Model Routing Service Demo

Plano is an AI-native proxy and data plane for agentic apps — with built-in orchestration, safety, observability, and intelligent LLM routing.

┌───────────┐      ┌─────────────────────────────────┐      ┌──────────────┐
│  Client   │ ───► │  Plano                          │ ───► │  OpenAI      │
│  (any     │      │                                 │      │  Anthropic   │
│  language)│      │  Arch-Router (1.5B model)       │      │  Any Provider│
└───────────┘      │  analyzes intent → picks model  │      └──────────────┘
                   └─────────────────────────────────┘

One endpoint, many models — apps call Plano using standard OpenAI/Anthropic APIs; Plano handles provider selection, keys, and failover
Intelligent routing — a lightweight 1.5B router model classifies user intent and picks the best model per request
Platform governance — centralize API keys, rate limits, guardrails, and observability without touching app code
Runs anywhere — single binary; self-host the router for full data privacy

How Routing Works

The entire routing configuration is plain YAML — no code:

model_providers:
  - model: openai/gpt-4o-mini
    default: true                    # fallback for unmatched requests

  - model: openai/gpt-4o
    routing_preferences:
      - name: complex_reasoning
        description: complex reasoning tasks, multi-step analysis

  - model: anthropic/claude-sonnet-4-20250514
    routing_preferences:
      - name: code_generation
        description: generating new code, writing functions

When a request arrives, Plano sends the conversation and routing preferences to Arch-Router, which classifies the intent and returns the matching route:

1. Request arrives          → "Write binary search in Python"
2. Preferences serialized   → [{"name":"code_generation", ...}, {"name":"complex_reasoning", ...}]
3. Arch-Router classifies   → {"route": "code_generation"}
4. Route → Model lookup     → code_generation → anthropic/claude-sonnet-4-20250514
5. Request forwarded        → Claude generates the response

No match? Arch-Router returns other → Plano falls back to the default model.

The /routing/v1/* endpoints return the routing decision without forwarding to the LLM — useful for testing and validating routing behavior before going to production.

Setup

Make sure you have Plano CLI installed (pip install planoai or uv tool install planoai).

export OPENAI_API_KEY=<your-key>
export ANTHROPIC_API_KEY=<your-key>

Start Plano:

cd demos/llm_routing/model_routing_service
planoai up config.yaml

Run the demo

./demo.sh

Endpoints

All three LLM API formats are supported:

Endpoint	Format
`POST /routing/v1/chat/completions`	OpenAI Chat Completions
`POST /routing/v1/messages`	Anthropic Messages
`POST /routing/v1/responses`	OpenAI Responses API

Example

curl http://localhost:12000/routing/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "Write a Python function for binary search"}]
  }'

Response:

{
    "model": "anthropic/claude-sonnet-4-20250514",
    "route": "code_generation",
    "trace_id": "c16d1096c1af4a17abb48fb182918a88"
}

The response tells you which model would handle this request and which route was matched, without actually making the LLM call.

Kubernetes Deployment (Self-hosted Arch-Router on GPU)

To run Arch-Router in-cluster using vLLM instead of the default hosted endpoint:

0. Check your GPU node labels and taints

kubectl get nodes --show-labels | grep -i gpu
kubectl get node <gpu-node-name> -o jsonpath='{.spec.taints}'

GPU nodes commonly have a nvidia.com/gpu:NoSchedule taint — vllm-deployment.yaml includes a matching toleration. If you have multiple GPU node pools and need to pin to a specific one, uncomment and set the nodeSelector in vllm-deployment.yaml using the label for your cloud provider.

1. Deploy Arch-Router and Plano:


# arch-router deployment
kubectl apply -f vllm-deployment.yaml

# plano deployment
kubectl create secret generic plano-secrets \
  --from-literal=OPENAI_API_KEY=$OPENAI_API_KEY \
  --from-literal=ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY

kubectl create configmap plano-config \
  --from-file=plano_config.yaml=config_k8s.yaml \
  --dry-run=client -o yaml | kubectl apply -f -

kubectl apply -f plano-deployment.yaml

3. Wait for both pods to be ready:

# Arch-Router downloads the model (~1 min) then vLLM loads it (~2 min)
kubectl get pods -l app=arch-router -w
kubectl rollout status deployment/plano

4. Test:

kubectl port-forward svc/plano 12000:12000
./demo.sh

To confirm requests are hitting your in-cluster Arch-Router (not just health checks):

kubectl logs -l app=arch-router -f --tail=0
# Look for POST /v1/chat/completions entries

Updating the config:

kubectl create configmap plano-config \
  --from-file=plano_config.yaml=config_k8s.yaml \
  --dry-run=client -o yaml | kubectl apply -f -
kubectl rollout restart deployment/plano

Demo Output

=== Model Routing Service Demo ===

--- 1. Code generation query (OpenAI format) ---
{
    "model": "anthropic/claude-sonnet-4-20250514",
    "route": "code_generation",
    "trace_id": "c16d1096c1af4a17abb48fb182918a88"
}

--- 2. Complex reasoning query (OpenAI format) ---
{
    "model": "openai/gpt-4o",
    "route": "complex_reasoning",
    "trace_id": "30795e228aff4d7696f082ed01b75ad4"
}

--- 3. Simple query - no routing match (OpenAI format) ---
{
    "model": "none",
    "route": null,
    "trace_id": "ae0b6c3b220d499fb5298ac63f4eac0e"
}

--- 4. Code generation query (Anthropic format) ---
{
    "model": "anthropic/claude-sonnet-4-20250514",
    "route": "code_generation",
    "trace_id": "26be822bbdf14a3ba19fe198e55ea4a9"
}

=== Demo Complete ===

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