As we enter 2025, Python web frameworks are becoming more advanced and diverse than ever. They are empowering developers to create everything from simple sites to complex web applications. Finding the best Python framework for web development is key to building efficient and scalable solutions. In this article, we’ll walk through a comprehensive list of Python web frameworks, showcasing the best ones developers can use in 2025. We’ll also compare these frameworks based on their speed, reliability, features, and more.
Web frameworks are pre-existing software packages which make the development and maintenance of web applications simpler. They offer a framework and guidelines that help developers work through common tasks such as routing, database interaction, and user authentication more easily.
By employing a web framework, the developer no longer needs to keep re-inventing the wheel with each project. Most frameworks come bundled with built-in libraries and utilities that make the development process faster. The better-known instances include Django with Python, Rails with Ruby, and Express with Node.js. Using these frameworks ensures that developers spend less time on building functions from scratch and addressing lower-level matters.
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Python provides a variety of web development frameworks to meet various project requirements and developer tastes:
The richness of Python web frameworks guarantees that developers can choose the appropriate tool depending on project needs, team skills, and performance requirements. From lean solutions to full-fledged solutions, Python’s ecosystem has everything in between web development and data science requirements.
Front-end and back-end frameworks play distinct roles in web development, each catering to different aspects of a web application. Below are key differences between the two:
Feature | Front-End Frameworks (Python) | Back-End Frameworks (Python) |
Focus | Client-side (User Interface & Experience) | Server-side (Logic, Database, Authentication) |
Primary Role | Building interactive and responsive interfaces | Handling requests, processing data, and managing databases |
Common Technologies | HTML, CSS, JavaScript, and Python (via frameworks like Anvil, PyScript, Streamlit, JustPy) | Python |
Tasks Handled | UI rendering, user interaction, and state management | Authentication, database integration, server-side security |
Tooling Provided | Widgets, event handling, layout rendering, interactivity | Routing, middleware, ORM, API development |
Examples (Python only) | Anvil, PyScript, Streamlit, JustPy | Django, Flask, FastAPI |
Execution Environment | Runs in the browser or via frontend-specific servers | Runs on the server |
With web development rapidly advancing in 2025, Python stands out for its clean syntax and powerful ecosystem. In this section, we’ll explore 10 popular Python web frameworks, offering a brief look at their strengths and weaknesses.
Django is a Python web framework based on the Model-View-Template (MVT) architecture. Used by leading companies such as Google, YouTube, and Instagram, Django has increased in popularity with Python’s swift growth. It supports the Convention Over Configuration and DRY (Don’t Repeat Yourself) principles, simplifying web development.
Django is built with security considerations, providing protection in the form of blocking code execution in templates. Its strong set of features sets it apart from other back-end frameworks.
For further information about Django, please visit here.
Also Read: Deploying Django App on Google Compute Engine
Flask is a lightweight Python microframework that is flexible and easy to use. First released in 2010, Flask only sends bare essentials by default, and the programmer can pick their own elements for database and authentication-like features. Based on Werkzeug and Jinja2, Flask is best suited for small-to-medium-sized applications, APIs, and projects that demand direct control.
In contrast to Django, Flask does not impose structure or include built-in features, offering greater liberty in app development. While light, it’s used by production-grade services of industry leaders like Netflix, Reddit, and Airbnb.
For further information about Flask, please visit here.
Also Read: Which is Better for Machine Learning: Flask vs Django?
FastAPI is a new, high-performance Python framework that is aimed at building APIs. It leverages Python type hints for automatic validation and documentation of data, with exceptional performance due to asynchronous programming. It is built on Starlette and Pydantic and is optimized for developers looking to create fast, efficient, and scalable API services.
FastAPI differentiates itself in the world of API development by adopting OpenAPI standards and including auto-generated interactive documentation. Its usage has increased immensely since its release in 2018, and now leads Django by monthly downloads on PyPI.
For further information about FastAPI, please visit here.
Tornado is an open-source, high-performance Python web application framework and asynchronous networking library created originally by FriendFeed. It is optimized for making thousands of connections concurrently. This makes it a very good choice for real-time apps, such as live data feeds, chat platforms, and long-poll WebSocket connections.
Based on non-blocking I/O and Python’s asyncio integration, Tornado shines in applications with high concurrency and low latency requirements. It also comes with features such as built-in authentication of users, support for custom templates, third-party auth, and internationalization.
For further information about Tornado, please visit here.
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Pyramid is an open-source Python web framework. It is as simple as microframeworks like Flask, yet scalable as full-stack choices like Django. Aiming for the middle ground between small prototype apps and big business enterprise apps, it is both flexible and solid. Popular with major businesses such as Dropbox, Mozilla, and SurveyMonkey, Pyramid has a modular structure, testability features, and an elegant design.
It supports several templating engines, routing frameworks, and authentication methods, which makes it very customizable according to project requirements. Pyramid is also WSGI-compliant and plays nicely with libraries such as SQLAlchemy, but can use other database tools if you want.
For further information about Pyramid, please visit here.
CherryPy is a lightweight, object-oriented web framework for Python that allows developers to write web applications rapidly and effectively. Being one of the oldest Python web frameworks (first published in 2002), it’s fast, secure, and minimalist.
CherryPy enables developers to code web apps like any other Python application using OOP concepts. Although it doesn’t have built-in templating or ORM systems, it’s very extensible and plays well with external libraries and tools. Netflix, Hulu, and Indigo Domotics are some of the companies that use CherryPy in their tech stack.
For further information about CherryPy, please visit here.
Dash framework are based on a component-based structure with interactive widgets such as sliders, dropdowns, and graphs. It is well-optimized for building analytical dashboards, real-time monitoring dashboards for web applications, and responsive, mobile-friendly interfaces. Based on Flask, Plotly.js, and React.js, Dash is flexible and powerful yet easy to use for Python programmers.
For further information about Dash, please visit here.
PyScript is an open-source framework developed by Anaconda in 2022 to let developers create interactive web apps in Python within the browser. It embeds Python code inside HTML, making it possible to build rich, client-side apps without requiring conventional JavaScript programming. It takes advantage of technologies such as Pyodide and WebAssembly to run Python code in the browser environment.
For further information about Pyscript, please visit here.
Also Read: All About Python 3.13.0
TurboGears is a robust, full-stack web application framework built for rapid data-driven application extensibility and scalability using Python. It leverages a group of best-in-class Python packages into a homogeneous framework with optimal developer productivity, requiring minimal setup. Initially implemented to fill in the gap between microframeworks and full-stack frameworks, TurboGears empowers developers with the ability to begin with an easy app that can be transformed into a rich solution when desired.
For further information about TurboGears, please visit here.
Sanic is a Python web framework for high-performance, asynchronous Python, optimized for speed and scalability. It’s built on Python 3.9+ and uses asyncio with uvloop, allowing non-blocking requests to be handled. This makes it perfect for applications that need high concurrency and real-time data processing.
The API of Sanic is designed similar to Flask, making an easy learning experience for developers who already know Flask
For further information about Sanic, please visit here.
Python has a diverse ecosystem of web frameworks, each designed for particular development requirements varying from rapid prototyping to production-level and scalable applications. The table below compares the popular Python web frameworks listed above, on the basis of their architecture, performance, simplicity, best use cases, important features, and limitations.
Framework | Type | Async Support | Performance | Learning Curve | Ideal For | Notable Features | Limitations |
Django | Full-stack | Limited | Moderate | Moderate | Large-scale apps, Admin panels | ORM, Admin interface, Authentication, Batteries-included | Heavyweight, less flexible for microservices |
FastAPI | Microframework | Native | Very High | Easy-Moderate | APIs, async apps, modern web | Automatic docs (Swagger), async/await, type hints | Newer ecosystem, async learning curve |
Flask | Microframework | Via plugins | Moderate | Very Easy | Simple web apps, MVPs, microservices | Minimalist, Jinja templating, Extensions for ORM, Auth | Not async-native, lacks built-in tools |
Tornado | Asynchronous framework | Native | High | Moderate-Hard | Real-time apps, long-lived connections | Non-blocking I/O, WebSockets, Coroutine-based | Complex syntax, limited community |
Pyramid | Hybrid (flexible size) | Via plugins | High | Moderate | Scalable apps, testing-intensive dev | Minimalism + flexibility, HTML form support, routing system | Requires deeper setup for simple apps |
CherryPy | Minimalist | Threaded | High | Easy | Portable apps, embedded systems | Built-in WSGI server, multithreaded, object-oriented | Sparse docs, no built-in templating or DB access |
Dash | Analytics/Dashboard | No (sync) | Moderate | Easy | Data viz, dashboards | Interactive graphs, Plotly support, mobile-friendly UI | Limited to data apps, not for general web dev |
PyScript | Front-end framework | N/A | Depends on browser | Easy | Embedding Python in HTML | Run Python in browser, JS interoperability, UI components | Limited browser support, not for full backend |
TurboGears | Full-stack | Partial | High | Moderate | Scalable full-stack apps | ORM, templating, hybrid MVC/micro support | Less popular, requires configuration |
Sanic | Micro/Async framework | Full | Very High | Easy-Moderate | High-performance async apps | Async/await support, uvloop, Flask-like syntax, WSGI/ASGI compatible | Smaller community, async-only |
Globally, Python is quickly becoming programmers’ first choice of programming language. Its simple syntax lowers the learning barrier considerably, allowing beginners to learn the fundamentals and start developing applications with little frustration. Its flexibility differentiates Python from competitors. Python succeeds in web application development, data analysis, applications of AI, and task automation. With such a spectrum of applications, it is easy for developers to change specialties without neglecting their central language knowledge.
Also, Python’s extensive ecosystem is the biggest contributor to its adoption level. Web frameworks such as Django and Flask remove the hassle of writing the same common functions over and over again. Such pre-built configurations tackle common problems, greatly reducing development cycles.
The community strength of Python is particularly noteworthy. Rich documentation, active forums, and plenty of learning resources ensure that troubleshooting is easy. Developers hardly get stuck for long when help is so easily accessible.
Using Python web frameworks can offer several advantages to your development cycle, including:
Python web frameworks offer a great balance between developer productivity and application performance. They take advantage of Python’s readability and large library ecosystem and add specialized tools for the difficulties of web development. From developing a straightforward API using Flask to creating a sophisticated application using Django, these frameworks simplify development without losing Python’s friendly feel.
Choosing the best Python web framework completely depends on your project’s particular requirements, size, and development objectives. Whether you’re developing lightweight APIs, data-driven dashboards, or enterprise-scale applications. Python has a vibrant ecosystem of frameworks each with its own strengths.
Finally, the better option depends on your team’s skill, performance needs, and scalability in the future. With a grasp of the fundamental traits and limitations of each framework, you can make a confident selection of the one that works best for your development plan.
A. Flask is often recommended for beginners due to its simplicity, minimal setup, and clear documentation. It provides enough flexibility for small projects and is easy to learn.
A. Django and FastAPI are highly scalable. Django offers a full-stack solution with built-in components, while FastAPI’s async support makes it suitable for modern, high-performance APIs.
A. Dash is ideal for building interactive data dashboards, especially for data scientists. PyScript is great when you want to embed Python directly into frontend applications and enable Python-JS interaction.
A. FastAPI is newer and offers modern features like asynchronous support and automatic documentation. Flask is more mature and has a larger ecosystem. The choice depends on project needs.
A. Yes, both CherryPy and Pyramid are capable of handling large-scale applications. CherryPy is known for its minimalism and performance, while Pyramid offers flexibility with a robust feature set.
A. FastAPI, Sanic, and Tornado support asynchronous programming out of the box. These are ideal when handling concurrent requests or building real-time applications.