Cloudflare Deploy

Cloudflare Workers AI

Expert guidance for Cloudflare Workers AI - serverless GPU-powered AI inference at the edge.

Overview

Workers AI provides:

• 50+ pre-trained models (LLMs, embeddings, image generation, speech-to-text, translation)

• Native Workers binding (no external API calls)

• Pay-per-use pricing (neurons consumed per inference)

• OpenAI-compatible REST API

• Streaming support for text generation

• Function calling with compatible models

Architecture: Inference runs on Cloudflare's GPU network. Models load on first request (cold start 1-3s), subsequent requests are faster.

Quick Start

interface Env {
  AI: Ai;
}

export default {
  async fetch(request: Request, env: Env) {
    const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
      messages: [{ role: 'user', content: 'What is Cloudflare?' }]
    });
    return Response.json(response);
  }
};

# Setup - add binding to wrangler.jsonc
wrangler dev --remote  # Must use --remote for AI
wrangler deploy

Model Selection Decision Tree

Text Generation (Chat/Completion)

Quality Priority:

• Best quality: @cf/meta/llama-3.1-70b-instruct (expensive, ~2000 neurons)

• Balanced: @cf/meta/llama-3.1-8b-instruct (good quality, ~200 neurons)

• Fastest/cheapest: @cf/mistral/mistral-7b-instruct-v0.1 (~50 neurons)

Function Calling:

• Use @cf/meta/llama-3.1-8b-instruct or @cf/meta/llama-3.1-70b-instruct (native tool support)

Code Generation:

• Use @cf/deepseek-ai/deepseek-coder-6.7b-instruct (specialized for code)

Embeddings (Semantic Search/RAG)

English text:

• Best: @cf/baai/bge-large-en-v1.5 (1024 dims, highest quality)

• Balanced: @cf/baai/bge-base-en-v1.5 (768 dims, good quality)

• Fast: @cf/baai/bge-small-en-v1.5 (384 dims, lower quality but fast)

Multilingual:

• Use @hf/sentence-transformers/paraphrase-multilingual-minilm-l12-v2

Image Generation

• Stable Diffusion: @cf/stabilityai/stable-diffusion-xl-base-1.0 (~10,000 neurons)

• Portraits: @cf/lykon/dreamshaper-8-lcm (optimized for faces)

Other Tasks

• Speech-to-text: @cf/openai/whisper

• Translation: @cf/meta/m2m100-1.2b (100 languages)

• Image classification: @cf/microsoft/resnet-50

SDK Approach Decision Tree

Native Binding (Recommended)

When: Building Workers/Pages with TypeScript
Why: Zero external dependencies, best performance, native types

await env.AI.run(model, input);

REST API

When: External services, non-Workers environments, testing
Why: Standard HTTP, works anywhere

curl https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/ai/run/@cf/meta/llama-3.1-8b-instruct \
  -H "Authorization: Bearer <API_TOKEN>" \
  -d '{"messages":[{"role":"user","content":"Hello"}]}'

Vercel AI SDK Integration

When: Using Vercel AI SDK features (streaming UI, tool calling abstractions)
Why: Unified interface across providers

import { openai } from '@ai-sdk/openai';

const model = openai('model-name', {
  baseURL: 'https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/ai/v1',
  headers: { Authorization: 'Bearer <API_TOKEN>' }
});

RAG vs Direct Generation

Use RAG (Vectorize + Workers AI) When:

• Answering questions about specific documents/data

• Need factual accuracy from known corpus

• Context exceeds model's window (>4K tokens)

• Building knowledge base chat

Use Direct Generation When:

• Creative writing, brainstorming

• General knowledge questions

• Small context fits in prompt (<4K tokens)

• Cost optimization (RAG adds embedding + vector search costs)

Platform Limits

Pricing: Free 10K neurons/day, then pay per neuron consumed (varies by model)

Common Tasks

// Streaming text generation
const stream = await env.AI.run(model, { messages, stream: true });
for await (const chunk of stream) {
  console.log(chunk.response);
}

// Embeddings for RAG
const { data } = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
  text: ['Query text', 'Document 1', 'Document 2']
});

// Function calling
const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
  messages: [{ role: 'user', content: 'What is the weather?' }],
  tools: [{
    type: 'function',
    function: { name: 'getWeather', parameters: { ... } }
  }]
});

Development Workflow

# Always use --remote for AI (local doesn't have models)
wrangler dev --remote

# Deploy to production
wrangler deploy

# View model catalog
# https://developers.cloudflare.com/workers-ai/models/

Reading Order

Start here: Quick Start above → configuration.md (setup)

Common tasks:

• First time setup: configuration.md → Add binding + deploy

• Choose model: Model Selection Decision Tree (above) → api.md

• Build RAG: patterns.md → Vectorize integration

• Optimize costs: Model Selection + gotchas.md (rate limits)

• Debugging: gotchas.md → Common errors

In This Reference

• configuration.md - wrangler.jsonc setup, TypeScript types, bindings, environment variables

• api.md - env.AI.run(), streaming, function calling, REST API, response types

• patterns.md - RAG with Vectorize, prompt engineering, batching, error handling, caching

• gotchas.md - Deprecated @cloudflare/ai package, rate limits, pricing, common errors

See Also

• vectorize - Vector database for RAG patterns

• ai-gateway - Caching, rate limiting, analytics for AI requests

• workers - Worker runtime and fetch handler patterns