "ambient_noise_configuration": {"enabled": False},
+        "model_overrides": {
+            "tts": {
+                "provider": "dograh",
+                "api_key": "mps-workflow",
+                "model": "default",
+                "voice": "default",
+            }
+        },
+    }
+
+    migrated, changed = migrate_workflow_configuration_model_override_to_v2(
+        workflow_configurations,
+        base,
+    )
+
+    assert changed is True
+    assert "model_overrides" not in migrated
+    assert migrated["ambient_noise_configuration"] == {"enabled": False}
+    v2_override = migrated[WORKFLOW_MODEL_CONFIGURATION_V2_OVERRIDE_KEY]
+    assert v2_override["mode"] == "dograh"
+    assert v2_override["dograh"]["api_key"] == "mps-workflow"
+
+
+def test_workflow_model_override_migration_removes_invalid_v1_override_marker():
+    base = EffectiveAIModelConfiguration()
+    workflow_configurations = {
+        "ambient_noise_configuration": {"enabled": False},
+        "model_overrides": None,
+    }
+
+    migrated, changed = migrate_workflow_configuration_model_override_to_v2(
+        workflow_configurations,
+        base,
+    )
+
+    assert changed is True
+    assert "model_overrides" not in migrated
+    assert migrated["ambient_noise_configuration"] == {"enabled": False}
+
+
+@pytest.mark.asyncio
+async def test_migrate_model_configuration_v2_initializes_hosted_mps_billing(
+    monkeypatch,
+):
+    from api.routes import organization as organization_routes
+
+    legacy = EffectiveAIModelConfiguration(
+        llm=DograhLLMService(
+            provider="dograh",
+            api_key=["mps-secret"],
+            model="default",
+        ),
+        tts=DograhTTSService(
+            provider="dograh",
+            api_key=["mps-secret"],
+            model="default",
+            voice="default",
+        ),
+        stt=DograhSTTService(
+            provider="dograh",
+            api_key=["mps-secret"],
+            model="default",
+        ),
+    )
+    expected_response = OrganizationAIModelConfigurationResponse(
+        configuration={"version": 2, "mode": "dograh"},
+        effective_configuration={},
+        source="organization_v2",
+    )
+
+    class FakeValidator:
+        async def validate(self, *args, **kwargs):
+            return {"status": [{"model": "all", "message": "ok"}]}
+
+    ensure_billing = AsyncMock(return_value={"billing_mode": "v2"})
+    upsert = AsyncMock()
+    migrate_workflows = AsyncMock()
+
+    monkeypatch.setattr(organization_routes, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        organization_routes,
+        "get_organization_ai_model_configuration_v2",
+        AsyncMock(return_value=None),
+    )
+    monkeypatch.setattr(
+        organization_routes.db_client,
+        "get_user_configurations",
+        AsyncMock(return_value=legacy),
+    )
+    monkeypatch.setattr(
+        organization_routes,
+        "UserConfigurationValidator",
+        lambda: FakeValidator(),
+    )
+    monkeypatch.setattr(
+        organization_routes,
+        "ensure_hosted_mps_billing_account_v2",
+        ensure_billing,
+    )
+    monkeypatch.setattr(
+        organization_routes,
+        "upsert_organization_ai_model_configuration_v2",
+        upsert,
+    )
+    monkeypatch.setattr(
+        organization_routes,
+        "migrate_workflow_model_configurations_to_v2",
+        migrate_workflows,
+    )
+    monkeypatch.setattr(
+        organization_routes,
+        "_model_configuration_v2_response",
+        AsyncMock(return_value=expected_response),
+    )
+
+    user = SimpleNamespace(
+        id=7,
+        provider_id="provider-123",
+        selected_organization_id=42,
+    )
+
+    response = await organization_routes.migrate_model_configuration_v2(
+        force=False,
+        user=user,
+    )
+
+    ensure_billing.assert_awaited_once_with(42, created_by="provider-123")
+    upsert.assert_awaited_once()
+    migrate_workflows.assert_awaited_once_with(
+        organization_id=42,
+        fallback_user_config=legacy,
+    )
+    assert response == expected_response

api/tests/test_auth_depends.py

@@ -0,0 +1,68 @@
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+
+from api.services.auth import depends as auth_depends
+
+
+@pytest.mark.asyncio
+async def test_get_user_initializes_hosted_mps_billing_for_new_org(monkeypatch):
+    stack_user = {
+        "id": "stack-user-1",
+        "selected_team_id": "team-1",
+        "primary_email_verified": False,
+    }
+    user = SimpleNamespace(
+        id=7,
+        email=None,
+        provider_id="stack-user-1",
+        selected_organization_id=None,
+    )
+    organization = SimpleNamespace(id=42)
+    existing_config = SimpleNamespace(llm=object(), tts=None, stt=None)
+
+    ensure_billing = AsyncMock(return_value={"billing_mode": "v2"})
+
+    monkeypatch.setattr(auth_depends, "AUTH_PROVIDER", "stack")
+    monkeypatch.setattr(
+        auth_depends.stackauth,
+        "get_user",
+        AsyncMock(return_value=stack_user),
+    )
+    monkeypatch.setattr(
+        auth_depends.db_client,
+        "get_or_create_user_by_provider_id",
+        AsyncMock(return_value=(user, False)),
+    )
+    monkeypatch.setattr(
+        auth_depends.db_client,
+        "get_or_create_organization_by_provider_id",
+        AsyncMock(return_value=(organization, True)),
+    )
+    monkeypatch.setattr(
+        auth_depends.db_client,
+        "add_user_to_organization",
+        AsyncMock(),
+    )
+    monkeypatch.setattr(
+        auth_depends.db_client,
+        "update_user_selected_organization",
+        AsyncMock(),
+    )
+    monkeypatch.setattr(
+        auth_depends.db_client,
+        "get_user_configurations",
+        AsyncMock(return_value=existing_config),
+    )
+    monkeypatch.setattr(
+        auth_depends,
+        "ensure_hosted_mps_billing_account_v2",
+        ensure_billing,
+    )
+
+    result = await auth_depends.get_user(authorization="Bearer token")
+
+    assert result is user
+    assert result.selected_organization_id == 42
+    ensure_billing.assert_awaited_once_with(42, created_by="stack-user-1")

api/tests/test_cartesia_tts_service_factory.py

@@ -0,0 +1,45 @@
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from api.services.configuration.registry import (
+    CARTESIA_TTS_MODELS,
+    CartesiaTTSConfiguration,
+    ServiceProviders,
+)
+from api.services.pipecat.service_factory import create_tts_service
+
+
+def test_cartesia_tts_configuration_defaults_to_sonic_3_5():
+    config = CartesiaTTSConfiguration(api_key="test-key")
+
+    assert config.provider == ServiceProviders.CARTESIA
+    assert config.model == "sonic-3.5"
+    assert CARTESIA_TTS_MODELS == ["sonic-3.5", "sonic-3"]
+
+
+def test_create_cartesia_tts_service_passes_selected_model():
+    user_config = SimpleNamespace(
+        tts=SimpleNamespace(
+            provider=ServiceProviders.CARTESIA.value,
+            api_key="test-key",
+            model="sonic-3.5",
+            voice="test-voice-id",
+            speed=1.0,
+            volume=1.0,
+        )
+    )
+    audio_config = SimpleNamespace(
+        transport_out_sample_rate=24000,
+        transport_in_sample_rate=16000,
+    )
+
+    with patch(
+        "api.services.pipecat.service_factory.CartesiaTTSService"
+    ) as mock_service:
+        create_tts_service(user_config, audio_config)
+
+    assert mock_service.call_count == 1
+    kwargs = mock_service.call_args.kwargs
+    assert kwargs["api_key"] == "test-key"
+    assert kwargs["settings"].model == "sonic-3.5"
+    assert kwargs["settings"].voice == "test-voice-id"

api/tests/test_cost_calculator.py

@@ -1,31 +0,0 @@
-from api.services.pricing.cost_calculator import cost_calculator
-
-
-def test_cost_calculator():
-    """Test function to verify cost calculation works"""
-    sample_usage = {
-        "llm": {
-            "OpenAILLMService#0|||gpt-4.1-mini": {
-                "prompt_tokens": 45380,
-                "completion_tokens": 496,
-                "total_tokens": 45876,
-                "cache_read_input_tokens": 0,
-                "cache_creation_input_tokens": 0,
-            }
-        },
-        "tts": {"ElevenLabsTTSService#0|||eleven_flash_v2_5": 2399},
-        "stt": {"DeepgramSTTService#0|||nova-3-general": 177.21536946296692},
-        "call_duration_seconds": 179,
-    }
-
-    result = cost_calculator.calculate_total_cost(sample_usage)
-    assert result["llm_cost"] == 45380 * 0.40 / 1_000_000 + 496 * 1.60 / 1_000_000
-    assert result["tts_cost"] == 2399 * 0.0256 / 1_000
-    assert result["stt_cost"] == 177.21536946296692 / 60 * 0.0077
-    assert (
-        abs(
-            result["total"]
-            - (result["llm_cost"] + result["tts_cost"] + result["stt_cost"])
-        )
-        < 1e-10
-    )

api/tests/test_dograh_managed_correlation.py

@@ -0,0 +1,110 @@
+import json
+
+import pytest
+from openai._types import NOT_GIVEN as OPENAI_NOT_GIVEN
+from pipecat.frames.frames import TTSStartedFrame
+from pipecat.services.dograh.llm import DograhLLMService
+from pipecat.services.dograh.stt import DograhSTTService
+from pipecat.services.dograh.tts import DograhTTSService
+from pipecat.services.openai.base_llm import OpenAILLMSettings
+from websockets.protocol import State
+
+
+class _FakeWebSocket:
+    def __init__(self):
+        self.state = State.OPEN
+        self.messages: list[dict] = []
+
+    async def send(self, message: str) -> None:
+        self.messages.append(json.loads(message))
+
+    async def close(self, *args, **kwargs) -> None:
+        self.state = State.CLOSED
+
+
+def test_dograh_llm_uses_explicit_mps_correlation_id():
+    service = DograhLLMService(
+        api_key="mps-secret",
+        correlation_id="mps-corr-123",
+        settings=OpenAILLMSettings(model="default"),
+    )
+    service._start_metadata = {"workflow_run_id": 99}
+
+    params = service.build_chat_completion_params(
+        {
+            "messages": [],
+            "tools": OPENAI_NOT_GIVEN,
+            "tool_choice": OPENAI_NOT_GIVEN,
+        }
+    )
+
+    assert params["metadata"]["correlation_id"] == "mps-corr-123"
+    assert params["metadata"]["mps_billing_version"] == "2"
+
+
+@pytest.mark.asyncio
+async def test_dograh_stt_config_uses_explicit_mps_correlation_id(monkeypatch):
+    fake_ws = _FakeWebSocket()
+
+    async def fake_connect(url, additional_headers):
+        return fake_ws
+
+    monkeypatch.setattr(
+        "pipecat.services.dograh.stt.websocket_connect",
+        fake_connect,
+    )
+
+    service = DograhSTTService(
+        api_key="mps-secret",
+        correlation_id="mps-corr-123",
+        sample_rate=16000,
+    )
+    service._start_metadata = {"workflow_run_id": 99}
+
+    await service._connect_websocket()
+
+    assert fake_ws.messages[0]["type"] == "config"
+    assert fake_ws.messages[0]["correlation_id"] == "mps-corr-123"
+    assert fake_ws.messages[0]["mps_billing_version"] == "2"
+
+
+@pytest.mark.asyncio
+async def test_dograh_tts_messages_use_explicit_mps_correlation_id(monkeypatch):
+    fake_ws = _FakeWebSocket()
+
+    async def fake_connect(url, additional_headers):
+        return fake_ws
+
+    monkeypatch.setattr(
+        "pipecat.services.dograh.tts.websocket_connect",
+        fake_connect,
+    )
+
+    service = DograhTTSService(
+        api_key="mps-secret",
+        correlation_id="mps-corr-123",
+        sample_rate=24000,
+    )
+    service._start_metadata = {"workflow_run_id": 99}
+
+    await service._connect_websocket()
+    assert fake_ws.messages[0]["type"] == "config"
+    assert fake_ws.messages[0]["correlation_id"] == "mps-corr-123"
+    assert fake_ws.messages[0]["mps_billing_version"] == "2"
+
+    async def _noop(*args, **kwargs):
+        return None
+
+    service.audio_context_available = lambda context_id: False
+    service.create_audio_context = _noop
+    service.start_ttfb_metrics = _noop
+    service.start_tts_usage_metrics = _noop
+
+    frames = []
+    async for frame in service.run_tts("hello", "ctx-1"):
+        frames.append(frame)
+
+    assert isinstance(frames[0], TTSStartedFrame)
+    assert fake_ws.messages[1]["type"] == "create_context"
+    assert fake_ws.messages[1]["correlation_id"] == "mps-corr-123"
+    assert fake_ws.messages[1]["mps_billing_version"] == "2"

api/tests/test_from_number_pool_isolation.py

@@ -270,6 +270,12 @@ class TestDispatcherThreadsTelephonyConfig:
                 "api.services.campaign.campaign_call_dispatcher.get_backend_endpoints",
                 AsyncMock(return_value=("https://example.com", None)),
             ),
+            patch(
+                "api.services.campaign.campaign_call_dispatcher.authorize_workflow_run_start",
+                AsyncMock(
+                    return_value=SimpleNamespace(has_quota=True, error_message="")
+                ),
+            ),
         ):
             mock_db.get_workflow_by_id = AsyncMock(return_value=SimpleNamespace(id=1))
             mock_db.create_workflow_run = AsyncMock(return_value=workflow_run)

api/tests/test_grok_realtime_wrapper.py

@@ -7,7 +7,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.xai.realtime import events
 
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.configuration.registry import GrokRealtimeLLMConfiguration
 from api.services.pipecat.realtime.grok_realtime import (
     DograhGrokRealtimeLLMService,
@@ -120,7 +120,7 @@ async def test_completed_input_transcription_is_broadcast_as_finalized():
 
 
 def test_factory_creates_dograh_grok_realtime_service():
-    user_config = UserConfiguration(
+    effective_config = EffectiveAIModelConfiguration(
         is_realtime=True,
         realtime=GrokRealtimeLLMConfiguration(
             provider="grok_realtime",
@@ -131,7 +131,7 @@ def test_factory_creates_dograh_grok_realtime_service():
     )
 
     service = create_realtime_llm_service(
-        user_config,
+        effective_config,
         audio_config=SimpleNamespace(),
     )
 

api/tests/test_masked_key_rejection.py

@@ -1,10 +1,11 @@
+from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
 
 from api.routes.user import router
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.auth.depends import get_user
 from api.services.configuration.masking import mask_key
 from api.services.configuration.registry import (
@@ -14,14 +15,14 @@ from api.services.configuration.registry import (
 )
 
 
-def _make_test_app():
+def _make_test_app(selected_organization_id=None):
     app = FastAPI()
     app.include_router(router)
 
     mock_user = MagicMock()
     mock_user.id = 1
     mock_user.is_superuser = False
-    mock_user.selected_organization_id = None
+    mock_user.selected_organization_id = selected_organization_id
 
     app.dependency_overrides[get_user] = lambda: mock_user
     return app
@@ -32,7 +33,7 @@ MASKED_KEY = mask_key(REAL_KEY)  # "**************************cdef"
 
 
 def _existing_openai_config():
-    return UserConfiguration(
+    return EffectiveAIModelConfiguration(
         llm=OpenAILLMService(
             provider="openai",
             api_key=REAL_KEY,
@@ -110,7 +111,7 @@ class TestMaskedKeyRejection:
         client = TestClient(app)
 
         new_key = "AIzaSyNewRealKey12345678"
-        updated = UserConfiguration(
+        updated = EffectiveAIModelConfiguration(
             llm=GoogleLLMService(
                 provider="google",
                 api_key=new_key,
@@ -177,7 +178,7 @@ class TestMaskedKeyRejection:
 
         real_credentials = '{"type":"service_account","project_id":"demo-project"}'
         masked_credentials = mask_key(real_credentials)
-        existing = UserConfiguration(
+        existing = EffectiveAIModelConfiguration(
             llm=GoogleVertexLLMConfiguration(
                 provider="google_vertex",
                 api_key=None,
@@ -210,3 +211,38 @@ class TestMaskedKeyRejection:
             )
 
             assert response.status_code == 200
+
+    def test_preference_only_update_does_not_validate_or_save_model_config(self):
+        """Saving a test phone number through the legacy endpoint must not touch models."""
+        app = _make_test_app(selected_organization_id=11)
+        client = TestClient(app)
+        preferences = SimpleNamespace(test_phone_number=None, timezone=None)
+
+        with (
+            patch("api.routes.user.db_client") as mock_db,
+            patch("api.routes.user.UserConfigurationValidator") as mock_validator,
+            patch(
+                "api.routes.user.get_organization_preferences",
+                new=AsyncMock(return_value=preferences),
+            ),
+            patch(
+                "api.routes.user.upsert_organization_preferences",
+                new=AsyncMock(return_value=preferences),
+            ) as upsert_preferences,
+        ):
+            existing = _existing_openai_config()
+            mock_db.get_user_configurations = AsyncMock(return_value=existing)
+            mock_db.update_user_configuration = AsyncMock()
+            mock_db.get_organization_by_id = AsyncMock(return_value=None)
+            mock_validator.return_value.validate = AsyncMock()
+
+            response = client.put(
+                "/user/configurations/user",
+                json={"test_phone_number": "+15551234567"},
+            )
+
+            assert response.status_code == 200
+            assert response.json()["test_phone_number"] == "+15551234567"
+            mock_db.update_user_configuration.assert_not_called()
+            mock_validator.return_value.validate.assert_not_called()
+            upsert_preferences.assert_awaited_once()

api/tests/test_mps_service_key_client.py

@@ -87,3 +87,387 @@ async def test_check_service_key_usage_uses_bearer_self_usage(monkeypatch):
             "Content-Type": "application/json",
         },
     )
+
+
+@pytest.mark.asyncio
+async def test_create_correlation_id_uses_bearer_auth(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def post(self, url, json, headers):
+            calls.append(("POST", url, json, headers))
+            return _Response(200, {"correlation_id": "mps-corr-123"})
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.create_correlation_id(
+        service_key="mps_sk_paid",
+        workflow_run_id=42,
+    ) == {"correlation_id": "mps-corr-123"}
+    assert calls == [
+        (
+            "POST",
+            f"{client.base_url}/api/v1/service-keys/correlation-id/self",
+            {"workflow_run_id": 42},
+            {
+                "Authorization": "Bearer mps_sk_paid",
+                "Content-Type": "application/json",
+            },
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_get_billing_account_status_uses_hosted_org_auth(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def get(self, url, headers):
+            calls.append(("GET", url, headers))
+            return _Response(200, {"organization_id": 42, "billing_mode": "v2"})
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+    monkeypatch.setattr("api.services.mps_service_key_client.DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.DOGRAH_MPS_SECRET_KEY", "mps-secret"
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.get_billing_account_status(organization_id=42) == {
+        "organization_id": 42,
+        "billing_mode": "v2",
+    }
+    assert calls == [
+        (
+            "GET",
+            f"{client.base_url}/api/v1/billing/accounts/42/status",
+            {
+                "Content-Type": "application/json",
+                "X-Secret-Key": "mps-secret",
+                "X-Organization-Id": "42",
+            },
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_start_uses_hosted_org_auth(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def post(self, url, json, headers):
+            calls.append(("POST", url, json, headers))
+            return _Response(
+                200,
+                {
+                    "allowed": True,
+                    "billing_mode": "v2",
+                    "remaining_credits": "25.0000",
+                    "correlation_id": "mps-corr-123",
+                },
+            )
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+    monkeypatch.setattr("api.services.mps_service_key_client.DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.DOGRAH_MPS_SECRET_KEY", "mps-secret"
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.authorize_workflow_run_start(
+        organization_id=42,
+        workflow_run_id=88,
+        service_key="mps_sk_paid",
+        require_correlation_id=True,
+        minimum_credits=0.1,
+        metadata={"workflow_id": 7},
+        created_by="provider-123",
+    ) == {
+        "allowed": True,
+        "billing_mode": "v2",
+        "remaining_credits": "25.0000",
+        "correlation_id": "mps-corr-123",
+    }
+    assert calls == [
+        (
+            "POST",
+            f"{client.base_url}/api/v1/billing/accounts/42/run-authorization",
+            {
+                "workflow_run_id": 88,
+                "service_key": "mps_sk_paid",
+                "require_correlation_id": True,
+                "minimum_credits": 0.1,
+                "metadata": {"workflow_id": 7},
+            },
+            {
+                "Content-Type": "application/json",
+                "X-Secret-Key": "mps-secret",
+                "X-Organization-Id": "42",
+            },
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_ensure_billing_account_v2_uses_balance_endpoint(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def get(self, url, headers):
+            calls.append(("GET", url, headers))
+            return _Response(
+                200,
+                {
+                    "id": 7,
+                    "organization_id": 42,
+                    "billing_mode": "v2",
+                    "cached_balance_credits": "0.0000",
+                    "currency": "USD",
+                },
+            )
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+    monkeypatch.setattr("api.services.mps_service_key_client.DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.DOGRAH_MPS_SECRET_KEY", "mps-secret"
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.ensure_billing_account_v2(
+        organization_id=42,
+        created_by="provider-123",
+    ) == {
+        "id": 7,
+        "organization_id": 42,
+        "billing_mode": "v2",
+        "cached_balance_credits": "0.0000",
+        "currency": "USD",
+    }
+    assert calls == [
+        (
+            "GET",
+            f"{client.base_url}/api/v1/billing/accounts/42/balance",
+            {
+                "Content-Type": "application/json",
+                "X-Secret-Key": "mps-secret",
+                "X-Organization-Id": "42",
+            },
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_get_credit_ledger_sends_page_and_limit(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def get(self, url, params, headers):
+            calls.append(("GET", url, params, headers))
+            return _Response(
+                200,
+                {
+                    "account": {"organization_id": 42},
+                    "ledger_entries": [],
+                    "total_count": 0,
+                    "page": 3,
+                    "limit": 25,
+                    "total_pages": 0,
+                },
+            )
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+    monkeypatch.setattr("api.services.mps_service_key_client.DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.DOGRAH_MPS_SECRET_KEY", "mps-secret"
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.get_credit_ledger(
+        organization_id=42,
+        page=3,
+        limit=25,
+    ) == {
+        "account": {"organization_id": 42},
+        "ledger_entries": [],
+        "total_count": 0,
+        "page": 3,
+        "limit": 25,
+        "total_pages": 0,
+    }
+    assert calls == [
+        (
+            "GET",
+            f"{client.base_url}/api/v1/billing/accounts/42/ledger",
+            {"page": 3, "limit": 25},
+            {
+                "Content-Type": "application/json",
+                "X-Secret-Key": "mps-secret",
+                "X-Organization-Id": "42",
+            },
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_report_platform_usage_uses_hosted_secret_auth(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def post(self, url, json, headers):
+            calls.append(("POST", url, json, headers))
+            return _Response(200, {"metered": True})
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+    monkeypatch.setattr("api.services.mps_service_key_client.DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.DOGRAH_MPS_SECRET_KEY", "mps-secret"
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.report_platform_usage(
+        organization_id=42,
+        correlation_id="mps-corr-123",
+        workflow_run_id=123,
+        metadata={"source": "workflow_run_completion"},
+    ) == {"metered": True}
+    assert calls == [
+        (
+            "POST",
+            f"{client.base_url}/api/v1/billing/accounts/42/platform-usage",
+            {
+                "correlation_id": "mps-corr-123",
+                "workflow_run_id": 123,
+                "metadata": {"source": "workflow_run_completion"},
+            },
+            {
+                "Content-Type": "application/json",
+                "X-Secret-Key": "mps-secret",
+                "X-Organization-Id": "42",
+            },
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_report_platform_usage_sends_duration_without_correlation(monkeypatch):
+    calls = []
+
+    class FakeAsyncClient:
+        def __init__(self, timeout):
+            self.timeout = timeout
+
+        async def __aenter__(self):
+            return self
+
+        async def __aexit__(self, exc_type, exc, tb):
+            return None
+
+        async def post(self, url, json, headers):
+            calls.append(("POST", url, json, headers))
+            return _Response(200, {"metered": True})
+
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.httpx.AsyncClient", FakeAsyncClient
+    )
+    monkeypatch.setattr("api.services.mps_service_key_client.DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        "api.services.mps_service_key_client.DOGRAH_MPS_SECRET_KEY", "mps-secret"
+    )
+
+    client = MPSServiceKeyClient()
+
+    assert await client.report_platform_usage(
+        organization_id=42,
+        duration_seconds=87.0,
+        workflow_run_id=123,
+        metadata={"source": "workflow_run_completion"},
+    ) == {"metered": True}
+    assert calls == [
+        (
+            "POST",
+            f"{client.base_url}/api/v1/billing/accounts/42/platform-usage",
+            {
+                "duration_seconds": 87.0,
+                "workflow_run_id": 123,
+                "metadata": {"source": "workflow_run_completion"},
+            },
+            {
+                "Content-Type": "application/json",
+                "X-Secret-Key": "mps-secret",
+                "X-Organization-Id": "42",
+            },
+        )
+    ]

api/tests/test_organization_usage_billing.py

@@ -0,0 +1,99 @@
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+
+from api.routes import organization_usage
+
+
+def test_is_mps_billing_v2_depends_only_on_account_mode():
+    assert organization_usage._is_mps_billing_v2({"billing_mode": "v2"}) is True
+    assert organization_usage._is_mps_billing_v2({"billing_mode": "v1"}) is False
+    assert organization_usage._is_mps_billing_v2({"billing_mode": "shadow"}) is False
+    assert organization_usage._is_mps_billing_v2(None) is False
+
+
+@pytest.mark.asyncio
+async def test_get_mps_billing_account_status_uses_user_provider_id(monkeypatch):
+    get_status = AsyncMock(return_value={"billing_mode": "v2"})
+    monkeypatch.setattr(
+        organization_usage.mps_service_key_client,
+        "get_billing_account_status",
+        get_status,
+    )
+
+    user = SimpleNamespace(provider_id="provider-123")
+
+    assert await organization_usage._get_mps_billing_account_status(user, 42) == {
+        "billing_mode": "v2"
+    }
+    get_status.assert_awaited_once_with(
+        organization_id=42,
+        created_by="provider-123",
+    )
+
+
+@pytest.mark.asyncio
+async def test_get_billing_credits_pages_v2_ledger(monkeypatch):
+    monkeypatch.setattr(organization_usage, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        organization_usage,
+        "_get_mps_billing_account_status",
+        AsyncMock(return_value={"billing_mode": "v2"}),
+    )
+    get_ledger = AsyncMock(
+        return_value={
+            "account": {
+                "id": 7,
+                "organization_id": 42,
+                "billing_mode": "v2",
+                "cached_balance_credits": 250,
+                "currency": "USD",
+            },
+            "ledger_entries": [
+                {
+                    "id": 99,
+                    "entry_type": "grant",
+                    "origin": "account_creation",
+                    "credits_delta": 250,
+                    "balance_after": 250,
+                    "created_at": "2026-06-12T00:00:00Z",
+                }
+            ],
+            "total_debits_credits": 75,
+            "total_count": 101,
+            "page": 3,
+            "limit": 25,
+            "total_pages": 5,
+        }
+    )
+    monkeypatch.setattr(
+        organization_usage.mps_service_key_client,
+        "get_credit_ledger",
+        get_ledger,
+    )
+
+    user = SimpleNamespace(
+        provider_id="provider-123",
+        selected_organization_id=42,
+    )
+
+    response = await organization_usage.get_billing_credits(
+        page=3,
+        limit=25,
+        user=user,
+    )
+
+    get_ledger.assert_awaited_once_with(
+        organization_id=42,
+        page=3,
+        limit=25,
+        created_by="provider-123",
+    )
+    assert response.billing_version == "v2"
+    assert response.total_credits_used == 75
+    assert response.total_count == 101
+    assert response.page == 3
+    assert response.limit == 25
+    assert response.total_pages == 5
+    assert response.ledger_entries[0].id == 99

api/tests/test_pre_call_fetch.py

@@ -0,0 +1,66 @@
+from api.services.pipecat.pre_call_fetch import _extract_initial_context
+
+
+class TestExtractInitialContext:
+    """Tests for _extract_initial_context, the pre-call fetch response parser."""
+
+    def test_initial_context_nested_under_call_inbound(self):
+        """The canonical `initial_context` key nested under `call_inbound`."""
+        response = {"call_inbound": {"initial_context": {"customer_name": "Jane"}}}
+        assert _extract_initial_context(response) == {"customer_name": "Jane"}
+
+    def test_initial_context_at_top_level(self):
+        """The canonical `initial_context` key at the top level."""
+        response = {"initial_context": {"customer_name": "Jane"}}
+        assert _extract_initial_context(response) == {"customer_name": "Jane"}
+
+    def test_legacy_dynamic_variables_nested(self):
+        """The legacy `dynamic_variables` key still works nested under `call_inbound`."""
+        response = {"call_inbound": {"dynamic_variables": {"customer_name": "Jane"}}}
+        assert _extract_initial_context(response) == {"customer_name": "Jane"}
+
+    def test_legacy_dynamic_variables_at_top_level(self):
+        """The legacy `dynamic_variables` key still works at the top level."""
+        response = {"dynamic_variables": {"customer_name": "Jane"}}
+        assert _extract_initial_context(response) == {"customer_name": "Jane"}
+
+    def test_initial_context_takes_precedence_over_legacy(self):
+        """When both keys are present, `initial_context` wins."""
+        response = {
+            "call_inbound": {
+                "initial_context": {"source": "new"},
+                "dynamic_variables": {"source": "legacy"},
+            }
+        }
+        assert _extract_initial_context(response) == {"source": "new"}
+
+    def test_falls_back_to_legacy_when_initial_context_not_a_dict(self):
+        """A non-dict `initial_context` falls back to `dynamic_variables`."""
+        response = {
+            "initial_context": None,
+            "dynamic_variables": {"customer_name": "Jane"},
+        }
+        assert _extract_initial_context(response) == {"customer_name": "Jane"}
+
+    def test_nested_values_preserved(self):
+        """Nested objects pass through untouched for dot-notation access."""
+        response = {
+            "call_inbound": {
+                "initial_context": {"customer": {"address": {"city": "LA"}}}
+            }
+        }
+        assert _extract_initial_context(response) == {
+            "customer": {"address": {"city": "LA"}}
+        }
+
+    def test_empty_when_no_known_keys(self):
+        """A response with neither key yields an empty dict."""
+        assert _extract_initial_context({"call_inbound": {"agent_id": 1}}) == {}
+
+    def test_empty_when_call_inbound_missing(self):
+        """No `call_inbound` and no top-level keys yields an empty dict."""
+        assert _extract_initial_context({}) == {}
+
+    def test_non_dict_vars_yield_empty(self):
+        """A non-dict value under a known key yields an empty dict."""
+        assert _extract_initial_context({"initial_context": "nope"}) == {}

api/tests/test_public_agent_routes.py

@@ -57,7 +57,7 @@ def test_trigger_route_executes_as_workflow_owner():
     with (
         patch("api.routes.public_agent.db_client") as mock_db,
         patch(
-            "api.routes.public_agent.check_dograh_quota_by_user_id",
+            "api.routes.public_agent.authorize_workflow_run_start",
             new=quota_mock,
         ),
         patch(
@@ -92,7 +92,10 @@ def test_trigger_route_executes_as_workflow_owner():
         )
 
     assert response.status_code == 200
-    quota_mock.assert_awaited_once_with(workflow.user_id, workflow_id=workflow.id)
+    quota_mock.assert_awaited_once_with(
+        workflow_id=workflow.id,
+        workflow_run_id=501,
+    )
     mock_db.get_workflow.assert_awaited_once_with(workflow.id, organization_id=11)
 
     create_kwargs = mock_db.create_workflow_run.await_args.kwargs
@@ -124,7 +127,7 @@ def test_workflow_uuid_route_uses_scoped_lookup_and_shared_execution():
     with (
         patch("api.routes.public_agent.db_client") as mock_db,
         patch(
-            "api.routes.public_agent.check_dograh_quota_by_user_id",
+            "api.routes.public_agent.authorize_workflow_run_start",
             new=quota_mock,
         ),
         patch(

api/tests/test_public_embed_cors.py

@@ -0,0 +1,274 @@
+from types import SimpleNamespace
+
+import pytest
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.testclient import TestClient
+
+from api.routes.public_embed import PublicEmbedCORSMiddleware, router
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://app.dograh.com"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+app.add_middleware(PublicEmbedCORSMiddleware, api_prefix="/api/v1")
+app.include_router(router, prefix="/api/v1")
+client = TestClient(app, raise_server_exceptions=False)
+
+_ACTIVE_TOKEN = SimpleNamespace(
+    id=10,
+    is_active=True,
+    expires_at=None,
+    allowed_domains=[],
+    workflow_id=1,
+    created_by=7,
+    usage_limit=None,
+    usage_count=0,
+    settings={},
+)
+
+_RESTRICTED_TOKEN = SimpleNamespace(
+    id=20,
+    is_active=True,
+    expires_at=None,
+    allowed_domains=["allowed.example.com"],
+    workflow_id=2,
+    created_by=7,
+    usage_limit=None,
+    usage_count=0,
+    settings={},
+)
+
+_LOCALHOST_TOKEN = SimpleNamespace(
+    id=30,
+    is_active=True,
+    expires_at=None,
+    allowed_domains=["localhost:3000", "localhost:3020"],
+    workflow_id=3,
+    created_by=7,
+    usage_limit=None,
+    usage_count=0,
+    settings={},
+)
+
+
+@pytest.fixture(autouse=True)
+def _patch_db(monkeypatch):
+    async def _get_token(token):
+        if token == "valid":
+            return _ACTIVE_TOKEN
+        if token == "restricted":
+            return _RESTRICTED_TOKEN
+        if token == "localhost":
+            return _LOCALHOST_TOKEN
+        return None
+
+    async def _get_token_by_id(token_id):
+        if token_id == _ACTIVE_TOKEN.id:
+            return _ACTIVE_TOKEN
+        if token_id == _RESTRICTED_TOKEN.id:
+            return _RESTRICTED_TOKEN
+        if token_id == _LOCALHOST_TOKEN.id:
+            return _LOCALHOST_TOKEN
+        return None
+
+    async def _get_session(session_token):
+        if session_token == "session-valid":
+            return SimpleNamespace(embed_token_id=_ACTIVE_TOKEN.id, expires_at=None)
+        if session_token == "session-restricted":
+            return SimpleNamespace(embed_token_id=_RESTRICTED_TOKEN.id, expires_at=None)
+        return None
+
+    async def _create_workflow_run(**_kwargs):
+        return SimpleNamespace(id=123)
+
+    async def _noop(*_args, **_kwargs):
+        return None
+
+    monkeypatch.setattr(
+        "api.routes.public_embed.db_client.get_embed_token_by_token",
+        _get_token,
+    )
+    monkeypatch.setattr(
+        "api.routes.public_embed.db_client.get_embed_token_by_id",
+        _get_token_by_id,
+    )
+    monkeypatch.setattr(
+        "api.routes.public_embed.db_client.get_embed_session_by_token",
+        _get_session,
+    )
+    monkeypatch.setattr(
+        "api.routes.public_embed.db_client.create_workflow_run",
+        _create_workflow_run,
+    )
+    monkeypatch.setattr(
+        "api.routes.public_embed.db_client.create_embed_session",
+        _noop,
+    )
+    monkeypatch.setattr(
+        "api.routes.public_embed.db_client.increment_embed_token_usage",
+        _noop,
+    )
+    monkeypatch.setattr("api.routes.public_embed.TURN_SECRET", "test-secret")
+    monkeypatch.setattr(
+        "api.routes.public_embed.generate_turn_credentials",
+        lambda _user_id: {
+            "username": "turn-user",
+            "password": "turn-password",
+            "ttl": 3600,
+            "uris": ["turn:example.com:3478"],
+        },
+    )
+
+
+def _assert_embed_cors(resp, origin: str):
+    assert resp.headers.get("access-control-allow-origin") == origin
+    assert "origin" in {
+        value.strip().lower() for value in resp.headers.get("vary", "").split(",")
+    }
+
+
+def test_options_config_returns_acao_for_allowed_origin():
+    origin = "https://mysite.vercel.app"
+    resp = client.options(
+        "/api/v1/public/embed/config/valid",
+        headers={
+            "Origin": origin,
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_options_config_accepts_allowed_localhost_port():
+    origin = "http://localhost:3020"
+    resp = client.options(
+        "/api/v1/public/embed/config/localhost",
+        headers={
+            "Origin": origin,
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_options_config_rejects_unknown_token():
+    resp = client.options(
+        "/api/v1/public/embed/config/unknown",
+        headers={
+            "Origin": "https://mysite.vercel.app",
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    assert resp.status_code == 403
+
+
+def test_options_config_rejects_disallowed_origin():
+    resp = client.options(
+        "/api/v1/public/embed/config/restricted",
+        headers={
+            "Origin": "https://notallowed.example.com",
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    assert resp.status_code == 403
+
+
+def test_get_config_includes_acao_header():
+    origin = "https://mysite.vercel.app"
+    resp = client.get(
+        "/api/v1/public/embed/config/valid",
+        headers={"Origin": origin},
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_get_config_accepts_allowed_localhost_port():
+    origin = "http://localhost:3020"
+    resp = client.get(
+        "/api/v1/public/embed/config/localhost",
+        headers={"Origin": origin},
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_get_config_rejects_unlisted_localhost_port():
+    resp = client.get(
+        "/api/v1/public/embed/config/localhost",
+        headers={"Origin": "http://localhost:3021"},
+    )
+    assert resp.status_code == 403
+
+
+def test_get_config_rejects_disallowed_origin():
+    resp = client.get(
+        "/api/v1/public/embed/config/restricted",
+        headers={"Origin": "https://notallowed.example.com"},
+    )
+    assert resp.status_code == 403
+
+
+def test_init_includes_acao_header():
+    origin = "https://mysite.vercel.app"
+    resp = client.post(
+        "/api/v1/public/embed/init",
+        headers={"Origin": origin},
+        json={"token": "valid"},
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_turn_credentials_includes_acao_header():
+    origin = "https://mysite.vercel.app"
+    resp = client.get(
+        "/api/v1/public/embed/turn-credentials/session-valid",
+        headers={"Origin": origin},
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_options_init_returns_acao_for_allowed_origin():
+    origin = "https://mysite.vercel.app"
+    resp = client.options(
+        "/api/v1/public/embed/init",
+        headers={
+            "Origin": origin,
+            "Access-Control-Request-Method": "POST",
+        },
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_options_turn_credentials_returns_acao_for_allowed_origin():
+    origin = "https://mysite.vercel.app"
+    resp = client.options(
+        "/api/v1/public/embed/turn-credentials/session-valid",
+        headers={
+            "Origin": origin,
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    assert resp.status_code == 200
+    _assert_embed_cors(resp, origin)
+
+
+def test_options_turn_credentials_rejects_disallowed_origin():
+    resp = client.options(
+        "/api/v1/public/embed/turn-credentials/session-restricted",
+        headers={
+            "Origin": "https://notallowed.example.com",
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    assert resp.status_code == 403

api/tests/test_quota_service.py

@@ -0,0 +1,369 @@
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+
+from api.services import quota_service
+from api.services.configuration.registry import ServiceProviders
+from api.services.managed_model_services import MPS_CORRELATION_ID_CONTEXT_KEY
+
+
+def _dograh_config(
+    api_key: str = "mps_sk_12345678",
+    *,
+    managed_service_version: int = 1,
+):
+    return SimpleNamespace(
+        managed_service_version=managed_service_version,
+        llm=SimpleNamespace(provider=ServiceProviders.DOGRAH, api_key=api_key),
+        stt=None,
+        tts=None,
+        embeddings=None,
+    )
+
+
+def _byok_config():
+    return SimpleNamespace(
+        managed_service_version=2,
+        llm=SimpleNamespace(provider="openai", api_key="sk-openai"),
+        stt=None,
+        tts=None,
+        embeddings=None,
+    )
+
+
+def _workflow():
+    return SimpleNamespace(
+        id=7,
+        user_id=123,
+        organization_id=42,
+        workflow_configurations={"model_overrides": {}},
+    )
+
+
+def _workflow_owner():
+    return SimpleNamespace(
+        id=123,
+        provider_id="provider-123",
+    )
+
+
+def _actor():
+    return SimpleNamespace(
+        id=456,
+        provider_id="actor-456",
+        selected_organization_id=42,
+    )
+
+
+def _patch_workflow_context(monkeypatch, *, workflow=None, owner=None):
+    monkeypatch.setattr(
+        quota_service.db_client,
+        "get_workflow_by_id",
+        AsyncMock(return_value=workflow or _workflow()),
+    )
+    monkeypatch.setattr(
+        quota_service.db_client,
+        "get_user_by_id",
+        AsyncMock(return_value=owner or _workflow_owner()),
+    )
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_uses_workflow_org_for_hosted_v2(
+    monkeypatch,
+):
+    get_config = AsyncMock(return_value=_dograh_config())
+    authorize = AsyncMock(
+        return_value={
+            "allowed": True,
+            "billing_mode": "v2",
+            "remaining_credits": "25.0000",
+        }
+    )
+    check_usage = AsyncMock()
+
+    monkeypatch.setattr(quota_service, "DEPLOYMENT_MODE", "saas")
+    _patch_workflow_context(monkeypatch)
+    monkeypatch.setattr(
+        quota_service,
+        "get_effective_ai_model_configuration_for_workflow",
+        get_config,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "authorize_workflow_run_start",
+        authorize,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "check_service_key_usage",
+        check_usage,
+    )
+
+    result = await quota_service.authorize_workflow_run_start(workflow_id=7)
+
+    assert result.has_quota is True
+    get_config.assert_awaited_once_with(
+        user_id=123,
+        organization_id=42,
+        workflow_configurations={"model_overrides": {}},
+    )
+    authorize.assert_awaited_once_with(
+        organization_id=42,
+        workflow_run_id=None,
+        service_key=None,
+        require_correlation_id=False,
+        minimum_credits=quota_service.MINIMUM_DOGRAH_CREDITS_FOR_CALL,
+        created_by="provider-123",
+        metadata={"dograh_user_id": "123", "workflow_id": 7},
+    )
+    check_usage.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_v2_insufficient_credits_prompts_billing(
+    monkeypatch,
+):
+    get_config = AsyncMock(return_value=_byok_config())
+    authorize = AsyncMock(
+        return_value={
+            "allowed": False,
+            "billing_mode": "v2",
+            "remaining_credits": "0.0000",
+            "error": "insufficient_credits",
+        }
+    )
+    check_usage = AsyncMock()
+
+    monkeypatch.setattr(quota_service, "DEPLOYMENT_MODE", "saas")
+    _patch_workflow_context(monkeypatch)
+    monkeypatch.setattr(
+        quota_service,
+        "get_effective_ai_model_configuration_for_workflow",
+        get_config,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "authorize_workflow_run_start",
+        authorize,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "check_service_key_usage",
+        check_usage,
+    )
+
+    result = await quota_service.authorize_workflow_run_start(workflow_id=7)
+
+    assert result.has_quota is False
+    assert result.error_code == "insufficient_credits"
+    assert "/billing" in result.error_message
+    assert "founders@dograh.com" not in result.error_message
+    authorize.assert_awaited_once()
+    check_usage.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_v1_uses_legacy_key_usage(
+    monkeypatch,
+):
+    api_key = "mps_sk_12345678"
+    get_config = AsyncMock(return_value=_dograh_config(api_key))
+    authorize = AsyncMock(
+        return_value={
+            "allowed": True,
+            "billing_mode": "v1",
+            "remaining_credits": "0.0000",
+        }
+    )
+    check_usage = AsyncMock(
+        return_value={"total_credits_used": 500.0, "remaining_credits": 0.0}
+    )
+
+    monkeypatch.setattr(quota_service, "DEPLOYMENT_MODE", "saas")
+    _patch_workflow_context(monkeypatch)
+    monkeypatch.setattr(
+        quota_service,
+        "get_effective_ai_model_configuration_for_workflow",
+        get_config,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "authorize_workflow_run_start",
+        authorize,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "check_service_key_usage",
+        check_usage,
+    )
+
+    result = await quota_service.authorize_workflow_run_start(workflow_id=7)
+
+    assert result.has_quota is False
+    assert result.error_code == "quota_exceeded"
+    assert "founders@dograh.com" in result.error_message
+    assert "/billing" not in result.error_message
+    authorize.assert_awaited_once()
+    check_usage.assert_awaited_once_with(
+        api_key,
+        organization_id=42,
+        created_by="provider-123",
+    )
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_managed_v2_stores_hosted_correlation(
+    monkeypatch,
+):
+    api_key = "mps_sk_12345678"
+    workflow_run = SimpleNamespace(initial_context={"existing": "value"})
+    get_config = AsyncMock(
+        return_value=_dograh_config(api_key, managed_service_version=2)
+    )
+    authorize = AsyncMock(
+        return_value={
+            "allowed": True,
+            "billing_mode": "v2",
+            "remaining_credits": "25.0000",
+            "correlation_id": "mps-corr-123",
+        }
+    )
+    update_workflow_run = AsyncMock()
+
+    monkeypatch.setattr(quota_service, "DEPLOYMENT_MODE", "saas")
+    _patch_workflow_context(monkeypatch)
+    monkeypatch.setattr(
+        quota_service.db_client,
+        "get_workflow_run_by_id",
+        AsyncMock(return_value=workflow_run),
+    )
+    monkeypatch.setattr(
+        quota_service.db_client,
+        "update_workflow_run",
+        update_workflow_run,
+    )
+    monkeypatch.setattr(
+        quota_service,
+        "get_effective_ai_model_configuration_for_workflow",
+        get_config,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "authorize_workflow_run_start",
+        authorize,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "check_service_key_usage",
+        AsyncMock(),
+    )
+
+    result = await quota_service.authorize_workflow_run_start(
+        workflow_id=7,
+        workflow_run_id=88,
+    )
+
+    assert result.has_quota is True
+    authorize.assert_awaited_once_with(
+        organization_id=42,
+        workflow_run_id=88,
+        service_key=api_key,
+        require_correlation_id=True,
+        minimum_credits=quota_service.MINIMUM_DOGRAH_CREDITS_FOR_CALL,
+        created_by="provider-123",
+        metadata={"dograh_user_id": "123", "workflow_id": 7},
+    )
+    update_workflow_run.assert_awaited_once_with(
+        88,
+        initial_context={
+            "existing": "value",
+            MPS_CORRELATION_ID_CONTEXT_KEY: "mps-corr-123",
+        },
+    )
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_oss_uses_key_paths_not_workflow_org(
+    monkeypatch,
+):
+    api_key = "mps_sk_12345678"
+    workflow_run = SimpleNamespace(initial_context={})
+    get_config = AsyncMock(
+        return_value=_dograh_config(api_key, managed_service_version=2)
+    )
+    hosted_authorize = AsyncMock()
+    check_usage = AsyncMock(
+        return_value={"total_credits_used": 1.0, "remaining_credits": 499.0}
+    )
+    create_correlation = AsyncMock(return_value={"correlation_id": "oss-corr-123"})
+    update_workflow_run = AsyncMock()
+
+    monkeypatch.setattr(quota_service, "DEPLOYMENT_MODE", "oss")
+    _patch_workflow_context(monkeypatch)
+    monkeypatch.setattr(
+        quota_service.db_client,
+        "get_workflow_run_by_id",
+        AsyncMock(return_value=workflow_run),
+    )
+    monkeypatch.setattr(
+        quota_service.db_client,
+        "update_workflow_run",
+        update_workflow_run,
+    )
+    monkeypatch.setattr(
+        quota_service,
+        "get_effective_ai_model_configuration_for_workflow",
+        get_config,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "authorize_workflow_run_start",
+        hosted_authorize,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "check_service_key_usage",
+        check_usage,
+    )
+    monkeypatch.setattr(
+        quota_service.mps_service_key_client,
+        "create_correlation_id",
+        create_correlation,
+    )
+
+    result = await quota_service.authorize_workflow_run_start(
+        workflow_id=7,
+        workflow_run_id=88,
+    )
+
+    assert result.has_quota is True
+    hosted_authorize.assert_not_awaited()
+    check_usage.assert_awaited_once_with(
+        api_key,
+        organization_id=None,
+        created_by="provider-123",
+    )
+    create_correlation.assert_awaited_once_with(
+        service_key=api_key,
+        workflow_run_id=88,
+    )
+    update_workflow_run.assert_awaited_once_with(
+        88,
+        initial_context={MPS_CORRELATION_ID_CONTEXT_KEY: "oss-corr-123"},
+    )
+
+
+@pytest.mark.asyncio
+async def test_authorize_workflow_run_rejects_actor_from_another_org(monkeypatch):
+    monkeypatch.setattr(quota_service, "DEPLOYMENT_MODE", "saas")
+    _patch_workflow_context(monkeypatch)
+
+    result = await quota_service.authorize_workflow_run_start(
+        workflow_id=7,
+        actor_user=SimpleNamespace(selected_organization_id=999),
+    )
+
+    assert result.has_quota is False
+    assert result.error_code == "workflow_not_found"

api/tests/test_resolve_effective_config.py

@@ -2,14 +2,14 @@
 TDD tests for resolve_effective_config().
 
 This function deep-merges workflow-level model_overrides onto the global
-UserConfiguration. Fields not overridden inherit from global.
+EffectiveAIModelConfiguration. Fields not overridden inherit from global.
 
 Module under test: api.services.configuration.resolve
 """
 
 import pytest
 
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.configuration.masking import (
     contains_masked_key,
     mask_workflow_configurations,
@@ -35,9 +35,9 @@ from api.services.configuration.resolve import (
 
 
 @pytest.fixture
-def global_config() -> UserConfiguration:
+def global_config() -> EffectiveAIModelConfiguration:
     """A realistic global user configuration."""
-    return UserConfiguration(
+    return EffectiveAIModelConfiguration(
         llm=OpenAILLMService(
             provider="openai", api_key="sk-global-llm", model="gpt-4.1"
         ),
@@ -59,9 +59,9 @@ def global_config() -> UserConfiguration:
 
 
 @pytest.fixture
-def global_config_realtime() -> UserConfiguration:
+def global_config_realtime() -> EffectiveAIModelConfiguration:
     """Global config with realtime enabled."""
-    return UserConfiguration(
+    return EffectiveAIModelConfiguration(
         llm=OpenAILLMService(
             provider="openai", api_key="sk-global-llm", model="gpt-4.1"
         ),
@@ -302,7 +302,7 @@ class TestRealtimeOverride:
 class TestOverrideOnNullGlobal:
     def test_override_stt_when_global_is_none(self):
         """When global has no STT config, override creates one from scratch."""
-        config = UserConfiguration(
+        config = EffectiveAIModelConfiguration(
             llm=OpenAILLMService(provider="openai", api_key="sk-key", model="gpt-4.1"),
             stt=None,
             tts=None,
@@ -325,7 +325,7 @@ class TestOverrideOnNullGlobal:
 
     def test_override_realtime_when_global_is_none(self):
         """Realtime section can be created from override even if global has none."""
-        config = UserConfiguration(
+        config = EffectiveAIModelConfiguration(
             llm=OpenAILLMService(provider="openai", api_key="sk-key", model="gpt-4.1"),
             is_realtime=False,
             realtime=None,

api/tests/test_run_usage_response.py

@@ -1,4 +1,4 @@
-from api.services.pricing.run_usage_response import format_public_usage_info
+from api.services.workflow.run_usage_response import format_public_usage_info
 
 
 def test_format_public_usage_info():

api/tests/test_telephony_routes.py

@@ -1,5 +1,5 @@
 from types import SimpleNamespace
-from unittest.mock import AsyncMock, Mock, patch
+from unittest.mock import ANY, AsyncMock, Mock, patch
 
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
@@ -54,7 +54,7 @@ def test_initiate_call_executes_as_workflow_owner_for_shared_org_workflow():
     with (
         patch("api.routes.telephony.db_client") as mock_db,
         patch(
-            "api.routes.telephony.check_dograh_quota_by_user_id",
+            "api.routes.telephony.authorize_workflow_run_start",
             new=quota_mock,
         ),
         patch(
@@ -88,7 +88,11 @@ def test_initiate_call_executes_as_workflow_owner_for_shared_org_workflow():
         )
 
     assert response.status_code == 200
-    quota_mock.assert_awaited_once_with(workflow.user_id, workflow_id=workflow.id)
+    quota_mock.assert_awaited_once_with(
+        workflow_id=workflow.id,
+        workflow_run_id=501,
+        actor_user=ANY,
+    )
     mock_db.get_workflow.assert_awaited_once_with(workflow.id, organization_id=11)
 
     create_call = mock_db.create_workflow_run.await_args
@@ -103,6 +107,61 @@ def test_initiate_call_executes_as_workflow_owner_for_shared_org_workflow():
     assert initiate_kwargs["workflow_id"] == workflow.id
     assert initiate_kwargs["user_id"] == workflow.user_id
     assert "user_id=99" in initiate_kwargs["webhook_url"]
+    mock_db.get_user_configurations.assert_not_called()
+
+
+def test_initiate_call_uses_organization_preference_phone_number():
+    app = _make_test_app()
+    client = TestClient(app)
+
+    workflow = _workflow()
+    provider = _provider()
+    quota_mock = AsyncMock(
+        return_value=SimpleNamespace(has_quota=True, error_message="")
+    )
+
+    with (
+        patch("api.routes.telephony.db_client") as mock_db,
+        patch(
+            "api.routes.telephony.authorize_workflow_run_start",
+            new=quota_mock,
+        ),
+        patch(
+            "api.routes.telephony.get_default_telephony_provider",
+            new=AsyncMock(return_value=provider),
+        ),
+        patch(
+            "api.routes.telephony.get_backend_endpoints",
+            new=AsyncMock(return_value=("https://api.example.com", "wss://ignored")),
+        ),
+    ):
+        mock_db.get_user_configurations = AsyncMock(
+            return_value=SimpleNamespace(test_phone_number="+15550000000")
+        )
+        mock_db.get_configuration = Mock(
+            return_value=SimpleNamespace(value={"test_phone_number": "+15557654321"})
+        )
+        mock_db.get_default_telephony_configuration = AsyncMock(
+            return_value=SimpleNamespace(id=55)
+        )
+        mock_db.get_workflow = AsyncMock(return_value=workflow)
+        mock_db.create_workflow_run = AsyncMock(
+            return_value=SimpleNamespace(
+                id=501,
+                name="WR-TEL-OUT-00000001",
+                initial_context={},
+            )
+        )
+        mock_db.update_workflow_run = AsyncMock()
+
+        response = client.post(
+            "/telephony/initiate-call",
+            json={"workflow_id": workflow.id},
+        )
+
+    assert response.status_code == 200
+    assert provider.initiate_call.await_args.kwargs["to_number"] == "+15557654321"
+    mock_db.get_user_configurations.assert_not_called()
 
 
 def test_initiate_call_rejects_existing_run_for_different_workflow():
@@ -118,7 +177,7 @@ def test_initiate_call_rejects_existing_run_for_different_workflow():
     with (
         patch("api.routes.telephony.db_client") as mock_db,
         patch(
-            "api.routes.telephony.check_dograh_quota_by_user_id",
+            "api.routes.telephony.authorize_workflow_run_start",
             new=quota_mock,
         ),
         patch(

api/tests/test_ultravox_realtime_wrapper.py

@@ -10,7 +10,7 @@ from pipecat.processors.frame_processor import FrameDirection
 from websockets.exceptions import ConnectionClosedError
 from websockets.frames import Close
 
-from api.schemas.user_configuration import UserConfiguration
+from api.schemas.ai_model_configuration import EffectiveAIModelConfiguration
 from api.services.configuration.registry import UltravoxRealtimeLLMConfiguration
 from api.services.pipecat.realtime.ultravox_realtime import (
     _RESUMPTION_USER_MESSAGE,
@@ -430,7 +430,7 @@ async def test_receive_messages_reports_unexpected_websocket_close():
 
 
 def test_factory_creates_dograh_ultravox_realtime_service():
-    user_config = UserConfiguration(
+    effective_config = EffectiveAIModelConfiguration(
         is_realtime=True,
         realtime=UltravoxRealtimeLLMConfiguration(
             provider="ultravox_realtime",
@@ -441,7 +441,7 @@ def test_factory_creates_dograh_ultravox_realtime_service():
     )
 
     service = create_realtime_llm_service(
-        user_config,
+        effective_config,
         audio_config=SimpleNamespace(),
     )
 

api/tests/test_workflow_run_billing.py

@@ -0,0 +1,212 @@
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+
+from api.services import workflow_run_billing as workflow_run_billing_mod
+from api.services.workflow_run_billing import (
+    report_completed_workflow_run_platform_usage,
+    report_workflow_run_platform_usage,
+)
+
+
+def _make_workflow_run():
+    return SimpleNamespace(
+        id=123,
+        workflow_id=456,
+        is_completed=True,
+        initial_context={"mps_correlation_id": "mps-corr-123"},
+        usage_info={"call_duration_seconds": 87},
+        workflow=SimpleNamespace(
+            organization_id=42,
+            user=SimpleNamespace(selected_organization_id=42),
+        ),
+    )
+
+
+@pytest.mark.asyncio
+async def test_report_workflow_run_platform_usage_reports_hosted_completion(
+    monkeypatch,
+):
+    workflow_run = _make_workflow_run()
+    get_status = AsyncMock(return_value={"billing_mode": "v2"})
+    report_usage = AsyncMock(return_value={"metered": True})
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "get_billing_account_status",
+        get_status,
+    )
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_workflow_run_platform_usage(workflow_run)
+
+    report_usage.assert_awaited_once_with(
+        organization_id=42,
+        correlation_id="mps-corr-123",
+        duration_seconds=None,
+        workflow_run_id=workflow_run.id,
+        metadata={
+            "source": "workflow_run_completion",
+            "workflow_id": workflow_run.workflow_id,
+            "duration_source": "mps_correlation",
+        },
+    )
+
+
+@pytest.mark.asyncio
+async def test_report_workflow_run_platform_usage_reports_duration_without_correlation(
+    monkeypatch,
+):
+    workflow_run = _make_workflow_run()
+    workflow_run.initial_context = {}
+    get_status = AsyncMock(return_value={"billing_mode": "v2"})
+    report_usage = AsyncMock(return_value={"metered": True})
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "get_billing_account_status",
+        get_status,
+    )
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_workflow_run_platform_usage(workflow_run)
+
+    report_usage.assert_awaited_once_with(
+        organization_id=42,
+        correlation_id=None,
+        duration_seconds=87.0,
+        workflow_run_id=workflow_run.id,
+        metadata={
+            "source": "workflow_run_completion",
+            "workflow_id": workflow_run.workflow_id,
+            "duration_source": "dograh_usage_info",
+        },
+    )
+
+
+@pytest.mark.asyncio
+async def test_report_workflow_run_platform_usage_skips_non_v2_account(monkeypatch):
+    workflow_run = _make_workflow_run()
+    get_status = AsyncMock(return_value={"billing_mode": "v1"})
+    report_usage = AsyncMock()
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "get_billing_account_status",
+        get_status,
+    )
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_workflow_run_platform_usage(workflow_run)
+
+    get_status.assert_awaited_once_with(organization_id=42)
+    report_usage.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_report_workflow_run_platform_usage_skips_missing_duration_without_correlation(
+    monkeypatch,
+):
+    workflow_run = _make_workflow_run()
+    workflow_run.initial_context = {}
+    workflow_run.usage_info = {}
+    get_status = AsyncMock(return_value={"billing_mode": "v2"})
+    report_usage = AsyncMock()
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "get_billing_account_status",
+        get_status,
+    )
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_workflow_run_platform_usage(workflow_run)
+
+    get_status.assert_not_awaited()
+    report_usage.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_report_workflow_run_platform_usage_skips_oss(monkeypatch):
+    workflow_run = _make_workflow_run()
+    report_usage = AsyncMock()
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "oss")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_workflow_run_platform_usage(workflow_run)
+
+    report_usage.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_report_workflow_run_platform_usage_skips_incomplete(monkeypatch):
+    workflow_run = _make_workflow_run()
+    workflow_run.is_completed = False
+    report_usage = AsyncMock()
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_workflow_run_platform_usage(workflow_run)
+
+    report_usage.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_report_completed_workflow_run_platform_usage_loads_run(monkeypatch):
+    workflow_run = _make_workflow_run()
+    get_run = AsyncMock(return_value=workflow_run)
+    get_status = AsyncMock(return_value={"billing_mode": "v2"})
+    report_usage = AsyncMock(return_value={"metered": True})
+
+    monkeypatch.setattr(workflow_run_billing_mod, "DEPLOYMENT_MODE", "saas")
+    monkeypatch.setattr(
+        workflow_run_billing_mod.db_client,
+        "get_workflow_run_by_id",
+        get_run,
+    )
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "get_billing_account_status",
+        get_status,
+    )
+    monkeypatch.setattr(
+        workflow_run_billing_mod.mps_service_key_client,
+        "report_platform_usage",
+        report_usage,
+    )
+
+    await report_completed_workflow_run_platform_usage(workflow_run.id)
+
+    get_run.assert_awaited_once_with(workflow_run.id)
+    report_usage.assert_awaited_once()