First Visit Takes 10s: AI Ignored Lambda Cold-Starts

The problem

An ed-tech platform's API took 10-15 seconds to respond on first visit, causing 67% of new users to abandon before seeing the homepage. CloudWatch showed Lambda cold starts averaging 11.3 seconds, with some hitting the 30-second API Gateway timeout. Students trying to submit assignments near deadlines faced random timeouts. The platform was hemorrhaging users despite having great features.

How AI created this issue

The developer asked Claude to "create a serverless API with AWS Lambda". Claude generated a typical monolithic Lambda function:

// Claude's Lambda function template
const AWS = require('aws-sdk');
const express = require('express');
const serverless = require('serverless-http');
const mongoose = require('mongoose');
const bcrypt = require('bcryptjs');
const jwt = require('jsonwebtoken');
const multer = require('multer');
const sharp = require('sharp');
const pdfkit = require('pdfkit');
const nodemailer = require('nodemailer');
const stripe = require('stripe');
// ... 20 more imports

const app = express();

// Connect to MongoDB on every invocation
mongoose.connect(process.env.MONGODB_URI);

// Initialize all services
const s3 = new AWS.S3();
const ses = new AWS.SES();
const dynamodb = new AWS.DynamoDB();

// Express middleware
app.use(express.json());
app.use(cors());
// ... more middleware

// All routes in one Lambda
app.get('/api/users', ...);
app.post('/api/auth/login', ...);
app.get('/api/courses', ...);
app.post('/api/assignments', ...);
// ... 40+ routes

module.exports.handler = serverless(app);

Claude created a 287MB deployment package with every possible dependency bundled together. It didn't mention Lambda layers, function splitting, or cold start optimization. The AI treated Lambda like a traditional server, initializing database connections and loading massive libraries on every cold start.

The solution

1. Function decomposition: Split the monolith into purpose-specific functions:

   
   // Optimized auth function - 18MB instead of 287MB
   const jwt = require('jsonwebtoken');
   let dbConnection;
   
   // Reuse connection across warm invocations
   async function getDB() {
     if (!dbConnection) {
       const { MongoClient } = require('mongodb');
       const client = new MongoClient(process.env.MONGODB_URI);
       await client.connect();
       dbConnection = client.db();
     }
     return dbConnection;
   }
   
   exports.handler = async (event) => {
     // Parse once, reuse parsed result
     const { email, password } = JSON.parse(event.body);
     
     try {
       const db = await getDB();
       const user = await db.collection('users').findOne({ email });
       
       // Lightweight password check
       const valid = await require('bcryptjs').compare(password, user.password);
       
       if (!valid) {
         return { statusCode: 401, body: JSON.stringify({ error: 'Invalid credentials' }) };
       }
       
       const token = jwt.sign({ userId: user._id }, process.env.JWT_SECRET);
       return { statusCode: 200, body: JSON.stringify({ token }) };
     } catch (error) {
       return { statusCode: 500, body: JSON.stringify({ error: error.message }) };
     }
   };

2. Lambda Layers for shared dependencies: Moved common libraries to layers, cached across functions
3. Provisioned concurrency for critical paths: Keep auth and homepage endpoints warm
4. Connection pooling: Implemented proper MongoDB connection reuse
5. Webpack tree-shaking: Reduced bundle sizes by 70% by eliminating dead code
6. Regional edge caching: Added CloudFront for static API responses

The results

• Cold start time dropped from 11.3s to 1.6s (7x improvement)
• P99 latency improved from 15s to 2.1s
• New user conversion increased 312% as first impressions improved
• AWS Lambda costs reduced by 64% through better resource utilization
• Zero timeout errors in 3 months (down from 1,200/day)
• Memory usage optimized from 3GB to 512MB per function

The team learned that serverless requires a different mindset than traditional servers. AI tools often generate "lift and shift" code that ignores serverless best practices. They now design for cold starts from day one and use AI only for specific function logic, not architecture decisions.