When you start a new web project with Python, choosing the right library or framework shapes how you build, maintain, and extend the application.

Should you pick a full-featured framework, a simple micro-framework, or one aimed at performance under high load?

This post helps you decide by comparing a number of popular libraries, showing where each shines, where it lags, and when it’s the best fit.

You’ll learn:

• How to evaluate web frameworks & libraries based on project needs
• What trade-offs matter (speed, community support, learning curve, extension ecosystem)
• Specific examples of libraries, with strengths & weaknesses
• Actionable advice: which library to pick depending on your project type

What to look for in a web library

Before we examine specific libraries, let's define criteria that truly matter when choosing one:

1. Request handling / routing: How the library maps URLs to logic, handles HTTP verbs, and manages parameters.
2. Template rendering / view layer: Whether the library gives built-in templating or you need to add one.
3. Database / ORM support: Whether you want built-in databases or prefer to plug in your choice (SQL, NoSQL).
4. Asynchronous / concurrency behaviour: If your app needs to deal with many simultaneous connections or asynchronous I/O.
5. Ecosystem & plugins / extensions: Available libraries for authentication, caching, validation, etc.
6. Documentation & community size: How easy it is to get help, find examples, and maintain code long-term.

Python libraries for web development

Django

Core features & fit

• Django follows a “batteries-included” philosophy, offering a built-in ORM, authentication, URL routing, admin interface, and templating engine.
• It boasts a large and active community.

When it works well

• Ideal for projects that require many built-in features, a strong structure, and scalable functionality.
• Perfect for data-driven websites, dashboards, or full-stack applications combining backend and frontend.

When it may be harder

• Django can feel heavy if your project only needs a few endpoints or APIs.
• It may introduce unnecessary overhead for lightweight microservices

TurboGears

Core features & fit

• A full-stack framework supporting MVC and integration with ORMs like SQLAlchemy.
• More opinionated than micro-frameworks but still flexible.

When it works well

• Useful if you want more structure than Flask but less rigidity than Django.
• Good for projects needing templates, built-in plumbing, and some flexibility.

When it may be harder

• Smaller community and fewer plugins than Django or Flask.
• Documentation may have gaps.

Hug

Core features & fit

• API-focused framework emphasizing Python typing and minimal syntax for endpoint creation.

When it works well

• Perfect for API-first applications, microservices, and smaller services needing quick endpoint development.

When it may be harder

• Smaller developer base and limited UI/template tooling. May need extra packages for features beyond HTTP endpoints.

Pyramid

Core features & fit

• Starts lean and extendable. Offers flexible routing, multiple templating engine options, and a choice of ORMs.

When it works well

• Ideal for lightweight projects that may grow, or enterprises needing custom architectures with varying module sizes.

When it may be harder

• Requires more upfront configuration and boilerplate compared to Django. Less “ready out of the box.”

Tornado

Core features & fit

• Designed for handling many concurrent connections. Supports non-blocking I/O, making it well-suited for real-time applications.

When it works well

• Excellent for real-time dashboards, streaming services, long polling, or WebSocket-heavy applications where concurrency matters.

When it may be harder

• Async programming has a steeper learning curve. Offers less prebuilt UI or administrative tooling.

NumPy/Pandas/Matplotlib

Core features & fit

• Libraries for data processing, analysis, and visualization.
• They integrate well with web apps, especially for dashboards or model-serving.

When it works well

• Use when your application requires data manipulation or visual display, such as compute pipelines, dashboards, or plotting.

When it may be harder

• Heavy memory usage for large datasets.
• Not suitable as primary web routing/view tools.
• Security and latency concerns when exposing via web endpoints.

Flask

Core features & fit

• Flask is a minimal framework, allowing you to select which components to use, such as ORM, templating, and validation. Clear routing is provided via decorators.

When it works well

• Great for APIs, MVPs, and services with limited UI needs.
• Works well if you want fine control over components and prefer simplicity.

When it may be harder

• Requires assembling and managing dependencies yourself.
• There’s a risk of fragmentation due to plugin choices and less structure compared to full-stack frameworks.

Web2py

Core features & fit

• Full-stack framework with an integrated web IDE and built-in database abstraction. Easy to set up.

When it works well

• Great for fast prototyping, educational projects, or when you want minimal configuration.

When it may be harder

• Smaller ecosystem and fewer resources/examples compared to mainstream frameworks.

Bottle

Core features & fit

• Object-oriented framework + integrated HTTP server. URLs are handled via Python classes/methods.

When it works well

• Good for small to medium services, embedded HTTP use, or APIs where you want clear routing logic.

When it may be harder

• Smaller community, fewer modern extensions, and less guidance for large applications.

CherryPy

Core features & fit

• Object-oriented framework + integrated HTTP server. URLs are handled via Python classes/methods.

When it works well

• Good for small to medium services, embedded HTTP use, or APIs where you want clear routing logic.

Where it may be harder

• Smaller community, fewer modern extensions, and less guidance for large applications.

Falcon

Core features & fit

• Minimal abstraction framework designed for fast, high-performance API endpoints.

When it works well

• Best for low-latency APIs or services that need to respond quickly without extra overhead.

When it may be harder

• Limited UI support and fewer built-in features, requiring more manual development or integration.

AIOHTTP

Core features & fit

• Async HTTP framework supporting standard HTTP methods and WebSockets. Works with Python’s asyncio.

When it works well

• Perfect for apps that rely on asynchronous IO, such as streaming services, WebSockets, or highly concurrent applications.

When it may be harder

• Async code can be tricky to debug.
• Some ecosystem tools assume synchronous code, which may cause integration challenges.

CubicWeb

Core features & fit

• Niche or domain-specific libraries, each offering unique patterns or features. Communities are smaller.

When it works well

• Useful for specialized projects that match the library’s design.
• Good for research, exploration, or experimental work.

When it may be harder

• Limited documentation, smaller community support, and fewer general-purpose plugins.

Matching library to project type

Here is guidance for typical web/data engineering / ML-adjacent project types, and which libraries are likely to serve you best.

Practical to-dos for smooth development

• Start with a clear project structure, even if using microframeworks.
• Define where views, models (or data processing), and templates live.
• Use environment-specific configuration so production/dev differ (in logging, performance settings).
• Monitor where response times lag, which endpoints use lots of CPU/memory. Cache wisely: template fragments, repeated database queries, static assets.
• Minimize dependencies: avoid pulling in large libraries you only use a little.
• Secure defaults: pay attention to input validation, CSRF, and XSS. Libraries with smaller ecosystems may leave more to you.
• Version and update dependencies regularly; monitor security vulnerabilities

FAQs