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Install in Kubernetes

This guide provides step-by-step instructions for deploying the vLLM Semantic Router with Envoy AI Gateway on Kubernetes.

Architecture Overview​

The deployment consists of:

  • vLLM Semantic Router: Provides intelligent request routing and semantic understanding
  • Envoy Gateway: Core gateway functionality and traffic management
  • Envoy AI Gateway: AI Gateway built on Envoy Gateway for LLM providers
  • Gateway API Inference Extension: CRDs for managing inference pools

Prerequisites​

Before starting, ensure you have the following tools installed:

  • kind - Kubernetes in Docker (Optional)
  • kubectl - Kubernetes CLI
  • Helm - Package manager for Kubernetes

Step 1: Create Kind Cluster (Optional)​

Create a local Kubernetes cluster optimized for the semantic router workload:

# Create cluster with optimized resource settings
kind create cluster --name semantic-router-cluster --config tools/kind/kind-config.yaml

# Verify cluster is ready
kubectl wait --for=condition=Ready nodes --all --timeout=300s

Note: The kind configuration provides sufficient resources (8GB+ RAM, 4+ CPU cores) for running the semantic router and AI gateway components.

Step 2: Deploy vLLM Semantic Router​

Configure the semantic router by editing deploy/kubernetes/config.yaml. This file contains the vLLM configuration, including model config, endpoints, and policies.

Deploy the semantic router service with all required components:

# Deploy semantic router using Kustomize
kubectl apply -k deploy/kubernetes/

# Wait for deployment to be ready (this may take several minutes for model downloads)
kubectl wait --for=condition=Available deployment/semantic-router -n vllm-semantic-router-system --timeout=600s

# Verify deployment status
kubectl get pods -n vllm-semantic-router-system

Step 3: Install Envoy Gateway​

Install the core Envoy Gateway for traffic management:

# Install Envoy Gateway using Helm
helm upgrade -i eg oci://docker.io/envoyproxy/gateway-helm \
    --version v0.0.0-latest \
    --namespace envoy-gateway-system \
    --create-namespace

# Wait for Envoy Gateway to be ready
kubectl wait --timeout=300s -n envoy-gateway-system deployment/envoy-gateway --for=condition=Available

Step 4: Install Envoy AI Gateway​

Install the AI-specific extensions for inference workloads:

# Install Envoy AI Gateway using Helm
helm upgrade -i aieg oci://docker.io/envoyproxy/ai-gateway-helm \
    --version v0.0.0-latest \
    --namespace envoy-ai-gateway-system \
    --create-namespace

# Wait for AI Gateway Controller to be ready
kubectl wait --timeout=300s -n envoy-ai-gateway-system deployment/ai-gateway-controller --for=condition=Available

Step 5: Install Gateway API Inference Extension​

Install the Custom Resource Definitions (CRDs) for managing inference pools:

# Install Gateway API Inference Extension CRDs
kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v0.5.1/manifests.yaml

# Verify CRDs are installed
kubectl get crd | grep inference

Step 6: Configure AI Gateway​

Apply the AI Gateway configuration to connect with the semantic router:

# Apply AI Gateway configuration
kubectl apply -f deploy/kubernetes/ai-gateway/configuration

# Restart controllers to pick up new configuration
kubectl rollout restart -n envoy-gateway-system deployment/envoy-gateway
kubectl rollout restart -n envoy-ai-gateway-system deployment/ai-gateway-controller

# Wait for controllers to be ready
kubectl wait --timeout=120s -n envoy-gateway-system deployment/envoy-gateway --for=condition=Available
kubectl wait --timeout=120s -n envoy-ai-gateway-system deployment/ai-gateway-controller --for=condition=Available

Step 7: Create Inference Pool​

Create the inference pool that connects the gateway to the semantic router backend:

# Create inference pool configuration
kubectl apply -f deploy/kubernetes/ai-gateway/inference-pool

# Wait for inference pool to be ready
sleep 30

Step 8: Verify Deployment​

Verify that the inference pool has been created and is properly configured:

# Check inference pool status
kubectl get inferencepool vllm-semantic-router -n vllm-semantic-router-system -o yaml

Expected output should show the inference pool in Accepted state:

status:
  parent:
  - conditions:
    - lastTransitionTime: "2025-09-27T09:27:32Z"
      message: 'InferencePool has been Accepted by controller ai-gateway-controller:
        InferencePool reconciled successfully'
      observedGeneration: 1
      reason: Accepted
      status: "True"
      type: Accepted
    - lastTransitionTime: "2025-09-27T09:27:32Z"
      message: 'Reference resolution by controller ai-gateway-controller: All references
        resolved successfully'
      observedGeneration: 1
      reason: ResolvedRefs
      status: "True"
      type: ResolvedRefs
    parentRef:
      group: gateway.networking.k8s.io
      kind: Gateway
      name: vllm-semantic-router
      namespace: vllm-semantic-router-system

Testing the Deployment​

Set up port forwarding to access the gateway locally:

# Set up environment variables
export GATEWAY_IP="localhost:8080"

# Get the Envoy service name
export ENVOY_SERVICE=$(kubectl get svc -n envoy-gateway-system \
    --selector=gateway.envoyproxy.io/owning-gateway-namespace=vllm-semantic-router-system,gateway.envoyproxy.io/owning-gateway-name=vllm-semantic-router \
    -o jsonpath='{.items[0].metadata.name}')

# Start port forwarding (run in background or separate terminal)
kubectl port-forward -n envoy-gateway-system svc/$ENVOY_SERVICE 8080:80

Method 2: External IP (For Production Deployments)​

For production deployments with external load balancers:

# Get the Gateway external IP
GATEWAY_IP=$(kubectl get gateway vllm-semantic-router -n vllm-semantic-router-system -o jsonpath='{.status.addresses[0].value}')
echo "Gateway IP: $GATEWAY_IP"

Send Test Requests​

Once the gateway is accessible, test the inference endpoint:

# Test math domain chat completions endpoint
curl -i -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [
      {"role": "user", "content": "What is the derivative of f(x) = x^3 + 2x^2 - 5x + 7?"}
    ]
  }'

Troubleshooting​

Common Issues​

Gateway not accessible:

# Check gateway status
kubectl get gateway vllm-semantic-router -n vllm-semantic-router-system

# Check Envoy service
kubectl get svc -n envoy-gateway-system

Inference pool not ready:

# Check inference pool events
kubectl describe inferencepool vllm-semantic-router -n vllm-semantic-router-system

# Check AI gateway controller logs
kubectl logs -n envoy-ai-gateway-system deployment/ai-gateway-controller

Semantic router not responding:

# Check semantic router pod status
kubectl get pods -n vllm-semantic-router-system

# Check semantic router logs
kubectl logs -n vllm-semantic-router-system deployment/semantic-router

Cleanup​

To remove the entire deployment:

# Remove inference pool
kubectl delete -f deploy/kubernetes/ai-gateway/inference-pool

# Remove AI gateway configuration
kubectl delete -f deploy/kubernetes/ai-gateway/configuration

# Remove semantic router
kubectl delete -k deploy/kubernetes/

# Remove AI gateway
helm uninstall aieg -n envoy-ai-gateway-system

# Remove Envoy gateway
helm uninstall eg -n envoy-gateway-system

# Remove Gateway API CRDs (optional)
kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v0.5.1/manifests.yaml

# Delete kind cluster
kind delete cluster --name semantic-router-cluster

Next Steps​

  • Configure custom routing rules in the AI Gateway
  • Set up monitoring and observability
  • Implement authentication and authorization
  • Scale the semantic router deployment for production workloads