Google Cloud API Gateway 推出统一模型路由功能，支持 Gemini、Claude 与 OpenAI OSS-GPT

Model routing with Google Cloud API Gateway

Mak Ahmad

Sanjay Pujare

Model Routing via API Gateway _ Header

When building AI applications, developers need the freedom to route traffic to the best model for the job without hardcoding endpoints or managing open-source proxies. Google Cloud API Gateway now offers model routing in Public Preview to solve this. It provides a lightweight, serverless ingress layer that accepts OpenAI-compatible requests and dynamically routes them to Gemini, Claude, or OpenAI OSS-GPT. This AI gateway pattern is commonly referred to as an LLM gateway or centralized LLM endpoint.

API Gateway can be used standalone for simple rules-based routing, rate limiting and token tracking, or paired seamlessly with Google Cloud’s broader AI gateway spectrum and Gemini Enterprise Agent Platform. For example, you can route your agent's egress through Agent Gateway for strict security governance, and then pass the request to API Gateway to handle dynamic routing to Google-hosted LLMs. Here is a step-by-step guide on how to configure your routing logic.

Routing your traffic

This gives you a single, stable endpoint for all your LLM traffic, so you can add or swap backend models centrally without changing client code. And because applications authenticate to the Gateway, not to the model providers, client auth stays separate from backend LLM auth, letting you rotate or change backend credentials without touching your apps.

Configure your routing rules: You can map virtual model names to specific backend targets directly in your OpenAPI 3.x specification using the new x-google-api-management extension block.

openapi: 3.0.4

info:
  title: OpenAPI 3.x spec using Model Routing
  description: Using Model Routing in an OAS 3.x spec
  version: 1.0.0

x-google-api-management:
  backends:
    gemini-35-flashlite:
      address: >-
        https://aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/global/publishers/google/models/gemini-3.5-flash-lite:generateContent
      deadline: 60.0
      pathTranslation: CONSTANT_ADDRESS

    anthropic-claude-opus-47:
      address: >-
        https://aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/global/publishers/anthropic/models/claude-opus-4-7:rawPredict
      deadline: 60.0
      pathTranslation: CONSTANT_ADDRESS

    openai-gpt-oss-120b:
      address: >-
        https://aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/global/endpoints/openapi/chat/completions
      deadline: 60.0
      pathTranslation: CONSTANT_ADDRESS

  ai:
    models:
      routing:
        routers:
          # Router 1: route between Gemini (default) and Claude.
          gemini-claude-router:
            defaultModel:
              backend: gemini-35-flashlite
              targetModel: google/gemini-3.5-flash-lite
            rules:
              - model: "claude-opus-4-7"
                backend: anthropic-claude-opus-47
                targetModel: anthropic/claude-opus-4-7

          # Router 2: route between OpenAI GPT (default) and Gemini.
          openai-gemini-router:
            defaultModel:
              backend: openai-gpt-oss-120b
              targetModel: openai/gpt-oss-120b-maas
            rules:
              - model: "gemini-3.5-flash-lite"
                backend: gemini-35-flashlite
                targetModel: google/gemini-3.5-flash-lite

servers:
  - url: "https://my-gateway.example.com"

paths:
  /v1/chat/gemini-claude:
    post:
      summary: "Endpoint:defaults to Gemini & Claude as an option."
      operationId: "chatGeminiClaude"
      x-google-model-router: gemini-claude-router
      responses:
        '200':
          description: "OK"

  /v1/chat/openai-gemini:
    post:
      summary: "Endpoint:defaults to OpenAI & Gemini as an option."
      operationId: "chatOpenAIGemini"
      x-google-model-router: openai-gemini-router
      responses:
        '200':
          description: "OK"

Note: All backends referenced by a single router must share the same host (for example, aiplatform.googleapis.com). Routing selects a different model and path on that shared Agent Platform host — it does not route across different hosts.

2. Deploy the Gateway: Deploy your updated API config so the Gateway is active and ready to process traffic.

3. Send standard requests: Your application simply sends a standard OpenAI POST /v1/chat/gemini-claude or POST /v1/chat/openai-gemini request. The Gateway intercepts it, transcodes the payload to the native schema of the backend, adds the required agent platform authentication token, and routes it on the fly. As an example (use appropriate values for $API_KEY and my-gateway.example.com) :

curl -X POST "https://my-gateway.example.com/v1/chat/gemini-claude" \
  -H "content-type: application/json" \
  -H "x-api-key: $API_KEY" \
  -d '{
        "model": "claude-opus-4-7",
        "messages": [
          {"role": "user", "content": "Introduce yourself in 5 words"}
        ]
      }'

Model routing is now available in Public Preview for API Gateway. To stop managing proxies and start unifying your AI traffic, check out our documentation to deploy your first model router today.

These model routing capabilities are part of Google Cloud's broader AI gateway spectrum: expansive API and tools management with Apigee, and end-to-end agent governance with Agent Gateway in the Gemini Enterprise Agent Platform, so you can start lightweight and grow into the rest when you need it.