15 Best Python Frameworks: Which One Should You Use?

When working on a Python-based project, there’s a need for speed and quality paired with reliability and innovation. So it’s important to select the right Python framework, prioritizing speed, robust security, or flexible integrations.

With these frameworks, you can reduce time-to-market and ensure reliable performance, directly impacting bottom-line outcomes. But for that, you need to match your project goals with the right framework.

But first, let’s look at what a Python framework is and what the different types are. So we begin.

What is a Python Framework?

A Python framework is a set of tools and code libraries for building web applications and software based on the Python programming language. It can streamline development, enforce best practices, and eliminate repetitive tasks.

These frameworks save you from writing everything from scratch. They handle common functionalities, like routing, database interactions, and security, while you focus on implementing business logic and unique features.

All in all, these frameworks provide standardized ways to build applications. They ensure efficiency, scalability, and maintainability.

Python Frameworks vs Python Libraries

While both frameworks and libraries are meant to help write code faster, they serve fundamentally different purposes.

A Python library is a collection of pre-written functions and modules that you can call for specific tasks. Like, NumPy for numerical computing or Requests for HTTP calls. You control the flow of your program, using libraries as needed.

A Python framework, on the other hand, dictates the structure of your application. It provides a foundation with predefined architecture, patterns, and rules. Like, Django for web development or PyTorch for machine learning. Instead of calling functions, your code integrates into the framework’s workflow. It handles core operations automatically.

The choice between frameworks and libraries depends on whether you need flexibility (libraries) or a structured approach (frameworks). So for the best decision with respect to framework selection and subsequent development, hire our expert Python developers.

Types of Python Frameworks

From Django to Flask to TensorFlow and more, Python frameworks fall into distinct categories. Each type is optimized for different development needs. Let’s check them one-by-one.

Full-stack Framework

These comprehensive solutions come with built-in components for nearly every aspect of web development. That includes database management, templating, authentication, and admin panels.

Django is the most popular example, offering an “all-in-one” approach. It’s ideal for complex applications that need rapid development without relying on third-party tools.

Microframework

Lightweight and minimal, these frameworks provide only the essentials, giving developers more control over architecture and libraries. Flask and Bottle are prime examples, perfect for smaller projects, APIs, or when you need flexibility to handpick components.

Asynchronous Frameworks

Asynchronous frameworks are optimized for high-performance, real-time application. They leverage Python’s async/await syntax to handle thousands of concurrent connections efficiently. FastAPI and Sanic lead this category, making them ideal for microservices, WebSockets, and low-latency systems.

It’s important to choose the right framework based on project scope, performance needs, and team expertise. That is a critical early decision in Python development.

Best Python Frameworks for Web Developer

Python’s versatility makes it one of the top web development languages. But when building the best web application based on it, one of the first steps would be to select the Python framework. Here are 10 of the best options available.

Flask

Flask is a lightweight microframework perfect for small to medium projects. With minimal boilerplate, it offers flexibility to choose extensions as needed. This framework is ideal for REST APIs and quick prototypes. Its simplicity makes it a favorite among beginners and experts alike.

Key Features of Flask

• Minimalist & lightweight–No built-in bloat, only core features.
• Extensible via extensions (SQLAlchemy, Flask-Security, etc.).
• Built-in development server & debugger.
• Jinja2 templating engine for dynamic HTML.
• RESTful request handling for APIs.

Django

Django is a full-stack, “batteries-included” framework for rapid development. It includes an ORM, admin panel, and authentication out-of-the-box. This framework is best for scalable web apps like CMS or eCommerce. Its convention over configuration speeds up production-ready deployments.

Key Features of Django

• “Batteries-included”–ORM, auth, admin panel, templating.
• Scalable & secure (CSRF, XSS, SQL injection protection).
• Built-in REST framework (DRF) for APIs.
• MVT (Model-View-Template) architecture.
• Automatic admin interface.

Pyramid

Pyramid balances flexibility and structure, scaling from simple apps to complex systems. Its modular design lets developers use only what they need. Pyramid is great for both REST APIs and traditional web apps. It’s less opinionated than Django but more robust than Flask.

Key Features of Pyramid

• Flexible–Can be as minimal as Flask or as full-featured as Django.
• Supports both SQL & NoSQL databases.
• URL routing & templating included.
• Authentication & authorization built-in.
• Highly scalable for large apps.

FastAPI

FastAPI is a modern, high-performance framework for building APIs with Python 3.7+. Automatic docs (Swagger/OpenAPI), async support, and data validation via Pydantic make it ideal for microservices and real-time applications. One of the fastest Python frameworks available.

Key Features of FastAPI

• Blazing-fast performance (ASGI support, Starlette-based).
• Automatic API documentation (Swagger & OpenAPI).
• Type hints & data validation (Pydantic).
• Async/await support for high concurrency.
• Easy dependency injection.

PyTorch

PyTorch is one of the top web frameworks, favored for deep learning, research, and production. Its dynamic computation graph and intuitive interface simplify neural network development. Widely used in academia and industry for AI/ML projects, from vision to NLP.

Key Features of PyTorch

• Dynamic computation graphs (great for research).
• GPU acceleration (CUDA support).
• Strong deep learning & neural network tools.
• Python-first, easy debugging.
• Used by Facebook, Tesla,and OpenAI.

TensorFlow

Developed by Google, TensorFlow is a powerhouse for machine learning and neural networks. It offers robust tools for training and deploying models at scale. This framework supports mobile and edge devices with TensorFlow Lite. Ideal for enterprise-grade AI solutions.

Key Features of TensorFlow

• Static computation graphs (better for production).
• Scalable for large ML models.
• TensorBoard for visualization.
• Deployable on mobile & edge devices (TF Lite).
• Backed by Google.

CherryPy

CherryPy is a minimalist, object-oriented web framework. Apps run on any WSGI server, making deployment easy. Despite its simplicity, it handles sessions, caching, and file uploads. Perfect for developers who want low-level control without excessive boilerplate.

Key Features of CherryPy

• Minimalist but powerful.
• Built-in HTTP/1.1-compliant server.
• Thread-pooled web server.
• Easy to embed in larger applications.
• Supports plugins & WSGI.

NumPy

NumPy is the foundation of scientific computing in Python. Its powerful N-dimensional arrays enable fast numerical operations. This framework is essential for data analysis, ML, and engineering tasks. It integrates seamlessly with Pandas, SciPy, and other data science tools.

Key Features of NumPy

• Efficient multidimensional arrays.
• Mathematical & statistical operations.
• Integrates with C/C++ & Fortran.
• Foundation for Pandas, SciPy, ML libs.
• Optimized for performance.

SciPy

Built on NumPy, SciPy provides advanced math, science, and engineering functions. Includes modules for optimization, signal processing, and statistics. This is a must-have for researchers and engineers working on complex computations or simulations.

Key Features of SciPy

• Advanced math & scientific computing.
• Built on NumPy.
• Optimization, FFT, signal processing.
• Linear algebra & statistics.
• Used in engineering & physics.

Pandas

Pandas simplifies data manipulation with DataFrame structures. Perfect for cleaning, analyzing, and visualizing structured data. Widely used in finance, analytics, and data science. This tool integrates well with NumPy, Matplotlib, and machine learning libraries.

Key Features of Pandas

• Data manipulation & analysis.
• DataFrames for structured data.
• Time-series handling.
• Merge, filter,and pivot operations.
• Essential for data cleaning & ETL.

Tornado

Tornado is an async framework designed for high performance and real-time services. Handles thousands of simultaneous connections efficiently. Ideal for WebSockets, long-polling, and applications requiring non-blocking I/O operations.

Key Features of Tornado

• Asynchronous networking for real-time apps.
• Handles long-polling & WebSockets.
• Built-in HTTP server.
• High performance under heavy traffic.
• Non-blocking I/O operations.

Bottle

Bottle is a single-file microframework with zero dependencies. Ultra-lightweight, it’s perfect for small apps, prototyping, or embedded systems. Despite its size, it supports routing, templating, and basic web server functionality.

Key Features of Bottle

• Single-file microframework (no dependencies).
• Built-in HTTP server.
• Simple routing & templating.
• Lightweight for small APIs & prototypes.
• WSGI-compatible.

CubicWeb

CubicWeb is a semantic web framework emphasizing reusability. Uses a component-based architecture for modular development. Best for data-centric applications requiring RDF, linked data, or complex querying. This framework showcases a steeper learning curve but is powerful for niche use cases.

Key Features of CubicWeb

• Semantic web framework (RDF, Linked Data support).
• Reusable components (cubes).
• Built-in database (RQL) & security.
• Highly modular.
• Supports multiple data backends.

Dash

Dash by Plotly simplifies building interactive data dashboards in Python. No JavaScript needed—create rich visualizations with pure Python. It’s ideal for analytics, BI tools, and internal reporting. Integrates seamlessly with Flask for customization.

Key Features of Dash

• Built for data visualization & dashboards.
• React.js-powered interactive UI.
• Works with Plotly, Pandas,and NumPy.
• Simple Python-to-web components.
• Enterprise-ready (used in analytics & BI).

Hug

Hug focuses on making API development simple and fast. Write once, deploy as CLI, local lib, or HTTP service. Designed for speed and developer happiness, this framework is great for microservices and rapid prototyping with minimal overhead.

Key Features of Hug

• Designed for API development.
• Write once, run as CLI, HTTP, or local.
• Automatic versioning & documentation.
• Performance optimized.
• Minimal boilerplate.

Each framework has strengths tailored to different use cases. Pick one that aligns with your project’s complexity, performance needs, and team expertise.

If you want help with integrating these tools and building the best web applications, consult with our professional Python development company.

How to Choose the Right Python Framework?

There’s no one-size-fits-all framework for all applications. You need to examine each of these frameworks and more based on project-specific needs, team expertise, long-term goals, and more.

Project Scope & Complexity

For large, feature-rich applications, Django’s all-in-one structure saves time. Smaller projects benefit from Flask or FastAPI’s lightweight flexibility. Match the framework’s capabilities to your project’s size and requirements.

Performance Needs

If speed is critical, FastAPI’s async support outperforms traditional frameworks. For CPU-intensive tasks, consider frameworks with Cython integration or those optimized for parallel processing.

Development Speed

Django’s built-in admin panel and ORM accelerate development. Microframeworks like Flask require more setup but offer faster iteration for simple prototypes or minimal MVPs.

Scalability

High-traffic apps need async frameworks (FastAPI, Tornado). Django scales well too but may require more optimization. Consider horizontal scaling capabilities from the start.

Learning Curve & Team Expertise

Django has more conventions to learn, while Flask is simpler for beginners. Choose based on your team’s familiarity to avoid costly onboarding delays.

Ecosystem & Libraries

Check if the framework supports plugins for your needs (e.g., Django REST Framework for APIs). A rich ecosystem reduces reinventing the wheel for common features.

Community & Support

Popular frameworks like Django and Flask have vast communities, ensuring quick troubleshooting. Niche frameworks may lack documentation or timely bug fixes.

Security Requirements

Django provides built-in protections against common threats (CSRF, SQLi). For other frameworks, verify security middleware availability and update frequency.

Test at least 2-3 frameworks with a small prototype before committing. The ideal one should align with your project’s goals and your team’s workflow.

FAQs on Best Python Frameworks

Let’s Summarize

The best Python framework depends on your project’s needs—whether you’re building a web app, API, data dashboard, or AI model. For example, Django excels for full-featured web applications. There’s also Flask and FastAPI offer flexibility for lightweight or high-performance APIs.

For data science, Pandas and PyTorch remain essential, while Dash simplifies interactive analytics. The key is matching the framework’s strengths to your goals. Start small if needed, scale when necessary, and leverage Python’s rich ecosystem to build efficiently.

If you want the best Python-based application for your project, then consult with our Python experts. Let’s connect today!