Cloudflare Workers & Edge AI Development

Cloudflare Workers & Edge AI Development

Overview

Master Cloudflare Workers - the fastest serverless platform with <1ms cold starts and Workers AI for running LLMs at the edge. Deploy across 330+ data centers globally for ultra-low-latency applications that run close to your users.

When to Use This Skill

• Building globally distributed APIs with <50ms latency
• Running AI/LLM inference at the edge (Workers AI)
• Creating serverless backends without managing infrastructure
• Implementing edge middleware (auth, rate limiting, A/B testing)
• Building real-time collaborative applications (Durable Objects)
• Processing high-traffic workloads cost-effectively
• Deploying static sites with dynamic edge logic

Core Principles

1. V8 Isolates (Not Containers)

Workers run in V8 isolates - much faster than containers

// Basic Worker structure
export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    // ✅ <1ms cold start (V8 isolate, not container!)
    // ✅ Runs in 330+ locations automatically
    // ✅ 0ms warm start (keeps isolate alive)

    return new Response("Hello from the edge!", {
      headers: { "Content-Type": "text/plain" }
    });
  }
};

// WRONG: Don't use Node.js APIs (not available)
// import fs from 'fs';  // ❌ No filesystem
// process.env.VAR;      // ❌ No process object

// CORRECT: Use Workers APIs
const value = env.MY_KV_NAMESPACE.get("key");  // ✅ KV storage
const response = await fetch("https://api.example.com");  // ✅ fetch API

Key Differences from Node.js/Lambda:

• ❌ No filesystem access
• ❌ No Node.js built-ins (fs, http, crypto from Node)
• ✅ Web Standard APIs (fetch, Request, Response, WebSockets)
• ✅ Sub-millisecond cold starts
• ✅ No VPC configuration needed

2. Workers AI - LLM Inference at the Edge

// Run LLaMA 2, Mistral, or other models at the edge
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const { prompt } = await request.json();

    // Text generation with LLaMA 2
    const response = await env.AI.run("@cf/meta/llama-2-7b-chat-int8", {
      messages: [
        { role: "system", content: "You are a helpful assistant" },
        { role: "user", content: prompt }
      ]
    });

    return Response.json(response);
  }
};

// Image generation
const image = await env.AI.run("@cf/stabilityai/stable-diffusion-xl-base-1.0", {
  prompt: "A futuristic city at sunset"
});

// Text embeddings (for vector search)
const embeddings = await env.AI.run("@cf/baai/bge-base-en-v1.5", {
  text: "Hello, world!"
});

// Image classification
const result = await env.AI.run("@cf/microsoft/resnet-50", {
  image: imageBytes
});