How I Built a FastAPI Backend That Handles 10K Requests/sec on a $5 Server

Disclosure: This post contains affiliate links. We may earn a small commission at no extra cost to you. TL;DR : You don't need expensive infrastructure to build a high-performance Python API. With FastAPI, async SQLAlchemy, connection pooling, and a few key optimizations, I'm handling production traffic on a $5/month server. Here's the exact configuration and the patterns that make it work. Most FastAPI tutorials stop at "hello world." They show you how to define a route, return some JSON, and call it a day. But what happens when real traffic hits your app? I spent the last year building AEGIS, a multi-agent AI orchestration system. The backend is a FastAPI service handling authentication, agent messaging, workflow execution, and real-time coordination across 16 AI agents. Here's how I optimized it to handle 10,000+ requests per second on minimal infrastructure. Why Does FastAPI Performance Matter? FastAPI is already one of the fastest Python frameworks — but the framework is only part