Choosing between Python frameworks is mostly a question of how much the framework should decide for you. Django decides almost everything and hands you an admin, an ORM, and auth on day one. Flask decides almost nothing and expects you to assemble the rest. FastAPI decides the API layer and leaves the database to you. Everything below sits somewhere on that line, and picking well matters more for the software build you will maintain in three years than for the one you ship next month.
Key Takeaways
- There are three real categories: batteries-included, micro, and API-first. Most choices are settled once you pick the category.
- Django and Flask remain the safest defaults for full applications and small services respectively, with FastAPI the default for new API work.
- Django is changing its release policy. From Django 2028, every feature release gets a three-year support period, and version numbers move to a year-based scheme.
- Async is no longer a reason to leave Django. Django 6.0 brought native async ORM methods, closing the most-cited gap.
- Hiring pool should weigh as heavily as benchmarks. A framework your team cannot staff is a maintenance problem waiting to happen.
- For internal data tools, Streamlit and Dash often replace a web framework entirely, which is the cheapest answer when it fits.
🔧Talk to Our Team About Your Python Build
What Python Frameworks Actually Do
Python frameworks are pre-built structure that handles the parts of an application nobody wants to rewrite: routing requests, parsing input, talking to a database, rendering output, and handling sessions and security. The difference between frameworks is how much of that they do for you and how much they leave open.
Batteries-included: frameworks provide almost everything and expect you to work their way.
Micro: frameworks provide routing and little else, leaving every other choice to you.
API-first: frameworks focus on serving and validating data rather than rendering pages.
That split does more work than any feature list, because it maps directly onto team size and project type.
The 15 Python Frameworks at a Glance
| Framework | Type | Best for |
| Django | Batteries-included | Full products needing auth, admin, and relational data |
| Flask | Micro | Small services and deliberately custom architectures |
| FastAPI | API-first, async | New APIs, ML and AI backends, typed request validation |
| Django REST Framework | API layer on Django | CRUD-heavy APIs on an existing Django codebase |
| Litestar | API-first, async | Typed APIs wanting more structure than FastAPI |
| Starlette | ASGI toolkit | The layer under FastAPI, or minimal async services |
| Tornado | Async, networking | Long-lived connections, websockets, custom networking |
| Sanic | Async, micro | High-throughput async services with a Flask-like feel |
| AIOHTTP | Async client/server | Async HTTP clients as much as servers |
| Falcon | Minimal API | High-performance REST with a very small surface |
| Pyramid | Flexible full-stack | Projects that start small and grow structurally |
| Bottle | Micro, single file | Tiny tools, embedded apps, zero-dependency scripts |
| CherryPy | Minimal, object-oriented | Long-running services with an object-mapped structure |
| Quart | Async Flask-compatible | Migrating existing Flask apps to async |
| Streamlit | Data app | Internal dashboards and ML demos without front-end work |
One row worth reading twice: Streamlit is on this list because for internal data tools it removes the need for a web framework at all, which is frequently the cheapest correct answer.
The Batteries-Included Options
1. Django
The default for full business applications. You get an ORM, migrations, authentication, permissions, forms, and a model-driven admin out of the box, which is why it stays the fastest route to a product with users, roles, and back-office screens.
Two things changed recently that matter. Django 6.0 added native async ORM methods and built-in Content Security Policy support, which closes the async gap that pushed many teams toward FastAPI. And per Django’s official download page, the support model is changing: Django 6.2 is the final release under the current policy, and from Django 2028 every feature release receives a full three-year support period, with version numbers moving to a year-based scheme. If you are planning a multi-year upgrade path, that is the single most important fact on this page.
2. Pyramid
Positioned around starting small and growing without rewriting. Less opinionated than Django, more structured than Flask, and useful when you know the application will grow but cannot predict its shape.
3. CherryPy
A minimalist, object-oriented framework that maps Python objects to URLs and includes its own production-capable HTTP server. Long-established and suited to long-running services rather than typical web products.
The Micro Frameworks
4. Flask
The reference micro framework. Routing, templating through Jinja, and very little else, so you choose the ORM, the validation layer, and the project structure. That freedom is the point and also the risk, since the architecture is entirely yours to get right. Flask’s stable documentation covers the 3.1 series, with the Pallets team keeping the core deliberately stable.
Pick Flask when the service is genuinely small, or when you have the discipline to impose a structure and keep it.
5. Bottle
A single-file framework with no dependencies outside the standard library. Genuinely useful for tiny tools, embedded applications, and anything where adding a dependency tree is not worth it.
6. Quart
Flask-compatible but async, which makes it the natural path for an existing Flask codebase that needs async without a rewrite. The API deliberately mirrors Flask’s.
The API-First and Async Options
7. FastAPI
The default for new API work. Built on Python type hints, so request validation, serialisation, and OpenAPI documentation come from the same annotations you would write anyway. It is async-first and pairs well with ML and LLM backends, which is why it dominates new AI service work. FastAPI’s documentation is unusually good and worth reading before choosing an alternative.
Note the 0.x version prefix is a maintainer signal that minor releases may contain breaking changes, not a statement about production readiness. Pin your version.
8. Django REST Framework
Serializers, viewsets, routers, pagination, and throttling layered on Django, inheriting the ORM, auth, and admin. Still the strongest option for CRUD-heavy and admin-heavy APIs where Django is already in place.
9. Litestar
An API-first framework for teams that want more built-in structure than FastAPI provides, with opinions about layout, dependency injection, and configuration.
10. Starlette
The ASGI toolkit FastAPI is built on. Choose it directly when you want async routing and middleware without the validation layer on top.
11. Falcon
Deliberately minimal and built for speed, with a very small API surface. Suits high-volume REST services where every layer of abstraction is a cost.
12. Sanic
Async with a Flask-like feel, aimed at high-throughput services where the familiar decorator style matters.
13. Tornado
Predates the modern async ecosystem and remains strong for long-lived connections, websockets, and custom networking rather than conventional request-response work.
14. AIOHTTP
Notable for being as useful on the client side as the server side. Many teams use it as their async HTTP client regardless of which framework serves their app.
The Data App Option
15. Streamlit
Not a web framework in the traditional sense, and included because it often removes the need for one. Internal dashboards, model demos, and data tools that would otherwise take a frontend and an API can be built in Python alone. Dash is the comparable alternative when you need more control over layout and callbacks.
If the deliverable is an internal tool for a handful of colleagues, start here before reaching for Django.
💡Get a Build Cost & Timeline Estimate
How to Actually Choose
| If your project is… | Start with | Why |
| A product with users, roles, and admin screens | Django | Auth, ORM, and admin included |
| A public or internal API, greenfield | FastAPI | Typed validation and OpenAPI for free |
| An API on an existing Django codebase | Django REST Framework | Inherits everything Django already gives you |
| A small service with a clear, narrow job | Flask | Minimal surface, no unused machinery |
| An existing Flask app that needs async | Quart | API compatibility avoids a rewrite |
| Websockets or long-lived connections | Tornado | Built for exactly this |
| An internal dashboard or ML demo | Streamlit | Removes the web layer entirely |
| A performance-critical REST service | Falcon or Litestar | Small surface, async-first |
Two considerations that override the table:
Hiring pool: Django and Flask have the deepest talent markets, FastAPI is growing quickly, and the rest are narrower. A framework you cannot staff becomes a maintenance liability regardless of technical merit.
Who maintains it after launch: The best framework is the one your team can still operate after the person who chose it leaves. That argument favours boring choices more often than benchmarks do.
What We Build With and Why
AB Ark’s Python-heavy builds follow the same rule the table above describes. AI Solid Waste Detection, a mobile app that identifies and categorises waste materials through computer vision, is the shape of work where an API-first framework serving a model earns its place. AI-Powered Habit Tracking, an ecosystem for building and sustaining daily habits with behavioural insight, is the shape where users, accounts, and back-office screens push toward a batteries-included framework instead.
The choice is rarely the interesting part of a project. Getting it wrong is expensive, and getting it merely adequate costs almost nothing, which is why we default to the framework with the deepest hiring pool unless there is a specific reason not to. More on how that fits into scoping a build is in our guide to bespoke software examples, and if you need Python engineers rather than a build, our developer staff augmentation guide covers role and seniority selection.
Frequently Asked Questions
What is the best Python framework?
There is no single best one; there are three defaults. Django for full products needing authentication, an ORM, and an admin interface. FastAPI for new APIs and AI or ML services. Flask for small, deliberately custom services. Most projects are well served by one of those three.
Should I use Django or FastAPI?
Choose Django when you need a full product with users, permissions, relational data, and back-office screens, since those come built in. Choose FastAPI when the deliverable is an API surface, especially for ML or LLM backends, where typed validation and automatic OpenAPI documentation matter more than an admin panel.
Is Flask still worth using?
Yes, for services that are genuinely small or that need an architecture you control completely. Its stable release series is deliberately conservative, and the Pallets team has kept the core largely unchanged for years, which is a feature for long-lived services rather than a sign of neglect.
Which Python frameworks are fastest?
Async frameworks such as FastAPI, Litestar, Falcon, and Sanic generally outperform synchronous ones on request throughput, but framework overhead is rarely the bottleneck in a real application. Database queries, external API calls, and serialisation usually dominate, so measure your own workload before optimising for benchmark numbers.
Do I need a framework for a small internal tool?
Often not. For internal dashboards, reports, and ML demos, Streamlit or Dash lets you build in Python without a frontend or an API layer, which is faster to build and far cheaper to maintain than a web application serving a handful of users.
Pick the Category, Then the Framework
Decide first whether you want the framework to make most decisions, few decisions, or only the API decisions. That narrows fifteen options to two or three, and from there hiring pool and maintenance ownership settle it.
If you can describe what the application has to do and who will maintain it, the framework choice usually follows in a single conversation.
📞Schedule a Free Consultation Call
Head Of Engineering Department
