I used all three in real projects for three months. Here is what I found.
Most people talk about Python frameworks based on docs they read or small projects they built on weekends. I am not doing that. I ran all three in actual production. Real traffic. Real bugs. Real 2 AM problems.
This article will tell you which one I would pick again and which ones I would skip.
Friend link for nonmembers- https://medium.com/@inprogrammer/fastapi-vs-litestar-vs-django-ninja-one-won-clearly-83fb48f9094b?sk=8ed954c85cee40343206f8a75d7890f4
What I Built
I made three small services for a software company:
- A data service that got 50,000 requests every day (FastAPI)
- A user API that had a lot of login and permission rules (Django Ninja)
- A notification service that sent real-time alerts (Litestar)
All three used the same database, the same server, and the same team. So the only thing that changed between them was the framework. That made it easy to compare.
FastAPI: Everyone Uses It for a Reason
FastAPI is what most Python developers pick first. And honestly, it makes sense.
You can write your first API endpoint in ten minutes. You add a type hint to your function, and FastAPI figures out the rest. It checks the data, builds the docs, and runs the server.
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
@app.post("/items/")
async def create_item(item: Item):
return {"name": item.name, "price": item.price}Short. Clean. Easy to read.
What FastAPI does well
The biggest advantage of FastAPI is its strong community. If you face a problem, there is a high chance someone has already solved it on Stack Overflow. Most libraries also have FastAPI examples, and the documentation is clear and helpful.
Adding new features is fast. New developers can quickly get started since many are already familiar with FastAPI. In my case, the team was ready to work on a data service within one day.
Where FastAPI gave me trouble
After two months, FastAPI projects can become messy. It does not enforce a clear project structure, so teams often follow different patterns. This leads to confusion and wasted time in code reviews instead of focusing on real improvements.
We also faced Pydantic version conflicts. Mixing old and new versions caused unexpected errors that took days to debug.
FastAPI handles async well for moderate traffic, but at around 50K requests per day, performance limits start to show. Scaling further needs careful optimization.
FastAPI in short
Good for small teams that want to ship fast. Not the best pick if you need to handle a lot of traffic or keep the code clean as the team grows.
Django Ninja: Good if You Already Use Django
If your project already runs on Django, Django Ninja will feel very natural. It adds type hints and automatic docs to Django without breaking anything you already built.
from ninja import NinjaAPI, Schema
api = NinjaAPI()
class UserSchema(Schema):
username: str
email: str
@api.post("/users/")
def create_user(request, payload: UserSchema):
user = User.objects.create(**payload.dict())
return {"id": user.id}Your Django models stay. Your Django admin stays. Your login system stays. You just get a cleaner way to build your API on top.
What Django Ninja does well
Adding it to an existing Django project is very easy. You change two lines in your urls.py file and you are done. Nothing breaks.
For my user service, this saved a lot of work. Django already handled all the user roles and permissions. I did not need to rebuild any of that. Django Ninja just let me expose it as an API.
Writing tests was also easy because we used the same Django test patterns the team already knew.
Where Django Ninja gave me trouble
Django Ninja works best if you are already using Django. Starting a new project with Django just for Ninja is overkill. Django is a heavy framework, not ideal for small APIs.
Async support is limited. Django was not built for async, and database operations can become a bottleneck. It works fine for low and steady traffic, but high-performance use cases can face issues.
There are also limits in flexibility. As your API grows and you need more control over data handling, Django Ninja can start to feel restrictive.
Django Ninja in short
Great if you already have a Django project. Do not start a new project with it unless you really need Django.
Litestar: The One Most People Ignore
Litestar (it used to be called Starlite) was not my first pick. I chose it for the notification service because I saw some numbers showing it was fast with async. That was it.
Three months later, it is the first framework I think of for new projects.
from litestar import Litestar, post
from litestar.dto import DTOData
from dataclasses import dataclass
@dataclass
class Notification:
user_id: int
message: str
@post("/notify/")
async def send_notification(data: DTOData[Notification]) -> dict:
payload = data.create_instance()
await push_to_queue(payload)
return {"status": "queued"}
app = Litestar(route_handlers=[send_notification])What Litestar does well
Litestar enforces a clear project structure, which keeps teams consistent and avoids confusion in code organization. This saves time and reduces unnecessary code review discussions.
Its async performance is strong. It handled traffic spikes of thousands of requests smoothly, even under heavy load where FastAPI struggled in comparison.
Litestar also includes built-in features like rate limiting, caching, and monitoring. Unlike FastAPI, you do not need extra packages for these, and everything works together out of the box.
It also supports DTOs (Data Transfer Objects), which separate internal logic from API responses. This makes the code cleaner and easier to scale as the project grows.
Where Litestar gave me trouble
Litestar has a steeper learning curve. You can get comfortable with FastAPI in a day, but Litestar can take about a week. Concepts like DTOs, guards, and layers need time to understand.
The community is smaller, so finding solutions online is harder. You may need to check GitHub or read the source code.
The documentation is improving, but some parts still have gaps, which can slow you down.
Litestar in short
Best choice if you care about performance, clean code, and a codebase that stays easy to work with as the team grows. You need to invest time to learn it, but it pays off.
Quick Comparison
What matters FastAPI Django Ninja Litestar
─────────────────────────────────────────────────────────────────────────────
How easy to learn Easy Easy (if you know Django) Medium
Async speed Good Slow Excellent
Code structure No rules Django rules Clear rules built in
Community Very big Medium Small but helpful
Best situation New projects, Existing Django apps High traffic,
fast start long projects
Extra tools Some Many (via Django) Many (native)So Which One Won?
Litestar. But it depends on your situation.
If I am starting a new API that needs to scale over time, I pick Litestar. It keeps code clean, handles high traffic well, and separates internal logic from API responses.
If the project already runs on Django, Django Ninja is the obvious choice. It fits in easily and saves time.
If speed matters and the team already knows FastAPI, go with FastAPI. It is fast to build and still very reliable.
The bottom line is simple. There is no one best framework. But for long-term performance, clean structure, and scalability, Litestar stands out the most.
What You Should Do Next
Try Litestar by building a small API this week. The quick start takes about 20 minutes and shows how structured it feels.
If FastAPI is working for you, do not switch. No need to fix what is not broken.
If you use Django, add Django Ninja. It takes about an hour and works immediately.
Choose the framework that fits your project, not the one that is trending. That is what really matters.