The Complete Cloudflare Workers Stack: 28 Workers Powering a Real Business

This isn't a tutorial. It's a production architecture guide from someone running 28 Cloudflare Workers in production right now. Sub-millisecond response times. Zero server management. Global distribution.

I'm going to show you exactly how we built it, why we made specific decisions, and share real performance metrics from our dashboard.

The Architecture: Why 28 Workers, Not 1

My first attempt was a monolith. One giant worker doing everything: leads, valuations, notifications, data processing. 10,000+ lines of code. It worked—until it didn't.

Debugging was hell. One bug could take down everything. Deployments were scary. So I rebuilt everything with a microservices mindset:

"Each worker does one thing well. If it breaks, only that function breaks. If it needs scaling, only that function scales."

The Worker Inventory (Production)

Here's every worker we run, what it does, and its actual response time:

🤖 AI & Chatbots

📊 Data & Analytics

🔗 Integrations & Webhooks

🛠️ Utilities & Proxies

Code Patterns That Work

Here are the patterns we use across all 28 workers:

1. The Standard Worker Structure

export default {
  async fetch(request, env, ctx) {
    // CORS handling
    if (request.method === 'OPTIONS') {
      return new Response(null, { headers: corsHeaders });
    }
    
    try {
      const url = new URL(request.url);
      
      // Route to handlers
      if (url.pathname === '/api/valuation') {
        return handleValuation(request, env);
      }
      
      return new Response('Not Found', { status: 404 });
    } catch (error) {
      return new Response(JSON.stringify({ error: error.message }), {
        status: 500,
        headers: { 'Content-Type': 'application/json', ...corsHeaders }
      });
    }
  }
}

2. KV Caching Pattern

async function getWithCache(key, fetchFn, env, ttl = 3600) {
  // Try cache first
  const cached = await env.CACHE_KV.get(key, 'json');
  if (cached) return cached;
  
  // Fetch fresh data
  const fresh = await fetchFn();
  
  // Store in cache (non-blocking)
  ctx.waitUntil(
    env.CACHE_KV.put(key, JSON.stringify(fresh), { expirationTtl: ttl })
  );
  
  return fresh;
}

3. Lead Processing Flow

async function processLead(lead, env) {
  // 1. Validate
  const validation = validateLead(lead);
  if (!validation.valid) return { error: validation.errors };
  
  // 2. Enrich with property data
  const propertyData = await env.VALUATION.fetch('/api/quick', {
    method: 'POST',
    body: JSON.stringify({ address: lead.address })
  });
  
  // 3. Score the lead
  const score = calculateLeadScore(lead, propertyData);
  
  // 4. Route to appropriate channel
  await env.SLACK_INTEL.fetch('/api/notify', {
    method: 'POST',
    body: JSON.stringify({
      channel: score > 80 ? '#hot-leads' : '#leads-incoming',
      lead: { ...lead, score, propertyData }
    })
  });
  
  return { success: true, score };
}

Cloudflare Services We Use

Lessons Learned

1. Start Small, Split Early

Don't wait until your worker is 10,000 lines to split it. If a function could be its own service, make it one. The overhead is minimal, the benefits are massive.

2. Use Service Bindings

Workers can call other workers directly without HTTP overhead. We use this for our lead-bot to call valuation, valuation to call market-intelligence, etc.

3. Cache Aggressively

KV is cheap. API calls aren't. We cache everything that doesn't need to be real-time: property data, market stats, even AI responses for common queries.

4. Monitor Everything

Cloudflare's analytics are good, but we also log to our Slack-intel worker. Every error, every slow response, every anomaly gets flagged immediately.

FAQ

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