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cloudflare-workers-edge-ai-development Build ultra-low-latency edge computing applications with Cloudflare Workers, Workers AI for LLM inference, V8 isolates, Durable Objects, and serverless patterns deployed across 330+ data centers worldwide
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name Cloudflare Workers & Edge AI Development skill_id cloudflare-edge-ai version 1.0.0 description Build ultra-low-latency edge computing applications with Cloudflare Workers, Workers AI for LLM inference, V8 isolates, Durable Objects, and serverless patterns deployed across 330+ data centers worldwide category Cloud & Serverless tags ["cloudflare","edge-computing","serverless","workers","workers-ai","v8-isolates","durable-objects","edge-ai","low-latency","wrangler"] author mcp-skillset license MIT created 2025-11-25T00:00:00.000Z last_updated 2025-11-25T00:00:00.000Z toolchain ["Cloudflare Workers","Wrangler CLI","Workers AI","TypeScript"] frameworks ["Cloudflare Workers","Hono (lightweight framework)","Vectorize (vector DB)"] related_skills ["fastapi-web-development","terraform-infrastructure","web3-blockchain"]
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
export default {
async fetch (request : Request , env : Env , ctx : ExecutionContext ): Promise <Response > {
return new Response ("Hello from the edge!" , {
headers : { "Content-Type" : "text/plain" }
});
}
};
const value = env.MY_KV_NAMESPACE .get
"key"
const
await
fetch
"https://api.example.com"
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
export default {
async fetch (request : Request , env : Env ): Promise <Response > {
const { prompt } = await request.json ();
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);
}
};
const image = await env.AI .run ("@cf/stabilityai/stable-diffusion-xl-base-1.0" , {
prompt : "A futuristic city at sunset"
});
const embeddings = await env.AI .run ("@cf/baai/bge-base-en-v1.5" , {
text : "Hello, world!"
});
const result = await env.AI .run ("@cf/microsoft/resnet-50" , {
image : imageBytes
});
🚀 <50ms inference latency globally
💰 Pay only for inference time (no idle costs)
🌍 Runs in 330+ locations automatically
🔒 Data never leaves Cloudflare's network
3. KV Storage for Edge Data
# kv_namespaces = [
# { binding = "MY_KV" , id = "xxxxx" , preview_id = "yyyyy" }
# ]
export default {
async fetch (request : Request , env : Env ): Promise <Response > {
const url = new URL (request.url );
const key = url.pathname .slice (1 );
const value = await env.MY_KV .get (key);
if (value === null ) {
return new Response ("Not found" , { status : 404 });
}
await env.MY_KV .put ("user:123" , JSON .stringify ({ name : "Alice" }), {
expirationTtl : 3600 ,
metadata : { createdAt : Date .now () }
});
const list = await env.MY_KV .list ({ prefix : "user:" });
await env.MY_KV .delete (key);
return new Response (value);
}
};
✅ Use for read-heavy workloads (caching, config)
✅ Values up to 25 MB
✅ Eventually consistent (may take 60s to propagate)
❌ Not for transactional data or immediate consistency
4. Durable Objects for Stateful Logic
export class RateLimiter {
state : DurableObjectState ;
constructor (state : DurableObjectState , env : Env this .state = state;
}
async fetch (request : Request const count = (await this .state .storage .get ("count" )) || 0 ;
const newCount = count + 1 ;
if (newCount > 100 ) {
return new Response ("Rate limit exceeded" , { status : 429 });
}
await this .state .storage .put ("count" , newCount);
return new Response (`Request ${newCount} /100` );
}
}
export default {
async fetch (request : Request , env : Env ): Promise <Response > {
const id = env.RATE_LIMITER .idFromName ("user:123" );
const stub = env.RATE_LIMITER .get (id);
return stub.fetch (request);
}
};
# [[durable_objects.bindings ]]
# name = "RATE_LIMITER"
# class_name = "RateLimiter"
# script_name = "my-worker"
Durable Objects Use Cases :
✅ Real-time collaboration (Google Docs-style)
✅ WebSocket connections
✅ Distributed locks and coordination
✅ Game servers, chat rooms
✅ Strongly consistent counters
5. Vectorize for Edge Vector Search
export default {
async fetch (request : Request , env : Env ): Promise <Response > {
const { text } = await request.json ();
const embedding = await env.AI .run ("@cf/baai/bge-base-en-v1.5" , {
text : text
});
await env.VECTORIZE .insert ([
{
id : "doc-1" ,
values : embedding.data [0 ],
metadata : { text : text }
}
]);
const results = await env.VECTORIZE .query (embedding.data [0 ], {
topK : 5 ,
returnMetadata : true
});
return Response .json (results);
}
};
# [[vectorize]]
# binding = "VECTORIZE"
# index_name = "my-index"
Best Practices
Request Routing & Middleware import { Hono } from 'hono' ;
import { cors } from 'hono/cors' ;
import { bearerAuth } from 'hono/bearer-auth' ;
const app = new Hono <{ Bindings : Env }>();
app.use ('/*' , cors ({
origin : ['https://example.com' ],
allowMethods : ['GET' , 'POST' , 'PUT' , 'DELETE' ],
}));
app.use ('/api/*' , bearerAuth ({ token : 'secret-token' }));
app.use ('/api/*' , async (c, next) => {
const ip = c.req .header ('cf-connecting-ip' );
const id = c.env .RATE_LIMITER .idFromName (ip);
const limiter = c.env .RATE_LIMITER .get (id);
const response = await limiter.fetch (c.req .raw );
if (response.status === 429 ) {
return c.text ('Rate limit exceeded' , 429 );
}
await next ();
});
app.get ('/api/users/:id' , async (c) => {
const userId = c.req .param ('id' );
const user = await c.env .MY_KV .get (`user:${userId} ` );
return c.json (JSON .parse (user));
});
app.post ('/api/chat' , async (c) => {
const { message } = await c.req .json ();
const response = await c.env .AI .run ("@cf/meta/llama-2-7b-chat-int8" , {
messages : [{ role : "user" , content : message }]
});
return c.json (response);
});
export default app;
Caching Strategies export default {
async fetch (request : Request , env : Env , ctx : ExecutionContext ): Promise <Response > {
const cacheUrl = new URL (request.url );
const cacheKey = new Request (cacheUrl.toString (), request);
const cache = caches.default ;
let response = await cache.match (cacheKey);
if (!response) {
response = await fetch (request);
response = new Response (response.body , response);
response.headers .set ("Cache-Control" , "public, max-age=3600" );
ctx.waitUntil (cache.put (cacheKey, response.clone ()));
}
return response;
}
};
Environment Variables & Secrets
# [vars]
# API_URL = "https://api.example.com"
#
# Run : wrangler secret put API_KEY
# (for sensitive values)
export default {
async fetch (request : Request , env : Env ): Promise <Response > {
const apiUrl = env.API_URL ;
const apiKey = env.API_KEY ;
const response = await fetch (`${apiUrl} /data` , {
headers : { "Authorization" : `Bearer ${apiKey} ` }
});
return response;
}
};
Common Patterns
A/B Testing at the Edge export default {
async fetch (request : Request ): Promise <Response > {
const cookie = request.headers .get ('cookie' ) || '' ;
const variant = cookie.includes ('variant=b' ) ? 'B' : 'A' ;
if (variant === 'B' ) {
return fetch ('https://variant-b.example.com' );
}
return fetch ('https://variant-a.example.com' );
}
};
Geolocation-Based Routing export default {
async fetch (request : Request ): Promise <Response > {
const country = request.cf ?.country ;
if (country === 'US' ) {
return fetch ('https://us.api.example.com' );
} else if (country === 'EU' ) {
return fetch ('https://eu.api.example.com' );
}
return fetch ('https://global.api.example.com' );
}
};
Anti-Patterns
❌ DON'T: Block the response
export default {
async fetch (request : Request ): Promise <Response > {
for (let i = 0 ; i < 1000000 ; i++) {
}
return new Response ("Done" );
}
};
export default {
async fetch (request : Request , env : Env , ctx : ExecutionContext ): Promise <Response > {
ctx.waitUntil (
(async () => {
await env.MY_KV .put ("last-request" , Date .now ());
})()
);
return new Response ("Done" );
}
};
❌ DON'T: Use Workers for long-running tasks Workers CPU limit: 10ms (free), 50ms (paid), 30s (unbound)
✅ Good for: API requests, edge logic, AI inference
❌ Bad for: Video encoding, large file processing, long ML training
Testing & Development
npm install -g wrangler
wrangler init my-worker
wrangler dev
wrangler deploy
wrangler tail
npm test
Related Skills
fastapi-web-development : Compare serverless vs traditional APIsterraform-infrastructure : Deploy Workers with Terraformweb3-blockchain : Build Web3 apps on Cloudflare
Additional Resources
Example Questions
"How do I run LLaMA 2 at the edge with Workers AI?"
"Show me how to implement rate limiting with Durable Objects"
"What's the difference between KV and Durable Objects?"
"How do I cache API responses at the edge?"
"Write a Worker that does geolocation-based routing"
