From envoy-ai-gateway-adopters
Create an AIServiceBackend and Envoy Gateway Backend for an AI provider
How this skill is triggered — by the user, by Claude, or both
Slash command
/envoy-ai-gateway-adopters:aigw-backendThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
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
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
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
| 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 |
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
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
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\""
/aigw-auth)npx claudepluginhub missberg/envoy-skills --plugin envoy-ai-gateway-adoptersConfigures Azure API Management as an AI Gateway to govern AI models, MCP tools, and agents with policies for semantic caching, token limits, content safety, and load balancing.
Configures TrueFoundry AI Gateway for unified OpenAI-compatible LLM access, provider account integrations, content safety guardrails, and request observability (traces, costs, errors).
Provides expert guidance for Vercel AI Gateway configuration: model routing, provider failover, cost tracking, unified API for multiple AI providers like OpenAI, Anthropic, Gemini.