> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wafer.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Router Setup

> Use Wafer through Vercel AI Gateway, OpenRouter, and TrueFoundry AI Gateway.

Use these setup paths when your app already sends traffic through a model router or gateway. The router owns the API key, base URL, model slug, billing, and routing policy; Wafer serves the underlying model capacity.

For direct Wafer Serverless API usage, use [API Reference](/serverless/api-reference). For coding agents and local harnesses, use [Agent Setup](/serverless/setup).

## Vercel

Wafer is available through [Vercel AI Gateway](https://vercel.com/ai-gateway/models/glm-5.2-fast) for `GLM 5.2 Fast`.

Use the Vercel AI SDK model slug:

```ts theme={null}
import { streamText } from "ai";

const result = streamText({
  model: "zai/glm-5.2-fast",
  prompt: "Why is the sky blue?",
});
```

See Vercel's [GLM 5.2 Fast via Wafer changelog](https://vercel.com/changelog/glm-5-2-fast-via-wafer-now-available-on-ai-gateway) for provider-specific details.

## OpenRouter

Wafer is listed as a provider on [OpenRouter](https://openrouter.ai/provider/wafer).

Use OpenRouter when you want Wafer models behind OpenRouter's unified API key, catalog, usage tracking, and routing controls.

OpenRouter model slugs are model-level slugs, not provider-specific slugs. To prefer `GLM 5.2 Fast` on Wafer, use [`z-ai/glm-5.2`](https://openrouter.ai/z-ai/glm-5.2) and put the Wafer Fast provider endpoint first with `provider.order: ["wafer/fast"]`.

<Warning>
  Use the full endpoint slug `wafer/fast`. The base slug `wafer` matches all Wafer endpoints for this model, including the standard Wafer endpoint. Fallbacks remain enabled, so OpenRouter can route to another available endpoint if Wafer Fast is unavailable.
</Warning>

```bash theme={null}
curl -sS "https://openrouter.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer <YOUR_OPENROUTER_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "z-ai/glm-5.2",
    "provider": {
      "order": ["wafer/fast"]
    },
    "messages": [
      {"role": "user", "content": "Reply with the single word: ready."}
    ]
  }'
```

See OpenRouter's [Quickstart](https://openrouter.ai/docs/quickstart) for the API shape and [Provider Routing](https://openrouter.ai/docs/features/provider-routing) for provider selection controls.

## TrueFoundry

[TrueFoundry AI Gateway](https://www.truefoundry.com/ai-gateway) is the proxy layer that sits between your applications and the LLM providers and MCP Servers. It is an enterprise-grade platform that enables users to access 1000+ LLMs using a unified interface while taking care of observability and governance.

Wafer is available natively in TrueFoundry AI Gateway. See TrueFoundry's [Wafer provider docs](https://www.truefoundry.com/docs/ai-gateway/wafer#wafer) for the source setup flow.

<Steps>
  <Step title="Add a Wafer account">
    In TrueFoundry, go to **AI Gateway** → **Models**, select **Wafer**, and add your Wafer account.

    The default Wafer base URL is `https://pass.wafer.ai/v1`. Change it only if Wafer directs you to a different endpoint. Add your Wafer API key for authentication. Zero Data Retention is enabled by default; when enabled, TrueFoundry sends `Wafer-ZDR: required` on requests.

    Add collaborators to the account if other users or teams should be able to access it.
  </Step>

  <Step title="Register models">
    Click **+ Add Models** and register the Wafer model IDs you want TrueFoundry to expose. For each model, set a display name, the exact Wafer model ID, and select **Chat** as the model type.

    Common Wafer model IDs include `deepseek-v4-flash` and `deepseek-v4-pro`.
  </Step>

  <Step title="Call the gateway">
    Use the TrueFoundry model ID format `your-wafer-account/model-display-name` through TrueFoundry's OpenAI-compatible `/chat/completions` API:

    ```python theme={null}
    from openai import OpenAI

    client = OpenAI(
        api_key="your_truefoundry_api_key",
        base_url="{GATEWAY_BASE_URL}",
    )

    response = client.chat.completions.create(
        model="my-wafer-account/deepseek-v4-flash",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
    )

    print(response.choices[0].message.content)
    ```
  </Step>
</Steps>