15 Best Python Frameworks for Website Development in 2026

Python has been a major choice for server-side work for nearly two decades, and the way teams pick a framework has changed completely in the last three years. Django and Flask are still here. FastAPI grew up. A new generation of async frameworks like Litestar, Sanic, Robyn, etc. gives teams more options than they had.

This guide ranks the 15 Python frameworks worth knowing, based on what we use and ship at Relia. Some are mature frameworks with years of production use behind them, while others are newer options we now use for specific projects. The criteria are the same in every case: what the framework does well, what it costs you to use, and which projects pay it back.

What is a Python Framework?

A Python framework is a collection of pre-written modules and libraries that gives a project its starting structure. Instead of writing common parts from scratch like routing HTTP requests, talking to a database, handling form input, or rendering templates from scratch, the framework already provides. The team writes the part that is unique to the product.

A full-stack framework can save time by handling many common tasks, but it also comes with more rules and structure. A lighter framework gives the team more freedom, but it also leaves more decisions to make. The right choice depends on the size of the project, the team’s experience, and how much control the product needs.

3 Types of Python Frameworks

Full-stack Frameworks

Full-stack frameworks come with everything a typical web app needs like an ORM, an admin panel, authentication, form handling, templating, security middleware, and a CLI for common tasks. Django is the best-known example. 

The main benefit is speed because teams can build faster with many common features already included. But, full-stack frameworks usually have an opinionated structure, so they give teams less flexibility than lighter frameworks.

Microframeworks

Microframeworks provide only the basics like routing, request handling, response building. Everything else like the ORM, the auth library, the templating engine, the deployment story depends on the team.

Flask and Bottle are the typical examples. The benefit is freedom and a lighter footprint, but the team has to make more technical decisions and maintain more parts of the stack themselves.

Asynchronous Frameworks

Async frameworks are built around Python's async/await model. They handle large numbers of concurrent connections like long-polling, WebSockets, real-time APIs, and streaming with less overhead than traditional sync frameworks. 

FastAPI, Starlette, Sanic, Tornado, AIOHTTP, and Robyn all fall into this group. They solve similar problems, but each has its own strengths, maturity level, and trade-offs.

Top 15 Best Frameworks You Should Consider

Django

GitHub: https://github.com/django/django 

Django turned 20 in 2025 and is still the framework most enterprise Python web work runs on. It ships an ORM, an admin interface, authentication, a templating system, form handling, security middleware (CSRF, XSS, SQL injection protections), and an ecosystem of third-party packages for nearly anything else. Django’s conventions are opinionated, which is what makes it fast to build.

​​The concern of using Django is weight. For a small API, Django gives you machinery you are not using. For complex queries, its ORM is less flexible than SQLAlchemy. Although Django 4.x and 5.x support async views, the framework's foundation is sync, and writing async code inside a Django project still requires more thought than it does inside a framework built async-first.

Django’s Use Cases: Content-heavy applications, SaaS products with user accounts, admin dashboards, internal tools, and systems where a ready-made admin panel can save weeks of custom UI work.

How we use Django at Relia: We have used Django for multi-tenant applications where the admin panel reduced the need for custom dashboard work. Our typical stack combines Django with Django REST Framework for APIs, Celery for background jobs, and PostgreSQL for storage.

Flask

GitHub: https://github.com/pallets/flask 

Flask is the microframework most Python developers learn second, after Django. Its features include routing, a development server, and templating through Jinja2. Everything else like ORM, authentication, form validation, admin is a separate library, and the team chooses. 

The Flask community is large enough that there is a well-maintained extension for almost any need. The learning curve is gentle, which makes Flask a good starting point for teams transitioning from another language.

The limitations come from the sync model and the freedom itself. High-concurrency workloads need Quart (Flask's async fork) or a different framework. And the flexibility that makes Flask suitable for small projects can turn into architectural drift on big ones. For example, 2 engineers can implement the same feature three different ways, with no framework convention pointing toward the right one.

Flask’s Use Cases: Small to mid-size APIs, internal tools, MVP and any project where the team wants to make its own architectural choices rather than inherit a stack.

FastAPI

GitHub: https://github.com/tiangolo/fastapi 

FastAPI became the standard for new Python APIs sometime around 2023, and by 2026 most new Python API projects use it.

FastAPI is built on Starlette and Pydantic, gives you typed requests and responses with runtime validation, generates OpenAPI documentation automatically, and runs async by default. Performance is in the same range as Go and Node frameworks for most workloads, and far ahead of Django or Flask.

However, FastAPI is less convenient for traditional server-rendered apps because it has no built-in templating story. Also, its async-first nature means the team has to think about async correctness everywhere.

How we use FastAPI at Relia: FastAPI is our default for any new API engagement, especially when the client has a React Native or React frontend that benefits from typed contracts. Our usual pairing is FastAPI with SQLModel or SQLAlchemy for data, Pydantic for validation, and asyncpg for PostgreSQL access.

Litestar

GitHub: https://github.com/litestar-org/litestar 

Litestar, formerly Starlite, is one of the strongest alternatives to FastAPI. It follows a similar typed and async-first approach, but adds more built-in structure for larger applications. It includes strong dependency injection, OpenAPI support, DTOs, WebSocket channels, scheduled tasks, and a more opinionated project architecture.

Teams often choose Litestar when they like FastAPI’s typed API model but want clearer conventions as the application grows. For projects with many endpoints, complex validation, background work, or WebSocket features, Litestar can feel more organized than a looser FastAPI setup.

But, Litestar's community size is much smaller than FastAPI's. So finding answers to edge-case problems is harder and requires more internal experience from the team. Most teams pick Litestar because they hit a wall with FastAPI on a larger project, not because they started there.

Starlette

GitHub: https://github.com/encode/starlette 

Starlette is the ASGI toolkit underneath FastAPI and several other frameworks. It is rarely used as the top-level framework for an application, but the foundation other frameworks build on.

The Starlette toolkit gives you async routing, middleware, WebSocket support, background tasks, session management, CORS handling, static file serving, and a test client. Together they are enough to build a working web service, but without the conveniences of a higher-level framework.

Pick Starlette directly when you need async routing and middleware primitives without the higher-level features of FastAPI or Litestar. We usually use Starlette when building our own framework or a very specialized service.

Sanic

GitHub: https://github.com/sanic-org/sanic 

Sanic is one of the oldest async Python frameworks, looking and feeling like Flask but runs async. Throughput is an outstanding feature when Sanic handles simple requests. It compiles request handlers efficiently, ships its own production-grade ASGI server (Sanic Server), and supports both worker-based and shared-loop concurrency models.

Sanic is the right pick for high-throughput APIs and real-time services where the team values raw speed over the type safety and OpenAPI conveniences. However, the community is smaller than FastAPI's and the ecosystem is still growing slowly.

Tornado

GitHub: https://github.com/tornadoweb/tornado 

Tornado is one of the oldest async Python web frameworks still in active use. It was originally developed at FriendFeed and open-sourced in 2009. Tornado predates Python’s native async and await syntax, so it was handling asynchronous workloads years before modern async frameworks became common.

Networking is the main strength of Tornado. It was not built as a web framework first, but as an async networking framework with its own HTTP server and I/O tools. So, Tornado is useful for applications that rely on long-lived connections like WebSockets, long-polling, server-sent events, and custom TCP protocols.

Tornado’s routing, templates, and request handlers still work, but they can feel older compared with newer ASGI-based frameworks. Thus, it is often used for the project with deep networking control, or when the team is already maintaining an existing Tornado codebase.

Pyramid

GitHub: https://github.com/Pylons/pyramid

Pyramid sits between Django and Flask philosophically. It can start small like a microframework and grow into something fuller-featured as the application gets more complex.

The outstanding feature of Pyramid is configurability that lets the team choose almost every architectural component like templating engine, ORM, URL routing style, etc. None of these are pre-decided, all of them are pluggable.

The traversal-style URL system is unusual among modern frameworks. Instead of matching URLs to view functions, Pyramid can walk a hierarchical object graph to find the right resource, ideal for CMs, file systems, and any application with deep hierarchical data.

Pyramid is not as common a choice for new projects today as Django or FastAPI. The community is smaller, the ecosystem of third-party packages is thinner, and the configurability is also a tired problem for teams that want defaults to follow.

Bottle

GitHub: https://github.com/bottlepy/bottle

Bottle is a microframework that fits in a single Python file with no dependencies outside the standard library. The standout feature is portability where a single bottle.py file you can drop into any Python environment like embedded systems, environments where you cannot pip install anything, or scenarios where the entire web server needs to be reviewable as one piece of code.

Bottle is the simplest framework on this list, and the fastest to learn. Most engineers can read the entire bottle.py source in an afternoon and understand exactly how every feature works.

However, Bottle is rarely the right choice for anything that will grow, because the single-file architecture makes it hard to organize a larger application cleanly.

Falcon

GitHub: https://github.com/falconry/falcon 

Falcon is a minimal framework tuned specifically for high-performance REST APIs and microservices. It is older than FastAPI and was originally sync-only, with async support added in version 3. You can’t auto-generate OpenAPI specs, validate against Pydantic schemas, and bundle the developer experience features. 

Falcon is fastest for simple JSON APIs that do not need the validation and OpenAPI features FastAPI gives you for free. Teams pick it when raw speed and a small footprint matter more than developer experience.

CherryPy

GitHub: https://github.com/cherrypy/cherrypy 

CherryPy is one of the oldest Python web frameworks still in active development. Its model is object-oriented and HTTP-aware: classes map directly to URL paths. It is a strong choice for long-running services, internal tools, and embedded applications where Python is being used as part of a larger non-web system.

For pure web work, CherryPy feels dated. The OOP-mapped routing pattern is less common than decorator-based routing today. The ecosystem is small, and the framework rarely shows up in modern technology comparisons.

CherryPy is rarely the choice for a new external web app today, but the people who use it tend to keep using it.

Dash

GitHub: https://github.com/plotly/dash 

Dash is a framework purpose-built for analytical web applications, including interactive dashboards, data exploration tools, and visualization apps. It is built on Flask and Plotly, and it lets data scientists build a working dashboard in pure Python without writing JavaScript. 

Dash is not the right choice for typical web applications. It is the right answer for internal analytics tools, executive dashboards, and customer-facing data apps where the engineering team is small and the data team is the one shipping.

AIOHTTP

GitHub: https://github.com/aio-libs/aiohttp 

AIOHTTP is both an async HTTP server and an async HTTP client. The server side competes with Sanic and Starlette for low-level async work. The client side is the standard async HTTP client for Python, used by a large fraction of services that need to call other services. 

Teams pick AIOHTTP for custom async work like scraping pipelines, proxy services, anything that does not fit a typical request/response shape.

The AIOHTTP framework is less convenient than FastAPI for typed APIs and produces no automatic OpenAPI documentation. Teams building modern REST APIs usually pick FastAPI, whereas teams building lower-level async services usually pick AIOHTTP.

Quart

GitHub: https://github.com/pallets/quart 

Quart is a Flask-compatible async web framework, originally by Phil Jones and now maintained as part of the Pallets project, the same organization that maintains Flask itself. The API mirrors Flask's almost exactly: the same decorators, blueprint system, request and response objects. The difference is that every handler is async.

A Flask application can usually be ported to Quart by changing the import line and converting handlers to async functions. The rest of the code, including templates, extensions, and project structure, typically works without modification. 

For teams with significant Flask codebases that need async support for one specific reason (long-running requests, WebSocket support, high concurrency), Quart is the smoothest path forward.

Robyn

GitHub: https://github.com/sparckles/Robyn 

Robyn is a newer Python web framework that takes a different approach to performance. Its runtime is written in Rust, while developers still write application logic in Python and let Rust handle the lower-level work like request handling, routing, and concurrency. So, developer experience is close to normal Python.

Robyn is known for raw throughput, often matching or exceeding the performance of Node.js and Go frameworks on simple workloads. The Rust runtime does the heavy lifting, while the developer continues to write handler functions in pure Python.

Robyn has a smaller ecosystem, fewer tutorials, and a much smaller community than frameworks like FastAPI, Flask, or Django. Documentation is improving, but teams can face more early-adopter friction.

>> Read more: Top 10+ Best Python Development Companies In Australia

Conclusion

Python frameworks improve code organization, productivity, and software development efficiency. Python developers shouldn't learn all 15 Python frameworks, but rather choose the ones that excite them, build projects with them, and master them.

Here is the article about top 15 Python frameworks you can consider for your projects. Hope this blog finds you well.

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