If you're still serving ML models with Flask in 2026, we need to talk.

I migrated a client's model serving from Flask to FastAPI last quarter. Same model, same hardware. Results: 3x more concurrent requests handled, auto-generated API docs that the frontend team actually used, and type validation that caught malformed inputs before they hit the model.

FastAPI wins for ML because it's async by default (critical when your model takes 200ms per prediction and you've got hundreds of concurrent users), validates inputs with Pydantic (no more debugging why the model crashed on unexpected input), and generates interactive API docs automatically.

The basic pattern is dead simple: load model at startup, define a prediction endpoint, validate input with Pydantic, return JSON. Add a health check endpoint. Done.

Flask was great. It served us well. But FastAPI was designed for exactly this kind of workload, and the difference shows in production.

If you know Flask, the switch takes about a day. Your infrastructure team will thank you.