The top 4 Python backend frameworks for building entry level AI projects

A backend framework has ready-made components for developing the server side of websites. It consists of libraries that make the development process stress-free and convenient. 

Many backend frameworks are based on different programming languages such as JavaScript, Python, and Ruby.

Python backend frameworks, in the same way as Python, have easy-to-read syntax and are object-oriented and scalable. With these frameworks, you don’t need to worry about low-level details like sockets and protocols.

In this article, we’ll show you the best Python backend framework for 2025, their features, and their pros and cons. 

To ensure a balanced and accurate selection, I’ve spoken to senior backend engineers, combed through Reddit threads and G2 reviews, and compiled this list for you based on:

• High availability for developers

• Ease of deployment

• Community support

• Better alignment with Rest API-heavy architecture

• Longevity 

TLDR: 

• Django: A high-level framework for python backend, featuring built-in admin, authentication, and ORM capabilities to help you launch robust web applications quickly.

• Flask: A microframework renowned for its simplicity and flexibility, allowing you to start small and scale up as your project grows.

• CherryPy: A minimalistic Python web framework that enables you to build standalone web apps quickly, running on any system that supports Python.

• FastAPI: The modern, high-performance Python backend framework that uses type hints to offer automatic validation and documentation, making it a prime choice for crafting lightning-fast APIs with minimal boilerplate.

Django

Django is by far the most popular Python web framework, widely celebrated for its “batteries-included” approach that helps you avoid coding from scratch. 

It’s built specifically for developing web applications using Python, and it comes with an ORM, authentication system, admin interface, and more.

I especially loved using Django because follows an MVT (Model-View-Template) structure and DRY (Don’t Repeat Yourself) principle, which is just a fancy way to say it keeps your code organized and easy to maintain.

Is Django enough for the backend? 

The simple answer is yes. Right out of the box, Django gives you everything from a built-in admin panel, an authentication system, and an object-relational mapping system(ORM) for database work, so you don’t need to look for separate libraries constantly. 

With just Django, I can build RESTful APIs, integrate with other services, and even scale to large enterprise solutions.

Combined with an AI companion like Pieces which streamlines developer workflow and boosts productivity, you can turn Django into the best python framework for backend.

from django.shortcuts import render, redirect
from django.http import HttpResponse
from .forms import EventForm

def create_event(request):
    if request.method == 'POST':
        form = EventForm(request.POST)
        if form.is_valid():
            event = form.save()
            # Here we do something trivial, but you might want to
            # redirect or display a success message in a more elegant way
            return HttpResponse(f"Event '{event.name}' created successfully!")
        else:
            # Pieces can help improve error handling or messaging
            return render(request, 'events/create_event.html', {'form': form})
    else:
        form = EventForm()
    return render(request, 'events/create_event.html', {'form': form})

If we want to refactor this code and convert the function-based view to a class-based view, you can simply highlight the code directly in your IDE, and then save the highlighted selection to Pieces.

Once that’s saved, you can open the Pieces app, select the saved material, and then tell the prompt to pieces to do what you want, which in this case is to convert the function-based view to a class-based view.

In sum: Django is great for building small prototypes to enterprise-scale apps right out of the box. It lets us use an organized approach that accelerates development and it also has a large community that can help you when stuck on something. However, Django’s design and numerous components can be overkill for simpler websites, and the learning curve is steeper than other Python backend frameworks.

Flask

If you’re looking for a lightweight Python backend framework with just the essentials, Flask is the answer. 

Flask is a minimalistic framework that has none of the ORM, admin panel, or built-in authentication that Django has, but instead leaves us free to assemble our own toolkit. Because it’s so “micro,” we get to choose exactly which extensions we need, which is great when building a lean, uncluttered project.

Regardless of its minimalistic nature, Flask is still one of the best backend python framework and can handle everything we need for web applications. Its flexibility lets you integrate whatever library best fits your situation – whether it’s a database connector or a user authentication module.

In sum: Flask is a lightweight Python backend framework that makes it easy to get started quickly and scale your application as it grows. Its minimal design is perfect for small projects or situations where you want to keep things simple and flexible. However, because Flask doesn’t include many built-in tools or a strict structure, it can slow you down when trying to launch a minimum viable product (MVP), and managing a larger codebase can get tricky without careful organization.

Cherrypy

Next up on our python backend framework list is CherryPy. Like Flask, CherryPy is a minimalist python framework, but it still follows a a very “Pythonic” design philosophy that emphasizes readability and simplicity. 

Despite its simplicity, CherryPy still packs a few helpful features – like an HTTP server and native testing support – that make it a powerful backend option. Since it’s MVC and OOP-based, structuring our code and implementing CRUD functionality is as easy as it can be.

In sum: CherryPy’s modular design and simplicity make it easy to build and maintain smaller applications or prototypes while still allowing for extension and modification as your needs evolve. However, CherryPy’s greatest drawback is its lack of comprehensive documentation. This can make the learning curve steeper, especially for beginners or anyone relying heavily on step-by-step guides.

FastAPI

Last but by no means least is FastAPI. Thanks to Starlette and Pydantic under the hood, FastAPI achieves performance levels similar to Node.js and Go – making it one of the fastest Python backend api frameworks around.

 It’s designed around asynchronous programming, which is just a fancy way of saying you can handle tons of requests at once without slowing down, effectively boosting your development speed by 200–300%.

Even though it’s relatively new compared to heavyweights like Django or Flask, FastAPI is catching on fast (pun intended) among developers who value speed and simplicity. It’s especially great for high-performance services or microservices where every millisecond counts. If you’re looking for an all-in-one framework with a built-in admin panel, you might prefer something like Django. But if you want to build super fast, scalable APIs without a lot of fuss, FastAPI is hard to beat.

In sum: FastAPI stands out for its remarkable speed and modern Python features, built on Starlette and Pydantic. With FastAPI, you get lightning-fast performance, automatic interactive docs, and async/await support that’s perfect for handling high-traffic apps. However, it’s still relatively new, meaning a smaller ecosystem and a learning curve for those unfamiliar with async code. 

What Python framework is best after all?

At the end of the day, we realized that choosing the right Python backend framework is all about the size of my project, how complex it needed to be, and our own personal coding style. All the above Python backend frameworks are great, but they shine in different areas:

• We found Django to be fantastic when we wanted a fully loaded toolkit from the start.

• Flask and CherryPy, on the other hand, let us piece together exactly what we need, keeping our app lean and flexible. 

• And FastAPI? Well, it was hard to beat when we decided to prioritize speed and performance, especially for APIs.

| Framework  | Built-in Features                                            | Performance                                      | Community                                      |
|------------|-------------------------------------------------------------|-------------------------------------------------|------------------------------------------------|
| Django     | Includes ORM, admin panel, authentication, form handling, etc. | High performance overall; largely synchronous  | Large, well-established; extensive docs and packages |
| Flask      | Barebones by default; relies on extensions for ORM, authentication, etc. | Generally good; synchronous unless you add async | Huge user base; many community-driven extensions |
| CherryPy   | Built-in server, basic MVC structure, native testing support, CRUD tools | Suitable for most typical use cases; synchronous by default | Smaller but dedicated community; less mainstream packages |
| FastAPI    | Automatic interactive docs (OpenAPI/Swagger), Pydantic validation | Very high performance via async Starlette & Pydantic | Growing community; rapidly expanding ecosystem

No matter which framework we went with, pairing it with a powerful AI assistant with long-term memory makes a world of difference – letting us code faster, fix errors on the fly, and focus on building something awesome.

Conclusion

Python is great for backend development. Python offers a wide range of frameworks—like Django, Flask, CherryPy, and FastAPI – that make it easy to build everything from small prototypes to large, high-traffic applications. 

However, there are other options for backend development, like Nodejs, Go, Java, and Ruby. If you only know Python and need to switch to any of these languages, you can use simply use transform snippets to translate your Python code into any language you want, directly from your IDE. 

To do that, you first have to download and set up the Pieces Desktop app on your local machine.

Next, download the plugin for any IDE you want, and once that’s done, you’re ready to get started. 

If we want to convert some Python code to Nodejs in VScode for instance, you can either save the whole file to Pieces or save the specific snippet you want to convert. 

Once that’s done, you can switch to the Pieces for Developers desktop app, select the saved file, and tell it what you want. 

In this case, we want to convert the app to Nodejs, so we can simply say “convert the code to Nodejs”.

Like that, we can get ready to use code in any language we want! 

So, if your organization switches the backend from Python to any other language, you can save a lot of time by using Pieces to streamline the process.

Go and read the docs on your own if this one didn’t get you persuaded!

This article was first published on November 16th, 2022, and was improved by Fimber Elemuva Isenah as of February 14th, 2025, to improve your experience and share the latest information.