Serverless

Cold starts are serverless’s original sin. Your function spins up, downloads dependencies, initializes connections, and finally runs your code — all while your user waits. The P99 latency spikes. The SLA teeters. Here’s what actually works, ranked by effectiveness and cost. Understanding the Cold Start A cold start happens when there’s no warm instance available to handle a request. The platform must: Provision a container — 50-500ms depending on runtime size Initialize the runtime — 10-100ms (Python) to 500ms+ (JVM without optimization) Run your initialization code — depends on what you do at module level Execute the handler — your actual function 1 2 3 4 5 6 7 8 9 10 11 12 # Everything at module level runs during cold start import boto3 # ~100ms import pandas # ~500ms import torch # ~2000ms # Connection initialization during cold start dynamodb = boto3.resource('dynamodb') table = dynamodb.Table('users') def handler(event, context): # Only this runs on warm invocations return table.get_item(Key={'id': event['user_id']}) Measured cold start times for AWS Lambda (1024MB, us-east-1): ...

A practical guide to serverless architecture — the real benefits, hidden costs, and patterns that actually work in production.