FastAPI Under Load: 5 Production Issues Most Teams Discover Too Late

FastAPI is fast. Clean. Productive. For MVPs, it’s excellent. But once traffic increases, the bottlenecks start appearing, and most of them are architectural, not framework-related. Here are 5 real production issues we’ve seen when FastAPI services start handling real concurrency. 1. Event Loop Blocking (Async Done Wrong) Just because your endpoint is async def doesn’t mean your system is non-blocking. Common mistakes: CPU-heavy operations inside request handlers Sync DB calls inside async endpoints Large JSON serialization Data processing (Pandas, ML inference) Blocking third-party SDKs Under light traffic → everything looks fine. Under concurrency → latency increases across all endpoints. Why? Because the event loop is blocked. What to do instead Offload CPU-bound work to worker processes Use async-native database drivers Push heavy processing to a task queue Test under realistic concurrency (Locust / k6) Async is a tool, not magic. 2. Database Connection Pool Exhaustion Default pool