Most Speed Comparisons Skip the Setup Cost
Every FastAPI vs Flask benchmark focuses on request throughput under load. But if you're deploying your first ML model, that's not what kills you. It's the 40 seconds your Flask app spends loading a 500MB model on every cold start, or the mystery "Address already in use" error that costs you 20 minutes of Googling.
Here's what actually matters for beginners: how fast can you go from pip install to a working prediction endpoint? I built the same sklearn model server in both frameworks, keeping each under 50 lines. The results surprised me.
The Test: Identical Model, Minimal Code
I trained a simple RandomForestClassifier on the iris dataset (yes, iris — the point is framework overhead, not model complexity). Both servers expose a /predict POST endpoint that accepts JSON features and returns a class prediction.
Here's the FastAPI version:
python
# fastapi_serve.py
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np
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*Continue reading the full article on [TildAlice](https://tildalice.io/fastapi-vs-flask-ml-serving-beginner-speed-test/)*
