Top Python Web Development Frameworks in 2025

With 16 million Python developers worldwide and that number steadily growing, Python continues to thrive as one of the most popular programming languages. It has long been favored by data scientists, machine learning engineers, and backend developers for its simplicity and powerful ecosystem.

With frameworks catering to various use cases, from full-stack web development to data visualization, Python offers something for every developer. Thanks to its vibrant community and robust ecosystem, developers have a wide range of options when building web apps. However, the sheer number of choices can make it difficult to select the right framework for your project.

That's why we've reviewed the top Python frameworks for building web apps, comparing the strengths and weaknesses of each. In this article we will review the following frameworks:

1. Reflex
2. Django
3. Flask
4. Streamlit
5. Gradio
6. Plotly Dash
7. FastAPI

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Reflex represents a shift in web development by enabling developers to build their full-stack web apps entirely in a single language, Python. Designed to seamlessly integrate both the backend and frontend, Reflex provides over 60 built-in components that developers can easily customize and extend, with the option to bring in their own components (React under the hood).

Pros

• Pure Python: Reflex allows you to build both your frontend and backend in Python.
• No boilerplate: Reflex handles the connection between your frontend and backend.
• Flexible and expressive: Reflex comes with over 60 UI components that are highly customizable (supporting Tailwind, custom CSS, etc.).
• Database management: Reflex integrates with SQLAlchemy and offers first-class support for SQLite, Postgres, and MySQL.
• Authentication: Reflex offers multiple authentication options, including Local Auth, Google Auth, Captcha, Magic Link Auth, and Clerk Auth, allowing for easy user management.
• Deployment: Reflex apps can be deployed to the cloud with a single command (reflex deploy).

Cons

• Ecosystem: Reflex is a newer framework, so its ecosystem (plugins) is smaller compared to more traditional alternatives like Django. However, with 20k+ stars on GitHub, it is one of the fastest-growing Python frameworks.
• Breaking changes: Reflex is still maturing, so its APIs are subject to change.

Django is a mature, "batteries included" framework with a large ecosystem offering plugins for additional functionality like authentication, admin interfaces, content management systems, and more. As one of the oldest Python frameworks, Django not only has a large community and ecosystem but has also been battle-tested over many years.

Pros

• Mature: Django has been around for a long time and has a large ecosystem and community.
• Admin interface: Django includes a built-in admin interface for content management, making it easier for developers to manage app data.
• ORM: Django has a built in database ORM for interacting with your database.
• Plugin system: Django’s plugin system allows you to extend its functionality with ease, enabling integration with tools like authentication, payments, and analytics.
• Security: Django comes with built-in security features, such as protection against SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).

Cons

• Frontend: Django requires a separate frontend framework. While it includes an HTML templating system, it’s not as powerful as React and other modern frontend technologies.
• Performance: Django is not as performant as FastAPI, which is designed explicitly with performance and async in mind (ASGI).
• Monolithic: Django is a monolithic framework, which can make scaling large web apps more challenging.

Flask is a lightweight (micro) web framework that is easy to get started with and is a good choice for building microservices or smaller web applications. With its simplicity and flexibility, Flask is also often used for creating APIs in Python, making it a popular choice for developers building Flask-based applications.

Flask vs Django:

Flask is ideal for projects that require simplicity and flexibility, where you can choose exactly what components you need. In contrast, Django is a more feature-complete framework with built-in solutions for many common web development tasks, such as authentication and an admin interface. This makes Flask a great choice for small to medium-sized projects or when building Flask APIs in Python, while Django shines in larger, more complex applications where an all-in-one solution is beneficial.

Pros

• Lightweight: Flask is a lightweight framework that is easy to get started with.
• Flexible: Flask has a minimalistic approach to web development, providing a solid foundation on which developers can build.
• Customizable: Its modular design allows developers to pick and choose the extensions and libraries they need, making it highly adaptable to different use cases.
• Large ecosystem: Flask has a mature ecosystem of extensions that can add functionality like database integration, authentication, and more.

Cons

• Synchronous: Flask is synchronous by default, which can be a bottleneck in performance-critical applications.
• Jinja templating: Web apps built with Flask are typically built using Jinja templating, which is not as powerful or modern as React.
• Documentation: Compared to FastAPI, Flask requires using additional libraries to build documentation.
• Scaling: Flask's simplicity can become a limitation for large, complex projects, as it requires more effort to manage dependencies and structure.

Streamlit has gained considerable traction among data scientists for its ability to convert data scripts into interactive web applications quickly with minimal coding. It is ideal for prototyping, building dashboards, and showcasing data insights. However, it is less suited for applications requiring complex user interfaces or persistent interactivity.

Pros

• Quick prototyping: Streamlit excels at rapidly converting Python scripts into web applications with minimal effort.
• Data visualization: Streamlit has built-in components for common data visualization tasks, making it ideal for dashboards.
• Ease of use: Streamlit's declarative syntax allows developers to focus on functionality rather than boilerplate code.
• Community-driven examples: Streamlit features a robust app gallery with examples and templates on platforms like Github, showcasing how to build various Streamlit apps and dashboards.

Cons

• Execution Inefficiency: Streamlit re-runs the entire application code upon every input change, leading to potential inefficiencies. This makes it less suitable for applications with complex workflows or large-scale data that require frequent updates.
• Limited UI components: Compared to frameworks like Reflex or React, Streamlit offers a restricted set of UI elements, making it less suitable for building complex, custom user interfaces.
• Scalability: Streamlit is not designed for building long-running or highly interactive applications with complex workflows.
• State management: Managing application state can become challenging for more complex apps requiring dynamic user interactions.
• Caching complications: Streamlit provides caching to improve performance, but caching large objects can introduce challenges, such as high memory consumption, slow performance, or the return of outdated data. Additionally, if cache invalidation is not handled properly, it can lead to unpredictable behavior, making it tricky to implement effective caching strategies.

Gradio is an open-source Python library that allows developers to create user-friendly, web-based interfaces for machine learning models with ease. It is especially favored for prototyping, demonstrating, and sharing ML models with minimal setup. Gradio integrates seamlessly with popular ML libraries like TensorFlow, PyTorch, and Hugging Face, making it a top choice for ML practitioners.

Pros

• Easy deployment: Gradio apps can be deployed quickly, including directly to Hugging Face Spaces, enabling effortless sharing.
• ML integration: It integrates seamlessly with popular machine learning libraries like TensorFlow, PyTorch, and Hugging Face, simplifying workflows for ML practitioners.

Cons

• Limited scope: Gradio is best suited for experimenting with machine learning models and creating demos, but it is not ideal for production-grade or highly complex web applications.
• Customization limitations: While sufficient for standard ML apps, creating highly customized or complex user interfaces often requires additional JavaScript or external libraries.
• Scalability: Gradio apps may struggle to scale effectively for large-scale production use cases.

Dash is a Python framework for building interactive analytical web applications. It is built on top of Flask, Plotly.js, and React.js, offering a robust environment for creating dashboards and data apps. Dash applications are structured into two main components: layout, which defines the visual structure using HTML and React components, and callbacks, which handle interactivity by responding to user inputs dynamically.

Dash is optimized for deployment efficiency, as each component of the app is independent, allowing for easy horizontal scaling. However, this comes with the trade-off of requiring stateless callbacks, where each component must be independent and self-contained, making development more challenging for those used to local or stateful programming.

Pros

• Rich data components: Dash includes an extensive library of pre-built data visualization and UI components, making it ideal for analytical applications.
• Multi-language support: Dash supports Python, R, and Julia, catering to a broad audience in the scientific and data communities.
• Academic backing: As one of the earliest frameworks for Python-based data visualization, Dash has a strong academic community.
• Plotly integration: Dash leverages Plotly.js for creating highly customizable and interactive visualizations.
• Deployment efficiency: Dash’s architecture allows for easy scaling of apps horizontally, making it suitable for deployment in distributed environments.

Cons

• Callback complexity: Managing interactivity with callbacks can become complicated in large or complex applications. Each component must operate independently without relying on global variables or stored intermediate states, which can increase development complexity.
• Cloud deployment: Plotly's hosting service primarily targets enterprise users, and while container deployment is possible, it adds extra complexity for individual developers or smaller teams.

FastAPI is a modern backend web framework that has gained popularity for building REST APIs. Built on top of Starlette and Pydantic, FastAPI is highly performant, ergonomic, and excels in scenarios requiring high performance and concurrent processing.

Pros

• Type hints: FastAPI leverages Python type hints, improving code quality and simplifying API interactions.
• Automatic documentation: FastAPI automatically generates OpenAPI (formerly Swagger) documentation for your API based on type hints.
• Data validation: FastAPI uses Pydantic for data validation, making it easy to validate request and response data.
• Performance: FastAPI is highly performant, often matching or surpassing the speed of Node.js and Go in benchmarks.
• Asynchronous support: FastAPI supports async programming, making it suitable for high-concurrency applications.

Cons

• Frontend: FastAPI does not provide a built-in frontend framework, so you will need to integrate it with a separate frontend solution.
• Onboarding: While FastAPI is powerful, it may be challenging for developers with little experience in deploying web apps. It works well for small to medium-sized organizations, but may require a steeper learning curve for individuals new to web development.
• Limited built-in features: Unlike full-stack frameworks like Django, FastAPI is focused on backend APIs and does not offer many built-in features for things like authentication, admin panels, or templating.

Reflex builds on top of FastAPI by adding a frontend layer and abstracting away the boilerplate required to connect the backend and frontend. This dramatically simplifies the development of full-stack apps. When deployed, Reflex apps are transpiled into a FastAPI backend with a React frontend.

Choosing the right Python framework for building your web applications depends on your specific needs, the type of app you're creating, the complexity of your project, and your preferred development workflow.

• Reflex is a great choice if you want to build interactive, full-stack apps entirely in Python. It simplifies the process by handling both frontend and backend seamlessly.
• Django works well for larger projects that need a lot of features and tools, offering everything you need to manage complex backend tasks.
• Flask is perfect for smaller projects or microservices, giving you more flexibility and keeping things lightweight.
• Streamlit and Plotly Dash are best for data-focused apps. Streamlit is great for quick prototypes that you know it will stay simple, but its design creates a lot of headaches as your app grows. Dash is better for detailed dashboards and visualizations.
• Gradio is perfect for developers working with machine learning models, offering an easy way to create interactive interfaces and share your work.
• FastAPI is ideal for creating fast and efficient APIs, especially for apps that need high performance and handle multiple requests at the same time.

Each framework has its strengths and limitations. By understanding what each one does best, you can select the framework that fits your project goals and makes development smoother and more efficient.