Course Content
FastAPI Essentials
1 sections · 32 lessons
What are the advantages of using FastAPI over Django for building APIs?
What you need to know
The two frameworks solve different problems, so the answer is about fit.
Django (+ DRF)
- ORM, migrations, admin UI, users, sessions, forms, templates
- Strong conventions; large teams move fast inside them
- Async views exist, but much of the ecosystem and ORM is still sync underneath
- API docs need serializers plus extra tools such as drf-spectacular
FastAPI
- Routing, validation, OpenAPI docs, DI, streaming — nothing else
- You choose the ORM, migrations and auth yourself
- Async-native: streaming and many concurrent upstream calls are natural
- Docs generated directly from type hints
| Need | Better fit |
|---|---|
| Token streaming, SSE, WebSockets to users | FastAPI |
| A thin service in front of a model or vector DB | FastAPI |
| Internal admin screens for staff | Django |
| 50 related tables, complex migrations, permissions | Django |
| Server-rendered website | Django |
There is also a middle path worth knowing: Django Ninja gives Django a FastAPI-style API layer (type hints and Pydantic), so a Django team can get typed, documented endpoints without leaving Django.
Why split the services
Model serving and product logic scale differently. An inference service needs GPUs or large memory, long timeouts and few workers per machine. A product backend needs many cheap workers and a database. Keeping them separate means a spike in LLM traffic does not slow down login, and a Django deploy does not reload the model.
A real-life example
An online-learning platform in India runs its courses, payments and staff admin on Django. Support staff use the Django admin every day to issue refunds. When they added an AI tutor with streaming answers, the first version lived in a Django view. Long-lived streams tied up sync workers, and during exam season the whole site slowed down, including checkout.
They moved the tutor into a FastAPI service on its own GPU nodes. Django keeps users, courses and payments; it issues a short-lived signed token that the FastAPI tutor service verifies. The tutor streams answers over SSE and calls back to Django's API only to record usage. Exam-season spikes now affect only the tutor pods, which scale on their own.
Follow-up questions to expect
- "Isn't Django async now?" — Django supports async views and an async ORM API, but many database operations still run through a thread underneath, and much of the ecosystem is sync. It works, but it is not async-native like FastAPI.
- "What do you lose by choosing FastAPI?" — The admin, built-in auth and sessions, and the strong project conventions. You must assemble and maintain those choices.
- "How would the two services share users?" — Through tokens: Django (or an identity provider) issues JWTs, and FastAPI only verifies them. Avoid sharing one database between two codebases.