---
title: Workers AI
description: Route Workers AI requests through AI Gateway for analytics, caching, and rate limiting.
image: https://developers.cloudflare.com/dev-products-preview.png
---

> Documentation Index  
> Fetch the complete documentation index at: https://developers.cloudflare.com/ai-gateway/llms.txt  
> Use this file to discover all available pages before exploring further. 

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# Workers AI

Use AI Gateway for analytics, caching, and security on requests to [Workers AI](https://developers.cloudflare.com/workers-ai/).

## REST API

Use the [REST API](https://developers.cloudflare.com/ai-gateway/usage/rest-api/) to call Workers AI models. Workers AI models use the `@cf/` prefix in the model name and require the `cf-aig-gateway-id` header to specify which gateway to route through.

Request to Workers AI Kimi model

```
# Run `wrangler whoami` to get your account ID to replace $CLOUDFLARE_ACCOUNT_ID,# and `wrangler auth token` to get an auth token to replace $CLOUDFLARE_API_TOKEN.curl -X POST "https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions" \  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \  --header "cf-aig-gateway-id: default" \  --header "Content-Type: application/json" \  --data '{    "model": "@cf/moonshotai/kimi-k2.6",    "messages": [      {        "role": "user",        "content": "What is Cloudflare?"      }    ]  }'
```

## Workers binding

You can integrate Workers AI with AI Gateway using an environment binding. To include an AI Gateway within your Worker, add the gateway as an object in your Workers AI request.

* [  JavaScript ](#tab-panel-6886)
* [  TypeScript ](#tab-panel-6887)

JavaScript

```
export default {  async fetch(request, env) {    const response = await env.AI.run(      "@cf/meta/llama-3.1-8b-instruct",      {        prompt: "Why should you use Cloudflare for your AI inference?",      },      {        gateway: {          id: "{gateway_id}",          skipCache: false,          cacheTtl: 3360,        },      },    );    return new Response(JSON.stringify(response));  },};
```

TypeScript

```
export interface Env {  AI: Ai;}
export default {  async fetch(request: Request, env: Env): Promise<Response> {    const response = await env.AI.run(      "@cf/meta/llama-3.1-8b-instruct",      {        prompt: "Why should you use Cloudflare for your AI inference?",      },      {        gateway: {          id: "{gateway_id}",          skipCache: false,          cacheTtl: 3360,        },      },    );    return new Response(JSON.stringify(response));  },} satisfies ExportedHandler<Env>;
```

For a detailed step-by-step guide on integrating Workers AI with AI Gateway using a binding, refer to [Integrations in AI Gateway](https://developers.cloudflare.com/ai-gateway/integrations/aig-workers-ai-binding/).

Workers AI supports the following parameters for AI gateways:

* `id` string  
  * Name of your existing [AI Gateway](https://developers.cloudflare.com/ai-gateway/get-started/). Must be in the same account as your Worker.
* `skipCache` boolean(default: false)  
  * Controls whether the request should [skip the cache](https://developers.cloudflare.com/ai-gateway/features/caching/#skip-cache-cf-aig-skip-cache).
* `cacheTtl` number  
  * Controls the [Cache TTL](https://developers.cloudflare.com/ai-gateway/features/caching/#cache-ttl-cf-aig-cache-ttl).

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