Feat(aigw): alternate vertex claude with gemini

app/_how-tos/ai-gateway/use-claude-code-with-ai-gateway-vertex.md

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+---
+title: Route Claude CLI traffic through {{site.ai_gateway}} and Vertex AI
+permalink: /ai-gateway/use-claude-code-with-ai-gateway-vertex/
+content_type: how_to
+
+related_resources:
+  - text: "{{site.ai_gateway}}"
+    url: /ai-gateway/
+
+description: Configure {{site.ai_gateway}} to proxy Claude CLI traffic using Google Vertex AI models
+
+products:
+  - ai-gateway
+
+works_on:
+  - konnect
+
+tools:
+  - kongctl
+
+min_version:
+  ai-gateway: '2.0'
+
+tags:
+  - ai
+  - vertex-ai
+
+tldr:
+  q: How do I run Claude CLI through {{site.ai_gateway}} against a Vertex AI model?
+  a: Create an AI Provider entity to store your GCP service account credentials, create an AI Model entity with an Anthropic-compatible format that routes to a Gemini model on Vertex AI through that provider, then point Claude CLI's `ANTHROPIC_BASE_URL` at your local {{site.ai_gateway}} endpoint so all LLM requests pass through the gateway for monitoring and control.
+
+prereqs:
+  inline:
+    - title: Vertex
+      content: |
+        Before you begin, you must get the following credentials from Google Cloud:
+
+        - **Service Account Key**: A JSON key file for a service account with Vertex AI permissions
+        - **Project ID**: Your Google Cloud project identifier
+        - **Location ID**: The region where your Vertex AI endpoint is deployed (for example, `us-central1`)
+        - **API Endpoint**: The Vertex AI API endpoint URL (typically `https://{location}-aiplatform.googleapis.com`)
+
+        Export these values as environment variables:
+        ```sh
+        export GEMINI_API_KEY="<your_gemini_api_key>"
+        export GCP_PROJECT_ID="<your-gemini-project-id>"
+        export GEMINI_LOCATION_ID="<your-gemini-location_id>"
+        export GEMINI_API_ENDPOINT="<your_gemini_api_endpoint>"
+        ```
+      icon_url: /assets/icons/vertex.svg
+    - title: Claude Code CLI
+      icon_url: /assets/icons/third-party/claude.svg
+      include_content: prereqs/claude-code
+
+---
+
+## Create an AI Provider entity
+
+Create an [AI Model Provider](/ai-gateway/entities/ai-model-provider/) entity to define your connection to Vertex AI and store your GCP service account credentials:
+
+```sh
+kongctl apply -f - --auto-approve --pat "$KONNECT_TOKEN" <<EOF
+_defaults:
+  kongctl:
+    namespace: ai-gateway-get-started
+
+ai_gateways:
+  - ref: ai-quickstart
+    name: ai-quickstart
+    display_name: "ai-quickstart"
+
+ai_gateway_model_providers:
+  - ref: my-vertex-account
+    ai_gateway: ai-quickstart
+    name: my-vertex-account
+    display_name: "my-vertex-account"
+    type: vertex
+    config:
+      auth:
+        type: gcp
+        use_gcp_service_account: true
+        service_account_json: !env GEMINI_API_KEY
+EOF
+```
+
+In this example, we're setting up the AI Model Provider with:
+
+* `type: vertex`: Specifies that this provider connects to Google Cloud Vertex AI.
+* `name: my-vertex-account`: A unique identifier that AI Models will reference to route requests through this provider.
+* `config.auth`: Authenticates to Vertex AI with a GCP service account. `use_gcp_service_account: true` tells {{site.ai_gateway}} to authenticate using service account credentials, and `service_account_json: !env GEMINI_API_KEY` loads the service account JSON key from your environment at apply time instead of embedding it in the YAML. `kongctl` redacts it in plan and diff output. {{site.ai_gateway}} securely manages this credential and injects it into upstream requests automatically, eliminating the need for clients to pass credentials.
+
+## Create an AI Model entity
+
+Create an [AI Model](/ai-gateway/entities/ai-model/) entity to declare which upstream models are available, configure how client requests are routed, and specify which AI Provider to use:
+
+```sh
+kongctl apply -f - --auto-approve --pat "$KONNECT_TOKEN" <<EOF
+_defaults:
+  kongctl:
+    namespace: ai-gateway-get-started
+
+ai_gateways:
+  - ref: ai-quickstart
+    name: ai-quickstart
+    display_name: "ai-quickstart"
+
+ai_gateway_models:
+  - ref: my-claude-vertex
+    ai_gateway: ai-quickstart
+    name: my-claude-vertex
+    display_name: "my-claude-vertex"
+    type: model
+    formats:
+      - type: anthropic
+    config:
+      route:
+        paths:
+          - /
+      model:
+        alias: my-claude-vertex
+    targets:
+      - name: gemini-2.5-flash
+        provider: my-vertex-account
+        config:
+          type: vertex
+          api_endpoint: !env GEMINI_API_ENDPOINT
+          project_id: !env GCP_PROJECT_ID
+          location_id: !env GEMINI_LOCATION_ID
+    policies: []
+    capabilities:
+      - generate
+EOF
+```
+
+In this example, we're setting up the AI Model with:
+
+* `type: model`: Specifies this is a synchronous model for request/response workloads.
+* `name: my-claude-vertex`: A unique identifier for this model.
+* `formats: [type: anthropic]`: Declares that this model accepts requests in Anthropic-compatible format, matching what {{ site.claude_code }} sends natively, even though the upstream model is a Gemini model on Vertex AI.
+* `config.route.paths: [/]`: Configures the custom base path where this model's Routes will be accessible. Setting this to a unique value avoids clashes when you have multiple AI Models.
+* `capabilities: [generate]`: Enables the text generation capability. For a model using the `anthropic` format, the `generate` capability creates a `/messages` endpoint matching Anthropic's native Messages API, so combined with your base path, clients send requests to `/v1/messages`.
+* `targets`: Specifies which upstream AI Provider model to route requests to. Here, `provider: my-vertex-account` references the AI Provider we created earlier, and `name: gemini-2.5-flash` specifies which Gemini model to call upstream. `config.api_endpoint`, `config.project_id`, and `config.location_id` identify the specific Vertex AI deployment to route requests to.
+
+## Verify traffic through Kong
+
+Now, we can start a {{ site.claude_code }} session that points it to the local {{site.ai_gateway}} endpoint:
+
+```sh
+ANTHROPIC_BASE_URL=http://localhost:8000/ ANTHROPIC_MODEL=my-claude-vertex claude
+```
+
+{{ site.claude_code }} asks for permission before it runs tools or interacts with files:
+
+```text
+I'll need permission to work with your files.
+
+This means I can:
+- Read any file in this folder
+- Create, edit, or delete files
+- Run commands (like npm, git, tests, ls, rm)
+- Use tools defined in .mcp.json
+
+Learn more ( https://docs.claude.com/s/claude-code-security )
+
+❯ 1. Yes, continue
+2. No, exit
+```
+{:.no-copy-code}
+
+Select **Yes, continue**. The session starts. Ask a simple question to confirm that requests reach {{site.ai_gateway}}.
+
+```text
+Tell me about Anna Komnene's Alexiad.
+```
+
+{{ site.claude_code }} might prompt you approve its web search for answering the question. When you select **Yes**, {{ site.claude }} will produce a full-length response to your request:
+
+```text
+Anna Komnene (1083-1153?) was a Byzantine princess, scholar, physician,
+hospital administrator, and historian. She is known for writing the
+Alexiad, a historical account of the reign of her father, Emperor Alexios
+I Komnenos (r. 1081-1118). The Alexiad is a valuable primary source for
+understanding Byzantine history and the First Crusade.
+```
+{:.no-copy-code}