FastAPI vs Django for a Python backend

FastAPI and Django both serve HTTP from Python, but they start from opposite ends. FastAPI is a framework for building APIs based on standard Python type hints, built on Starlette for the web parts and Pydantic for the data parts. Django is a full web framework that ships its own ORM, migrations, automatic admin, URL routing, and templates.

The useful question is how much of the application you want the framework to own.

What the framework owns

With FastAPI, the framework owns routing, request validation through Pydantic models, and the OpenAPI description, which it serves as interactive docs at /docs and /redoc. The database layer, migrations, authentication, and any back-office screens are separate choices you make and wire together.

With Django, the framework owns much more. Models describe the database layout in Python, makemigrations and migrate manage schema changes, and registering a model with admin.site.register gives authenticated users an admin site to add, change, and delete records. A JSON API usually adds a library on top, such as Django REST framework, which describes itself as "a powerful and flexible toolkit for building Web APIs."

That difference shapes the rest of the comparison. A team that wants the database, admin, and auth to follow one set of conventions gets that from Django. A team that wants to assemble those pieces, or does not need them, carries less with FastAPI.

Concurrency and runtime model

FastAPI runs on ASGI. A path operation can be async def or plain def. FastAPI's async guide explains that a plain def function runs in an external threadpool, and it advises plain def when the code calls a library that does not support await, which it notes is "currently the case for most database libraries." The same guide notes that a blocking call made directly inside async def would block the server.

Django supports async views, but the deployment decides what you get from them. Under WSGI, async views run in a one-off event loop and do not get the benefits of an async stack. Under ASGI they do. The ORM has a-prefixed async variants of query methods, and the current docs state that transactions do not yet work in async mode; that code is wrapped with sync_to_async().

If the service is mostly long-lived connections or many concurrent outbound calls, check whether every library on that path supports async before choosing either framework.

Ecosystem and library fit

FastAPI uses Pydantic models directly as request and response types. Code that already describes its data with Pydantic, or a service that sits beside other Python code and needs a thin HTTP layer, fits that model.

Django's admin works by registering Django models, so it depends on the Django ORM. If a plan depends on the admin or on packages built around Django models, the ORM is part of the decision, not an implementation detail.

Deployment constraints

FastAPI applications run under an ASGI server; the fastapi run command uses Uvicorn. Django can be deployed with WSGI or ASGI. Django's own runserver documentation is explicit: "DO NOT USE THIS SERVER IN A PRODUCTION SETTING." Plan the production server before the first deploy with either framework.

What Better Fullstack generates

The two commands below generate a Python project with each framework:

bun create better-fullstack@latest my-api \
  --ecosystem python \
  --python-web-framework fastapi \
  --database postgres \
  --python-orm sqlalchemy \
  --python-validation pydantic \
  --python-quality ruff

bun create better-fullstack@latest my-api \
  --ecosystem python \
  --python-web-framework django \
  --python-orm none \
  --python-api django-rest-framework \
  --python-server gunicorn \
  --python-quality ruff

Add --dry-run to preview either project without writing files. Both use uv with a pyproject.toml, put the application in src/app, and include pytest tests for the root and health routes. The generated code differs in these places:

The Django option is deliberately small and leaves out Django's data and admin layers. The generated module configures settings in code instead of a Django project layout: there is no manage.py, no DATABASES setting, no models, and no admin. INSTALLED_APPS lists only contenttypes, auth, corsheaders, and rest_framework. Adding Django models, migrations, and the admin is work you do after generation. The command sets --python-orm none because with --python-orm sqlalchemy the SQLAlchemy modules are generated but no Django view uses them. --python-api django-ninja is the other API option, and the CLI accepts both API options only with Django.

The FastAPI option is more complete, with limits worth knowing before you build on it:

• The CRUD routes are declared async def but call a synchronous SQLAlchemy Session. Following FastAPI's guidance above means changing them to plain def or moving to an async session.
• Tables are created by create_all() at startup. Alembic is configured, but no migration file exists until you generate one.
• database.py reads DATABASE_URL from the process environment and otherwise connects to postgres:postgres@localhost:5432 with a database named after the project. It is imported before main.py calls load_dotenv(), so a value only in .env does not reach the engine; export DATABASE_URL in the environment instead. --database postgres is what adds the psycopg[binary] driver and that default URL; without it, the fallback is a local SQLite file.

These are generated projects, not runtime-verified deployments. Install, run the tests, and exercise one real endpoint against your target database before committing to either.

Decision table

Compare real generated stacks

• FastAPI + PostgreSQL + SQLAlchemy
• Django + PostgreSQL + Django REST Framework

The FastAPI PostgreSQL SQLAlchemy guide and the Django REST API guide walk through each project in more detail.