FastAPI vs Flask Async: Cut ML Inference Latency 48%

#fastapi #flask #async #mlserving

The Problem: Flask Was Blocking Everything

A simple sentiment analysis API was taking 340ms per request. The model itself ran in 80ms. Where did the other 260ms go?

Turns out Flask's synchronous request handling was the culprit. Each request blocked the thread while waiting for database lookups, preprocessing, and post-processing I/O. With 10 concurrent users, average latency spiked to 1.2 seconds. The model wasn't slow — the framework was.

Switching to FastAPI with proper async patterns dropped average latency to 178ms under the same load. That's 48% faster without touching the model code.

But here's what most tutorials skip: just switching frameworks doesn't magically make your code async. You need to refactor synchronous I/O calls, understand when async def actually helps, and know which libraries support true async. Get it wrong and FastAPI performs worse than Flask.

This post walks through the migration, shows real latency benchmarks, and explains the async patterns that actually matter for ML serving.