Best Python Backend Frameworks in 2026

Most new Python APIs now start on FastAPI, while Django still runs a large share of the data-heavy apps that need an admin and an ORM, and Flask remains the small core people reach for when they want to choose every library themselves. The framework decides how requests are routed and validated, but none of these frameworks provision anything, so once the app needs a Postgres database, a queue and a bucket the team ends up maintaining Docker Compose for local development and Terraform for production next to the application code.

What to weigh when choosing:

• WSGI frameworks such as Flask and classic Django handle concurrency differently from ASGI frameworks such as FastAPI, Starlette and Litestar.
• Django ships an ORM, admin and auth, while FastAPI and Flask leave those choices to you.
• Type-hint-driven frameworks generate OpenAPI schemas and validation from your code.
• None of the frameworks provision databases, queues or cloud resources, so that work lands somewhere else.

Python backend frameworks at a glance

Encore: infrastructure for whichever framework you choose

Encore combines the Python web framework you already use with infrastructure declared in the same code, as an SDK you install into the existing app, so routing, validation and request handling stay in the framework while Postgres databases, Pub/Sub topics, object storage buckets, Redis, cron jobs and secrets are module-level declarations that Encore finds and provisions. It works with any Python framework, encore run starts the databases, topics and buckets locally next to the app, and a deploy provisions the same declarations in your own AWS or GCP account.

The SDK is the same whichever framework you choose, so the comparison below still decides how your requests are handled. For code that attaches it to FastAPI, Django and Flask, see FastAPI vs Django vs Flask, and for a full deploy and a multi-service layout, see deploying FastAPI to AWS and Python microservices. An AI coding agent working in any of these codebases can declare a topic or bucket in the change that needs it and try it locally right away.

Python support is in beta, and you can sign up for access.

Try Encore →

FastAPI: type hints as the API contract

FastAPI

FastAPI builds on Starlette and Pydantic, and function signatures declare the path, query and body parameters that FastAPI validates and turns into an OpenAPI schema.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Signup(BaseModel):
    email: str


@app.post("/users", status_code=201)
async def create_user(req: Signup) -> dict:
    return {"email": req.email}

Key features

• Request validation and serialization come from Pydantic models and type hints.
• The OpenAPI schema is generated, with Swagger UI and ReDoc included.
• Depends handles dependency injection for auth, database sessions and shared logic.
• Async and sync handlers can live in the same app.

Good to know

FastAPI is an API framework rather than an application framework, so the ORM, migrations tool, auth, background jobs and project layout are your choice, and larger codebases need conventions that the framework does not enforce. BackgroundTasks runs in the same process as the request, which means long-running work that must survive a restart belongs in a queue.

Try FastAPI →

Django: batteries included

Django

Django ships an ORM, migrations, an admin interface, authentication, forms and templating, and most API work adds Django REST framework or django-ninja on top.

# models.py
from django.db import models


class User(models.Model):
    email = models.EmailField(unique=True)
    created_at = models.DateTimeField(auto_now_add=True)

Key features

• Schema migrations are generated from models.
• The admin UI is generated from the same models.
• Users, sessions, permissions and CSRF protection are built in.
• A large ecosystem of reusable apps covers common needs.

Good to know

Django is the fastest path when the app is mostly CRUD over a relational schema and someone needs an admin. Async views and parts of the async ORM exist, although much of the ecosystem is still sync-first, and the conventions that help large teams get in the way when you only need a few JSON endpoints.

Try Django →

Flask: a small core you extend

Flask

Flask gives you routing, request and response objects and templating, and everything else comes from extensions.

from flask import Flask, request

app = Flask(__name__)


@app.post("/users")
def create_user():
    data = request.get_json()
    return {"email": data["email"]}, 201

Key features

• The core is small enough to read end to end.
• Blueprints split an app into modules.
• A mature extension ecosystem includes Flask-SQLAlchemy and Flask-Login.

Good to know

Flask fits small services, internal tools and teams that want to pick every library, but it is WSGI and synchronous by default with no built-in validation, and the structure of the project is up to you, which is why most new async JSON APIs start on FastAPI instead.

Try Flask →

Litestar: typed async APIs with more built in

Litestar

Litestar is an ASGI framework with typed route handlers, dependency injection and generated OpenAPI docs, and it includes more than FastAPI out of the box, such as a SQLAlchemy plugin and DTOs for shaping data.

from litestar import Litestar, post
from msgspec import Struct


class Signup(Struct):
    email: str


@post("/users")
async def create_user(data: Signup) -> dict:
    return {"email": data.email}


app = Litestar(route_handlers=[create_user])

Key features

• Handlers are typed with msgspec, Pydantic, dataclasses or attrs.
• Dependency injection, guards and middleware are layered.
• A SQLAlchemy plugin and DTOs are included.

Good to know

Litestar suits teams that like the FastAPI style and want more structure built in, although its community and third-party ecosystem are smaller, so there are fewer tutorials, integrations and published examples than for FastAPI.

Try Litestar →

Starlette: the minimal ASGI toolkit

Starlette

Starlette is the ASGI toolkit that FastAPI is built on, with routing, middleware, WebSockets and background tasks but no validation layer.

from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route


async def health(request):
    return JSONResponse({"ok": True})


app = Starlette(routes=[Route("/health", health)])

Key features

• The ASGI core is small and supports WebSockets.
• Middleware, routing and a test client are included.

Good to know

Starlette works well for small services, proxies and custom framework layers, and anything that takes request bodies ends up with validation code that FastAPI would have provided.

Try Starlette →

How to choose

• Encore added to whichever framework you pick, such as FastAPI, Django or Flask, when you want the database, Pub/Sub, buckets and cron jobs declared in Python, with the database, topics and buckets running locally and everything provisioned in your AWS or GCP account.
• FastAPI for new JSON APIs, ML inference services and teams comfortable choosing their own ORM and auth.
• Django for relational, CRUD-heavy apps that benefit from an ORM, admin and auth.
• Flask for small services and teams that want a minimal core.
• Litestar for typed async APIs that want more built in than FastAPI.
• Starlette for minimal ASGI services.

Whichever framework you pick, the Postgres database, queue, bucket and cron schedule still have to exist locally, in preview environments and in production. Python support in Encore is in beta, and you can sign up for access if you want to declare that infrastructure in the framework app you already have.

Related guides

• FastAPI vs Django vs Flask
• Celery alternatives for Python background jobs
• Deploy a PyTorch API with FastAPI and Encore
• Message queues vs Pub/Sub