> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://www.respan.ai/docs/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://www.respan.ai/docs/_mcp/server.

# Provider: Google Vertex AI

> **Note**
>
> This page is for **Respan LLM Gateway** users.

Use Respan Gateway to call Google Vertex AI models (`vertex_ai/claude-opus-4-8@default` and Gemini on Vertex) while keeping unified observability (logs, cost, latency, reliability) in Respan.

> **Note**
>
> Google Vertex AI requires **bring your own key (BYOK)**. Provider usage cannot be paid with Respan credits.

## Quick setup

#### Get a Respan API key

[Sign up](https://platform.respan.ai) and create a key on the [API keys page](https://platform.respan.ai/platform/gateway/api-keys).

#### Add your Google Vertex AI credentials

Configure your Google Vertex AI credentials on the [Providers page](https://platform.respan.ai/platform/gateway/api-keys?providers=1) before using the examples below. See [Use your own Google Vertex AI key](#use-your-own-google-vertex-ai-key-byok).

## Send your first request

After configuring your Google Vertex AI credentials in Respan, pick the integration that matches your stack. The base URL is `https://api.respan.ai/api` and your application authenticates with `RESPAN_API_KEY`.

#### Vercel AI SDK

Call `respan.chat(...)` explicitly to use the Gateway Chat Completions endpoint.

Use `@ai-sdk/openai` and point `createOpenAI` at the Respan gateway. The example uses the live `vertex_ai/claude-opus-4-8@default` catalog entry. Enable that model in your Vertex project and choose a supported region.

**`TypeScript`**

```typescript TypeScript
import { createOpenAI } from "@ai-sdk/openai";
import { generateText } from "ai";

const respan = createOpenAI({
  apiKey: process.env.RESPAN_API_KEY!,
  baseURL: "https://api.respan.ai/api",
});

const result = await generateText({
  model: respan.chat("vertex_ai/claude-opus-4-8@default"),
  prompt: "Hello, Vertex AI!",
});
console.log(result.text);
```

#### Respan API

Call the Respan REST API directly from any language.

**`Python`**

```python Python
import requests

response = requests.post(
    "https://api.respan.ai/api/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_RESPAN_API_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "vertex_ai/claude-opus-4-8@default",
        "messages": [{"role": "user", "content": "Hello, Vertex AI!"}],
    },
)
print(response.json()["choices"][0]["message"]["content"])
```

**`TypeScript`**

```typescript TypeScript
const response = await fetch("https://api.respan.ai/api/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.RESPAN_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "vertex_ai/claude-opus-4-8@default",
    messages: [{ role: "user", content: "Hello, Vertex AI!" }],
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);
```

## More integrations

Vertex AI models work with every Respan gateway integration:

* [OpenAI SDK](/docs/integrations/openai-sdk)
* [LangChain](/docs/integrations/langchain)
* [LangGraph](/docs/integrations/langgraph)
* [LlamaIndex](/docs/integrations/llama-index)
* [Pydantic AI](/docs/integrations/pydantic-ai)
* [Respan native (OTel)](/docs/documentation/features/gateway/gateway-quickstart)

## Switch models

Change the `model` parameter to call any supported model through the same client. Use the `vertex_ai/` prefix to disambiguate when routing across providers. Browse the full list on the [Models page](https://platform.respan.ai/platform/models).

```python
client.chat.completions.create(model="vertex_ai/claude-opus-4-8@default", messages=messages)
client.chat.completions.create(model="vertex_ai/gemini-3.5-flash", messages=messages)
client.chat.completions.create(model="vertex_ai/gemini-3.1-flash-lite", messages=messages)
client.chat.completions.create(model="openai/gpt-6-sol", messages=messages)
client.chat.completions.create(model="anthropic/claude-opus-5-5", messages=messages)
```

## Use your own Google Vertex AI key (BYOK)

Google Vertex AI credentials are required. Configure them in Respan or pass them with each request using one of the options below.

#### Global (UI)

Google credentials can be tricky. Follow this walkthrough if you need help finding the required fields:

#### Open Providers

Go to the [Providers page](https://platform.respan.ai/platform/gateway/api-keys?providers=1).

#### Add Google Vertex AI

Select **Google Vertex AI** and fill in the required credential fields:

* `vertex_ai_project` (your Google Cloud project ID)
* `vertex_ai_location` (the Vertex AI region to use)
* `vertex_ai_credentials` (your Google service-account or application-default credential JSON object)

#### Load balancing (Optional)

Add multiple credential sets and use **Load balancing weight** to distribute traffic across them.

#### Per-request (Code)

Pass `customer_credentials` on each [Gateway request](/docs/apis/gateway/create-chat-completion). Useful when serving end-user keys.

```json
{
  "customer_credentials": {
    "google_vertex_ai": {
      "vertex_ai_project": "your-project",
      "vertex_ai_location": "us-central1",
      "vertex_ai_credentials": {
        "type": "service_account",
        "project_id": "your-project"
      }
    }
  }
}
```

### Override credentials per model (Optional)

Use [`credential_override`](/docs/documentation/admin/llm_provider_keys#per-model-credential-override) when one model on a request should use a different Vertex project than the default.

```json
{
  "customer_credentials": {
    "google_vertex_ai": {
      "vertex_ai_project": "your-project",
      "vertex_ai_location": "us-central1",
      "vertex_ai_credentials": {
        "type": "service_account",
        "project_id": "your-project"
      }
    }
  },
  "credential_override": {
    "vertex_ai/claude-opus-4-8@default": {
      "vertex_ai_project": "ANOTHER_VERTEX_AI_PROJECT",
      "vertex_ai_location": "ANOTHER_VERTEX_AI_LOCATION",
      "vertex_ai_credentials": {
        "type": "service_account",
        "project_id": "another-project"
      }
    }
  }
}
```

## Log without proxying (Optional)

Already calling Vertex AI directly? Send logs to Respan asynchronously to track cost, latency, and performance for those external calls.

```python
import requests

requests.post(
    "https://api.respan.ai/api/request-logs/create/",
    headers={
        "Authorization": "Bearer YOUR_RESPAN_API_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "vertex_ai/claude-opus-4-8@default",
        "prompt_messages": [{"role": "user", "content": "Hello, how are you?"}],
        "completion_message": {"role": "assistant", "content": "Hello from Vertex AI through Respan."},
        "cost": 0.001,
        "generation_time": 1.2,
        "customer_params": {"customer_identifier": "user_123"},
    },
)
```

See the [logging guide](/docs/apis/spans/create-span) for the full setup.