What’s The Best Backend Framework for AI Development in 2024? Django, FastAPI or Flask? 🤔

I knew choosing the right framework was crucial for developing our AI-Agent, it impacts the project’s performance, scalability, and ease of development.

So I did a bit of research to compare the three most popular Python frameworks in 2024; Django, FastAPI and Flask. I wanted to find out their advantages and disadvantages before choosing one.

I’ve summarised my findings in this article along with a quick practical example guide to implement each of them.

If you’re planning on building a complex application, a high-performance API, or a simple, flexible AI project, I hope this breakdown to understand the strengths and limitations of each framework helps you make an informed decision. I’ve shared my final thoughts on the best uses of each in the conclusion section below.

Happy reading!

Introduction to Backend Frameworks

So if you’re here you probably know that backend frameworks basically provide the tools and libraries we need to build robust and scalable web applications. But for AI Development in particular (our case and that of most people building in 2024), the chosen framework has to be able to efficiently handle requests, integrate with AI services, and manage data. So let’s look at the specifics of Django, FastAPI, and Flask to understand their capabilities and limitations.

Django

Django

https://github.com/django/django

Overview

Django is an open source high-level Python web framework that promotes rapid development and clean, pragmatic design. It is known for its “batteries-included” approach, offering a wide range of built-in features such as authentication, ORM, and admin interface.

Pros and Cons

Pros:

• Comprehensive Features: Comes with many built-in functionalities.
• Security: Helps avoid common security pitfalls.
• Community Support: Large and active community.
• Scalability: Suitable for scaling up complex applications.

Cons:

• Learning Curve: Can be overwhelming for beginners.
• Monolithic Structure: Less flexibility compared to microframeworks.

Example: How To Set Up a Basic Django Instance

We will install a very basic instance of django, for more in detail follow our Django for AI series

Installation

With python installed in your computer open a new terminal/console and type the following:

pip install django

Creating a Django Project

After django has been installed start the project

django-admin startproject myproject
cd myproject
python manage.py startapp myapp

Configuring and Running the Server

In myproject/settings.py, add myapp to INSTALLED_APPS:

INSTALLED_APPS = [
    ...
    'myapp',
]

Run the development server:

python manage.py runserver

Creating a Simple View

In myapp/views.py:

from django.http import HttpResponse

def index(request):
    return HttpResponse("Hello, world!")

In myapp/urls.py:

from django.urls import path
from .views import index

urlpatterns = [
    path('', index),
]

In myproject/urls.py, include the app’s URLs:

from django.urls import include, path

urlpatterns = [
    path('myapp/', include('myapp.urls')),
]

FastAPI

https://fastapi.tiangolo.com/

Overview

FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.6+ based on standard Python type hints. It is designed to be easy to use and to make the most out of Python’s type system.

Pros and Cons

Pros:

• Performance: One of the fastest Python frameworks available.
• Ease of Use: Intuitive and concise code.
• Type Safety: Leverages Python type hints for validation and serialization.
• Asynchronous Support: Built-in support for async programming.

Cons:

• Smaller Community: Relatively new with a smaller community.
• Limited Built-in Features: Lacks the extensive built-in features of Django.

Example: How To Set Up a Basic FastAPI Instance

Installation

With python installed in your computer open a new terminal/console and type the following:

pip install fastapi uvicorn

Creating a FastAPI App

In main.py:

from fastapi import FastAPI

app = FastAPI()
@app.get("/")
def read_root():
    return {"Hello": "World"}

Running the Server

In a terminal just run uvicorn to see your main app in action.

uvicorn main:app --reload

Flask

https://flask.palletsprojects.com/en/3.0.x/

Overview

Flask is a microframework for Python based on Werkzeug and Jinja2. It is designed with simplicity and flexibility in mind, allowing developers to choose their components.

Pros and Cons

Pros:

• Lightweight and Flexible: Minimalistic approach, easy to extend.
• Ease of Learning: Simple and straightforward for beginners.
• Large Ecosystem: Many extensions available to add functionality.

Cons:

• Manual Configuration: More setup required for complex applications.
• Less Built-in Functionality: Requires additional libraries for full-fledged applications.

Example: How To Set Up a Basic Flask Instance

Installation

With python installed in your computer open a new terminal/console and type the following:

pip install flask

Creating a Flask App

In app.py:

from flask import Flask

app = Flask(__name__)
@app.route("/")
def hello_world():
    return "Hello, World!"

Running the Server

Running the server in Flask is even easier, open a terminal and type:

flask run

So, what’s the best backend framework to choose in 2024? Django, FastAPI or Flask? 👇

I go into each of the topics below, but for me I need a table to compare them visually, so I’ve added this view, if you prefer to read criteria by criteria, continue below.

Performance

• FastAPI: Known for its high performance, thanks to asynchronous support.
• Flask: Generally performs well, but may require additional tweaks for high performance.
• Django: Performance is good, but can be slower due to its comprehensive features.

Ease of Use

• Flask: Easiest to learn and use, with a simple and minimalistic design.
• FastAPI: User-friendly with concise code and type safety, but newer to the scene.
• Django: Steeper learning curve due to its extensive features and structure.

Community and Ecosystem

• Django: Largest community and extensive ecosystem.
• Flask: Mature and large community with many available extensions.
• FastAPI: Growing rapidly, but smaller compared to Django and Flask.

AI Integration

• Django: Suitable for large-scale AI projects with robust features and scalability.
• FastAPI: Excellent choice for building performant APIs for AI services.
• Flask: Good for small to medium AI projects, especially if simplicity and flexibility are desired.

Conclusion

I’m a big fan of Django, yes it’s a bit of a pain to learn at first but imo, the value add of the security features, the speed of development and the rest of pro’s outway it’s cons. I do think that choosing the best backend framework for AI development depends on your specific needs and project requirements, in summary:

• Django: Is best for large-scale applications that require robust features and security.
• FastAPI: Is ideal for high-performance applications with modern async capabilities.
• Flask: Is perfect for simple, flexible applications where you can add components as needed easily.

I would always choose Django even though its learning curve is harder, that learning pays back in security, scalability and maintainability. If you want just to play a little bit around Flask is a very good candidate.

By the way, I’m David 👋 I’m the CTO of Cubode, I’m writing this at 9pm, with big headphones on and wondering whether I should actually post this? I’m not used to sharing my work, so if you find any sort of value from this post, I’d love to hear about you and how it helped you!

This is how Dall-E pictured me, it’s pretty accurate, me in my development cave.

If you’re curious about how we’re building an open source AI Agent in 30 days at Cubode, we’re documenting the whole thing with step by step guides, this was article 6 of 30.

Stay curious, see you in the next one!