Python web framework comparison: Django, Flask, FastAPI and more

Django, Flask or FastAPI: The short answer

The right Python web framework comes down to three conditions: whether you need an ORM and admin built in, whether the work is mostly I/O-bound API traffic, and how much you want to assemble yourself.

Django fits full-stack products, FastAPI fits typed async APIs, and Flask fits small services where you choose each component.

Netguru's Django development and Python teams build web products and API backends with Django, Flask and FastAPI.

We recommend starting with Django when the product needs auth, migrations and an admin, because the Flask equivalent is a set of extensions you must pick and keep compatible.

Choose FastAPI only when handlers stay async end to end: a synchronous ORM call blocks the event loop and cancels its advantage.

What is a Python web framework?

A Python web framework is a library that maps incoming HTTP requests to Python handlers and supplies the routing, request parsing, and response handling around them. Django adds an ORM, admin, and auth on top. Flask stops at routing, templating, and a request context.

The interface between framework and server decides which servers and concurrency models you can use:

• WSGI (PEP 3333): a synchronous callable, app(environ, start_response). Each request holds a worker thread or process until it returns, so you scale by adding workers under Gunicorn or uWSGI. WebSockets and other long-lived connections need workarounds.
• ASGI: an async callable, app(scope, receive, send), awaited on an event loop under Uvicorn, Hypercorn, or Daphne. It carries HTTP, WebSockets, and lifespan events, and one worker interleaves many I/O-bound requests.

Flask is WSGI-first, while FastAPI, Litestar, and Starlette are ASGI-native; Django runs under either interface from the same project.

Which Python web framework fits which project?

Choose Django for database-backed products that need its ORM and admin, FastAPI or Litestar for typed API services, and Flask or Starlette when you want to assemble the stack yourself.

The table compares the frameworks on the mechanics that decide fit: interface, data layer, admin, and latest release.

Release data comes from each project's PyPI page as of early November 2026.

The Django ORM and admin are the deciding factor for most business applications. Every other row in the table needs you to pick and wire up a data layer and build the back-office screens yourself, which costs weeks on a CRUD-heavy product.

If the product is a pure API with no staff-facing screens, that cost disappears and the async frameworks become the better fit.

Among the API-first options, FastAPI has the larger community. We recommend Litestar when you want DTOs, dependency injection, and layered configuration in the core, and can accept a smaller community. Head-to-head detail is in our Flask vs Django and Flask vs FastAPI comparisons.

Bottle, Pyramid, CherryPy, TurboGears, and web2py differ in maintenance state, which the alternatives section covers.

When is Django the right choice?

Django is the right choice when a product needs the ORM, admin, authentication, and migrations in one codebase and most request time goes to database work.

The Django ORM and admin cover what most teams otherwise assemble by hand. Models generate migrations, the admin builds CRUD screens from model definitions, and django.contrib.auth supplies users, permissions, and sessions. On Flask or FastAPI, the same coverage means SQLAlchemy, Alembic, an admin add-on, and an auth library, each on its own release cadence.

Django follows the MTV pattern: models hold data, templates render output, and views (what MVC calls the controller) hold request logic. URL routing lives in urlconfs, and reverse() or the {% url %} tag builds internal paths from route names.

Django's staticfiles app (django.contrib.staticfiles) collects static assets and serves them in development. In production, a web server or CDN takes over that job.

ASGI vs WSGI is where Django's limits show. Django runs under both, and async views work under ASGI servers such as Uvicorn or Daphne. The ORM's async methods (aget, async for) run the query in a thread pool, as Django's async documentation describes. Transactions do not work in async mode yet, so Django's async documentation recommends wrapping transactional code in one synchronous function called through sync_to_async().

For a mixed workload with many concurrent outbound calls or long-lived WebSocket connections, that thread-pool boundary caps the gain from async. Django Channels covers WebSockets, but the ORM boundary stays.

We recommend a different framework in two cases:

• The service is a typed API with little relational data. FastAPI or Litestar validate input from type hints and generate the OpenAPI schema directly.
• The workload is dominated by concurrent I/O, where a natively async stack fits better.

All three main frameworks are easy to hire for. In the 2024 Python Developers Survey by the PSF and JetBrains, 38% of respondents used FastAPI, 35% Django and 34% Flask (Python Developers Survey 2024).

Django's enforced project layout also means a new engineer finds models, urls, and views where they expect them. Our post on Django's pros and cons covers the trade-offs in more depth.

What are the best alternatives to Flask?

The best alternatives to Flask depend on why you are leaving: FastAPI or Litestar for async and validation, Django for built-in batteries, and Bottle or Falcon for a smaller footprint.

The table maps each reason to the framework that changes the mechanic you are unhappy with.

Bottle vs Flask is a question of extensions. Bottle gives you routing, templates, and a dev server, and nothing else. Falcon gives up templating and focuses on HTTP APIs.

Flask leaves four things to assemble, and each has a conventional extension:

• Authentication: Flask-Login for sessions, Flask-Security-Too for registration and password flows.
• Permissions: Flask-Security-Too roles, or Flask-Principal for finer rules.
• Migrations: Flask-Migrate, which wraps Alembic.
• Admin: Flask-Admin, configured per model.

Each extension has its own maintainers and release cadence, so check the repository before you adopt one. We recommend Django once a project needs three or more of these.

Aiohttp suits teams that want an async server and client in one library.

If you stay on Flask, async views work, but Flask still runs under WSGI. For high-concurrency I/O, the ASGI frameworks above fit better.

When should you pick FastAPI?

Pick FastAPI for an async JSON API where Pydantic type-hint validation and an auto-generated OpenAPI schema are the main requirements.

FastAPI builds on Starlette for routing and the ASGI layer, then adds dependency injection and Pydantic models on top. Each annotated parameter and return type becomes a validation rule and an entry in the OpenAPI document served at /docs, so the published contract follows the code.

It fits three kinds of project:

• Services that spend most of their time waiting on upstream HTTP calls, queues or databases, where ASGI's event loop beats WSGI's one-worker-per-request model.
• Model-serving and internal APIs where typed request and response contracts matter more than server-rendered pages.
• Teams that already use type hints and want schema-first client generation from OpenAPI.

Failure modes that cancel the async advantage

A synchronous ORM call inside an async def handler blocks the event loop, and every concurrent request waits behind it. Two fixes work: declare the handler with plain def so FastAPI runs it in a threadpool, or move to async SQLAlchemy 2.0 with an async driver such as asyncpg.

CPU-bound work, such as image processing or large in-process model inference, blocks the loop in the same way. A threadpool does not help much because of the GIL. Push that work to a process pool or a task queue like Celery.

FastAPI is a poor fit for admin-heavy products. Django's ORM and admin provide a back-office CRUD interface, migrations and auth without extra setup. With FastAPI you assemble SQLAlchemy, Alembic and an auth layer yourself.

Is FastAPI the fastest Python framework?

No. FastAPI adds routing, dependency injection and Pydantic validation on top of Starlette, so a bare Starlette endpoint usually outpaces it in a plain JSON test such as those in the TechEmpower Framework Benchmarks.

Benchmarks of this kind mostly measure framework overhead on trivial endpoints. Once a handler waits on a database round trip, query design, indexing and connection pooling move latency far more than the framework does. Benchmark your own endpoints against your own database before treating a ranking as a reason to choose.

Which async framework beyond FastAPI?

Litestar and Starlette are the closest ASGI-native alternatives to FastAPI, and Sanic, aiohttp, and Tornado cover narrower cases: custom servers, websocket-heavy services, and older event-loop code.

Litestar targets teams that find FastAPI's structure thin for a large codebase. It offers class-based controllers, built-in dependency injection, DTOs, and OpenAPI generation, and accepts Pydantic models, msgspec structs, or dataclasses.

Litestar's session, JWT, and CSRF middleware are first-party, so fewer decisions go to third-party packages.

Starlette is the layer FastAPI itself builds on, and it gives you routing, middleware, and websockets with no validation or schema layer. Choose it for small services or when you want to pick your own validation library.

The remaining three differ in how they relate to asyncio and ASGI:

Tornado and Sanic ship their own server stack, so their request handling does not follow the ASGI contract that Litestar, Starlette, and FastAPI share. aiohttp is asyncio-native but also sits outside ASGI. In practice, middleware, test clients, and servers such as Uvicorn or Hypercorn carry over between ASGI frameworks and do not carry over to these three.

For database access on Sanic or aiohttp, use SQLAlchemy's native asyncio support, available since 1.4. Older guides recommend the GINO wrapper, so check its maintenance status before adopting it in a new service.

We recommend Litestar for new ASGI APIs that need more structure than FastAPI. Tornado and Sanic fit mainly when a service already runs on them.

Alternatives to Django, and which older frameworks are still maintained

Pick a Django alternative by trigger: an API-only service, a minimal surface, websocket-heavy or async work, or flexible URL dispatch. If the project uses the Django ORM and admin, stay on Django.

CherryPy ships no template layer of its own, so Mako, Jinja2 or Cheetah plug in. That freedom suits teams with an existing template investment and is a cost for everyone else.

Maintenance status of the older frameworks

All five still ship releases, at very different rates (PyPI, and GitHub tags for web2py, as of early November 2026):

A long gap between releases matters less for small frameworks than for the code around them. An unmaintained base stops receiving security patches for its own code, and its pinned dependencies (template engines, form libraries, WSGI servers) drift. It also stops declaring support for new Python versions, which blocks interpreter upgrades once the version you run leaves its five-year support window.

CherryPy, with no release since June 2024, is the one to inspect hardest before building anything new on it. For a migration off any of them, put the framework swap behind the same ASGI or WSGI boundary so it ships in stages.

How do you choose by project type?

Pick Django for CMS and data-heavy MVPs, FastAPI for typed APIs, Flask for small internal tools, and Litestar for structured async services. The table adds the test that decides each pick and what switching later costs.

Our estimate is that switching cost tracks how much code touches the ORM and the validation layer. Route handlers port quickly; moving from Django to anything else means rewriting data access and replacing the admin.

A sync WSGI codebase moving to ASGI also needs its blocking calls audited. Hiring is easiest for Django, Flask and FastAPI, so weigh that before committing to a niche pick.

FAQ

Talk to Netguru about your Python backend

If you are choosing between Django and FastAPI for a new product, or moving a Flask codebase onto one of them, our engineers can review your requirements and recommend a framework with the trade-offs written down. Our Python development services cover framework selection, new builds, and migrations from older frameworks, from MVP to production.

Talk to our team with your expected traffic, team skills, and compliance constraints, and we will tell you which framework fits.