aigw-backend

name	aigw-backend
description	Create an AIServiceBackend and Envoy Gateway Backend for an AI provider
arguments	[{"name":"BackendName","description":"Name for the AIServiceBackend and Backend resources","required":true},{"name":"Schema","description":"API schema: OpenAI, Anthropic, AWSBedrock, AzureOpenAI, GCPVertexAI, Cohere, etc.","required":true},{"name":"Hostname","description":"FQDN or hostname for the backend (e.g., api.openai.com, bedrock-runtime.us-east-1.amazonaws.com)","required":true},{"name":"Port","description":"Port number (default: 443 for HTTPS)","required":false}]

Create an AIServiceBackend and the corresponding Envoy Gateway Backend resource. The AIServiceBackend defines the API schema (OpenAI, Anthropic, AWS Bedrock, etc.) and must reference an Envoy Gateway Backend via backendRef. It cannot reference a Kubernetes Service directly—use a Backend with FQDN endpoints (e.g., my-svc.default.svc.cluster.local) for in-cluster targets.

Instructions

Step 1: Create the Backend (Envoy Gateway)

The Backend specifies the external endpoint. For cloud providers, use HTTPS (port 443):

apiVersion: gateway.envoyproxy.io/v1alpha1
kind: Backend
metadata:
  name: ${BackendName}  # TODO: Replace with your backend name
  namespace: default
spec:
  endpoints:
    - fqdn:
        hostname: ${Hostname}  # TODO: e.g., api.openai.com, bedrock-runtime.us-east-1.amazonaws.com
        port: ${Port}  # TODO: 443 for HTTPS

Step 2: Create the AIServiceBackend

apiVersion: aigateway.envoyproxy.io/v1alpha1
kind: AIServiceBackend
metadata:
  name: ${BackendName}
  namespace: default
spec:
  schema:
    name: ${Schema}  # TODO: OpenAI, Anthropic, AWSBedrock, AzureOpenAI, GCPVertexAI, Cohere, etc.
  backendRef:
    name: ${BackendName}
    kind: Backend
    group: gateway.envoyproxy.io

Step 3: Add BackendTLSPolicy for HTTPS backends

For external HTTPS endpoints, attach a BackendTLSPolicy (use gateway.networking.k8s.io/v1 with Envoy Gateway v1.6+):

apiVersion: gateway.networking.k8s.io/v1
kind: BackendTLSPolicy
metadata:
  name: ${BackendName}-tls
  namespace: default
spec:
  targetRefs:
    - group: gateway.envoyproxy.io
      kind: Backend
      name: ${BackendName}
  validation:
    wellKnownCACertificates: "System"
    hostname: ${Hostname}  # Must match the Backend hostname

Step 4: Schema-specific notes

Schema	Hostname examples	Notes
OpenAI	api.openai.com
Anthropic	api.anthropic.com
AWSBedrock	bedrock-runtime.us-east-1.amazonaws.com	Region in hostname
AzureOpenAI	your-resource.openai.azure.com
GCPVertexAI	{region}-aiplatform.googleapis.com	Requires BackendSecurityPolicy for region/project
Cohere	api.cohere.ai
GCPAnthropic	{region}-aiplatform.googleapis.com	Anthropic on Vertex AI
AWSAnthropic	bedrock-runtime.us-east-1.amazonaws.com	Anthropic on Bedrock

Step 5: In-cluster backend (Kubernetes Service via Backend)

For a self-hosted model served by a Kubernetes Service, create a Backend with FQDN endpoints pointing to the service DNS. AIServiceBackend always references Backend, never Service directly:

apiVersion: gateway.envoyproxy.io/v1alpha1
kind: Backend
metadata:
  name: my-ollama-backend
  namespace: default
spec:
  endpoints:
    - fqdn:
        hostname: my-ollama-service.default.svc.cluster.local
        port: 80
---
apiVersion: aigateway.envoyproxy.io/v1alpha1
kind: AIServiceBackend
metadata:
  name: my-ollama-backend
  namespace: default
spec:
  schema:
    name: OpenAI
  backendRef:
    name: my-ollama-backend
    kind: Backend
    group: gateway.envoyproxy.io

Step 6: Custom prefix (e.g., Gemini OpenAI-compatible)

For backends with non-standard prefixes (e.g., Gemini uses /v1beta/openai):

spec:
  schema:
    name: OpenAI
    prefix: "/v1beta/openai"
  backendRef:
    name: my-vertex-backend
    kind: Backend
    group: gateway.envoyproxy.io

Step 7: Header and body mutation

Add header or body mutations at the backend level:

spec:
  schema:
    name: OpenAI
  backendRef:
    name: my-backend
    kind: Backend
    group: gateway.envoyproxy.io
  headerMutation:
    set:
      - name: X-Custom-Header
        value: "custom-value"
  bodyMutation:
    set:
      - path: "model"
        value: "\"gpt-4o\""

Checklist

• Backend created first; AIServiceBackend.backendRef references it (not K8s Service)
• Backend hostname and port correct
• AIServiceBackend schema matches provider API
• BackendTLSPolicy for HTTPS external endpoints
• BackendSecurityPolicy attached for cloud provider auth (see /aigw-auth)
• Prefix set if backend uses non-standard path