Course Content
FastAPI Essentials
1 sections · 32 lessons
What are some advantages of using FastAPI over Flask?
What you need to know
Each advantage maps to a concrete daily task. It helps to name the task, not just the feature.
| Advantage | What it saves you in an AI service |
|---|---|
| Native async | Holding 200 open LLM calls without 200 threads |
| Automatic validation | Writing if "text" not in data checks in every handler |
| Generated OpenAPI docs | Keeping a wiki page in sync with the code |
Dependency injection (Depends) | Passing the DB session, current user or model handle into each route by hand |
| Typed handlers | Bugs from misspelled dict keys; your editor now autocompletes body.text |
| Streaming and SSE | Custom code to send tokens as they arrive |
| Test overrides | Monkeypatching to swap the real model for a fake one in tests |
The last two deserve a closer look because interviewers like them.
Dependency injection means a route declares what it needs and FastAPI builds it:
1from typing import Annotated2from fastapi import Depends, Request34def get_model(request: Request) -> SentimentModel:5 return request.app.state.model # loaded once at startup, in lifespan67ModelDep = Annotated[SentimentModel, Depends(get_model)]89@app.post("/sentiment")10def sentiment(review: Review, model: ModelDep) -> Sentiment:11 return model.predict(review.text)In a test, one line swaps the real model for a fake: app.dependency_overrides[get_model] = lambda: FakeModel(). No GPU, no monkeypatching.
Where Flask still wins
- A much bigger extension ecosystem (Flask-Login, Flask-Admin and many more).
- Server-rendered HTML apps with Jinja templates.
- Teams that already know it, with years of deployment experience.
The honest summary: for a JSON API that calls models and databases, FastAPI's defaults save real work. For a small internal web app with forms and pages, Flask is just as good.
A real-life example
A fintech company has a KYC document classifier behind a Flask API. There are about 40 lines of hand-written input checks, and the API documentation is a wiki page last updated eight months ago. The mobile team keeps sending confidence_threshold as a string, and the Flask code compares a string with a float and crashes with a 500.
After the move to FastAPI, the request model declares confidence_threshold: float = Field(0.8, ge=0, le=1). The string "0.9" is converted safely, "high" gets a 422 naming the field, and the mobile team generates their client from /openapi.json. The 40 lines of checks are gone, and the "docs are wrong" support tickets stop.
Follow-up questions to expect
- "Are the generated docs always right?" — They match the code exactly, but they are only as detailed as your annotations. Without return types and field descriptions they are thin.
- "Would you rewrite a working Flask service?" — Not just for the framework. I would move it if it is I/O-bound and hitting concurrency limits, or if the lack of validation is causing real bugs.
- "What does FastAPI not give you?" — No ORM, migrations, admin UI or user management. You add SQLAlchemy, Alembic and an auth provider yourself.