Python API Development in 2025: FastAPI vs Flask vs Django REST Framework

Key Takeaways

• FastAPI: Great when speed matters; it uses modern Python async/await for top performance and automatically provides beautiful API documentation.
• Flask: Offers complete freedom as a minimalist framework, making it the easiest to learn for prototypes and custom solutions.
• Django REST Framework: The enterprise choice due to its feature-packed, standardized approach (security, ORM, admin), best suited for complex, large-scale systems.
• Deployment: All three work well with containers and VPS platforms like DigitalOcean, Linode, or SSD Nodes. FastAPI and Flask are lighter and cheaper to run, while DRF requires more resources but handles complex business logic better.

FastAPI, Flask, and Django REST Framework (DRF) are three of the most widely used Python frameworks for building APIs. Each of them has something in their design philosophy and capabilities that make them unique.

The decision to adopt one can directly influence performance, development speed, and long-term maintainability.

FastAPI focuses on high performance and type safety through async support and automatic schema generation.

Flask has a minimal core that lets you define the architecture entirely on your terms. For large applications, the Django REST Framework offers a wide range of features and is closely linked with Django’s admin and ORM. The structure of your application, ecosystem maturity, and performance requirements must all be carefully considered when choosing between them in 2025.

This guide compares these frameworks in depth by covering their strengths, limitations, and a little of the technical aspects.

Note: If you are looking for a full comprehensive comparison for Flask vs. Django for general web development, and not just APIs, check out this article: Django or Flask?

Framework Comparison Table

This section will provide a table to show you how each Python API development tool compares with each other. Before you decide on which API development tool to pick, you should go through this table to see which of them works well for your use case and project expectations.

Criteria	FastAPI	Flask	Django REST Framework (DRF)
Learning Curve	Medium. Requires comfortable Python knowledge and an understanding of asynchronous programming (necessary to unlock full potential).	Easy. If you know Python, the transition is straightforward; it’s the simplest to start with.	Hard. Built atop Django, requiring mastery of Django’s conventions and ORM before tackling DRF’s specific abstractions.
Open Source	Yes	Yes	Yes
Ease of Deployment	Easy. Standard Python API deployment. Simple to set up with an ASGI server (e.g., Uvicorn).	Easy. Standard Python API deployment with a WSGI server (e.g., Gunicorn).	Moderate. Due to Django’s extensive feature set, the deployment configuration is more complex and involves many integrated components.
Community & Ecosystem	Fast-Growing. The community is vibrant and rapidly expanding, though currently the smallest of the three.	Mature. Has a huge, established ecosystem and a rich history of extensions.	Mature. Inherits Django’s vast ecosystem; is the dominant choice for Django API development and widely used in enterprise.
Security	Better. Built-in support for security standards (OAuth2, JWT, CORS, rate limiting) often requires proper setup, but the tools are readily available.	Needs Extensions. Requires significant manual setup and third-party extensions to achieve a strong, deployable security standard.	Best. Inherits Django’s strict, opinionated security protocols, providing robust built-in features like authentication, permissions, and CSRF protection.
Performance	Excellent. Asynchronous features built-in via Starlette and Pydantic make it extremely fast.	Good (Synchronous). Synchronous by default, though modern versions support async/await (requires proper server configuration).	Good (Synchronous). Slower than FastAPI due to Django’s overhead, but performance is sufficient for the vast majority of business applications.
Flexibility	Very High. Works seamlessly with any ORM, authentication method, or middleware stack. Fully supports both async and sync code.	High. Micro-framework design allows you to plug in anything and build a stack exactly to your specifications.	Low. Built to adhere to strict Django principles. It is rigid and opinionated, making custom architectural changes difficult or near impossible.
Automatic Documentation	Yes (Swagger/OpenAPI & ReDoc by default).	No (requires manual setup and extensions).	No (requires popular community packages like drf-yasg or drf-spectacular).
Database Integration	No built-in ORM, but works well with modern async ORMs like SQLModel, Tortoise ORM, and non-relational databases.	No built-in ORM, but integrates well with established packages like SQLAlchemy, Pony ORM, and non-relational databases.	In-built ORM integration with the powerful Django ORM, which is tightly coupled with the framework.

Advantages of Each Framework

You should know the advantages of each framework so you can choose the one that best matches your project’s use case or team’s expertise. You will learn the strengths of each framework, what to look out for in this part of the article.

FastAPI

You should choose FastAPI if your primary interest is in building a fast API, as the name suggests.

FastAPI is also built for type safety and rapid development. Its asynchronous nature is built on Starlette, which allows handling a high number of concurrent requests with the least latency. This reduced latency makes it suitable for microservices or applications that frequently interact with external APIs.

Another big advantage of using FastAPI is automatic request validation (with Pydantic) and documentation through OpenAPI. FastAPI automatically generates interactive API documentation based on the Python code and type annotations.

FastAPI is minimalistic at its core as it allows the flexibility to integrate any ORM or authentication library, and gives you control over each component without locking you into a specific stack.

Flask

Flask gives you maximum control over the API structure without enforcing a specific architecture compared to the other two options. Because it is a microframework you can start with a minimal codebase and progressively integrate extensions for other components as needed.

There is no standard structure, and you have room to do whatever you want, which comes with its own risks. Flask makes the most sense if your goal is to build prototypes that need to grow into production systems with custom requirements.

Flask also has an advantage in maturity and a wide extension ecosystem.

Django Rest Framework

You should choose Django REST Framework for projects that require a structured environment and the most features out of the box. It uses Django’s ORM, middleware, and admin interface to enable you to build a good solution for CRUD-heavy applications where business logic revolves around relational data.

While DRF does not offer automatic type-driven request validation like FastAPI, it provides serializers for defining and validating data structures, along with viewsets for organizing CRUD logic. This is what DRF takes advantage of to support fast development of consistent and structured APIs.

DRF has a tight integration with Django’s security and authentication system, which provides a secure foundation for enterprise-level applications.

DRF’s strictness is an advantage because it enforces a consistent structure for your API endpoints, data validation, and authentication, reducing ambiguity and accidental errors as a project grows. Most APIs built with DRF look the same, which is a big advantage in onboarding new engineers to the team.

Challenges of each framework

There are some challenges you will come across while using these tools for API development. It’s important to know some of these challenges to choose the best tool for you and your team. In this section you will see the practical challenges you may face when working with each tool so you can plan accordingly.

FastAPI

To make your API’s performance fast with FastAPI, you would rely on asynchronous programming, which can add complexity for teams unfamiliar with async/await patterns. You would also encounter this issue when integrating with synchronous libraries or ORMs.

There are fewer mature extensions and a smaller pool of developers with experience in production-scale FastAPI projects. The ecosystem is still in its infant stage so this is to be expected.

Flask

With Flask you can design your API without a strict structure, which comes at the cost of standardization. Because it does not have a standard project structure, large teams can end up with different coding patterns, which can make maintenance difficult over time.

Some common API requirements, such as authentication, authorization, documentation, etc. require additional packages. This can lead to too many external libraries and version conflicts in the long run.

Flask is also synchronous by default, so you can not achieve FastAPI-level concurrency without restructuring code or using less familiar async extensions.

Django REST Framework

DRF’s strict conventions can slow down projects that need unconventional API designs or non-ORM data sources, because working against its defaults can feel cumbersome.

There is a dependency bloat for projects that do not require a full web framework, which affects performance compared to lighter options.

Another challenge you should consider is that DRF takes longer to learn because you must understand Django itself before mastering DRF. This can delay onboarding for developers coming from non-Django backgrounds.

Authentication Patterns

Authentication is one of the first decisions you make when you want to build anything serious and each framework has its own approach to implementing it. Even though the aim of authentication is to identify and verify users, the way you go about it in each platform is different.

In this section we will go through a comparison and see basic examples of how authentication is handled in each framework.

FastAPI

If you decide to go with FastAPI, know that you have authentication helpers but you would have to make the choice of which storage, token management, and user models you would use. It has security utilities in fastapi.security that integrate directly with FastAPI’s dependency injection.

Example code Here HTTP Basic Auth is handled by extracting credentials from the request and comparing them against predefined values.

from fastapi import FastAPI, Depends, HTTPException

from fastapi.security import HTTPBasic, HTTPBasicCredentials

app = FastAPI()

security = HTTPBasic()

@app.get("/secure")

def secure(credentials: HTTPBasicCredentials = Depends(security)):

if credentials.username != "username" or credentials.password != "a secret password":

raise HTTPException(status_code=401, detail="Invalid credentials")

return {"message": f"Hello, {credentials.username}"}

The program above reads the credentials from the request’s authorization details and decodes them. The credentials object is then made available inside the protected route so the username and password can be checked without handling the raw request headers. An error response with a 401 status is returned if the values do not match what is expected.

Flask

Here, there is no built-in authentication, but Flask supports a number of extensions you could use. There are two routes you can go with authentication which are session and token based authentications.

Since APIs are not browser based for the most part, you will authenticate with a token. You can easily set that up with an extension or custom middleware.

With Flask you have an open slate to build anything from minimal single sign-on to full custom security layers.

But note that inconsistent patterns can emerge across services if the authentication strategy is not standardized early.

Here, Flask-HTTPAuth reads credentials from the request headers and validates them:

from flask import Flask

from flask_httpauth import HTTPBasicAuth

app = Flask(__name__)

auth = HTTPBasicAuth()

@auth.verify_password

def verify(username, password):

return username == "username" and password == "asecretpassword"

@app.route("/secure")

@auth.login_required

def secure():

return {"message": f"Hello, {auth.current_user()}"}

if name == "__main__":

app.run()

The program uses a method to check the provided username and password against expected values. If the credentials are correct, access will be granted. If not, the request is rejected. We also set up a protected route so that only requests with valid authentication details are allowed to proceed.

Django REST Framework

DRF mainly focuses on standardization. It supports multiple authentication classes out of the box. You can set up Basic Auth by enabling it in settings and using the IsAuthenticated permission class, which is enough for handling validation in DRF.

DRF offers a built-in authentication feature and is built on top of Django’s user system which means you would require minimal code for standard authentication patterns.

# settings.py

REST_FRAMEWORK = {

"DEFAULT_AUTHENTICATION_CLASSES": [

"rest_framework.authentication.BasicAuthentication",

],

"DEFAULT_PERMISSION_CLASSES": [

"rest_framework.permissions.IsAuthenticated",

]

}

# views.py

from rest_framework.views import APIView

from rest_framework.response import Response

class SecureView(APIView):

def get(self, request):

return Response({"message": f"Welcome, {request.user}"})

The program above enables Basic Authentication for all requests by applying the settings globally in the settings.py file. It automatically checks the provided credentials against stored user records. Finally, a 401 error response is returned without adding any extra logic to the endpoint.

Database Integration

Databases are another important aspect of building APIs with Python frameworks. One has built-in support for the database and the other two do not. From my experience, most people building with these frameworks use ORMs, as they are simpler to work with.

We’ll compare how each framework handles database connections, and you will see some simple examples.

FastAPI

Before you pick a tool you have to decide if you want a relational or non-relational database. For a relational database you would need an ORM. You can choose either SQLAlchemy, or other libraries depending on your needs.

The most common choice for FastAPI developers is SQLAlchemy. This means you have control over architecture so you would need to set up all the components yourself.

The Python library for the database platform will work here perfectly if you want to use a NoSQL database.

Example with SQLAlchemy

# app.py

from fastapi import FastAPI

from sqlalchemy.ext.declarative import declarative_base

from sqlalchemy.orm import sessionmaker

from sqlalchemy import create_engine, Column, Integer, String




app = FastAPI()

DATABASE_URL = "sqlite:///./userdatabase.db"

engine = create_engine(DATABASE_URL)

SessionLocal = sessionmaker(bind=engine)

Base = declarative_base()

class User(Base):

tablename = "users"

id = Column(Integer, primary_key=True)

name = Column(String)

Base.metadata.create_all(engine)

@app.get("/users")

def get_users():

db = SessionLocal()

users = db.query(User).all()

return [{"id": u.id, "name": u.name} for u in users]

Here, you explicitly manage the ORM setup and sessions which make it more customizable.

Flask

Just like FastAPI, there is no ORM built into the framework. Using SQLAlchemy through the Flask-SQLAlchemy library is your best option when setting up your database. This library makes it easy for Flask to communicate with the Database.

The implementation of NoSQL databases in Flask follows the same approach as in FastAPI. The Python library provided by the Database serves as the necessary component.

Example:

# app.py

from flask import Flask, jsonify

from flask_sqlalchemy import SQLAlchemy

app = Flask(__name__)

app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///test.db"

db = SQLAlchemy(app)

class User(db.Model):

id = db.Column(db.Integer, primary_key=True)

name = db.Column(db.String)

db.create_all()

@app.route("/users")

def get_users():

users = User.query.all()

return jsonify([{"id": u.id, "name": u.name} for u in users])

if name == "__main__":

app.run()

Here, Flask-SQLAlchemy hides most of the manual setup seen in FastAPI, but you still have full SQLAlchemy access.

Django REST Framework

The ORM system of Django is a strong ORM and DRF implements it. The framework includes this ORM system which functions as part of its complete stack. The framework operates model definitions and migrations and query handling in the same way as Django so users do not need to install any additional libraries.

Example

# models.py

from django.db import models

class User(models.Model):

name = models.CharField(max_length=100)

# views.py

from rest_framework.response import Response

from rest_framework.decorators import api_view

from .models import User

@api_view(["GET"])

def get_users(request):

users = User.objects.all().values("id", "name")

return Response(list(users))

Django’s ORM removes the need for explicit connection setup in your view logic. Database configuration is handled in settings.py, and migrations are part of the core workflow.

Testing Strategies

API endpoint testing serves as a critical process to detect regressions while confirming business logic operations and verifying integration functionality. The three frameworks implement different testing methods yet they share the fundamental purpose of simulating requests to check responses through fake server operations.

The following section explains testing methods for each framework before presenting a basic example that demonstrates identical testing across all three environments.

Testing in FastAPI

FastAPI uses Starlette’s TestClient to make in-process HTTP calls against your app. You can test async endpoints synchronously, and the test client handles JSON encoding/decoding automatically. Since FastAPI is tightly coupled with Pydantic models, request and response validation are also tested implicitly.

This runs entirely in-memory so no need to spin up Uvicorn. The tests are fast because TestClient manages the lifecycle of the app for you.

Example

# test_fastapi.py

from fastapi import FastAPI

from fastapi.testclient import TestClient

app = FastAPI()

@app.get("/ping")

def ping():

return {"message": "pong"}

client = TestClient(app)

def test_ping():

response = client.get("/ping")

assert response.status_code == 200

assert response.json() == {"message": "pong"}

Testing in Flask

The Flask framework comes with its own test client which operates in a way that is similar to FastAPI’s. The main difference is that Flask does not perform automatic validation, so your tests focus purely on status codes, returned data, and any explicit validation logic you implemented.

# test_flask.py

from flask import Flask, jsonify

app = Flask(__name__)

@app.route("/ping")

def ping():

return jsonify({"message": "pong"})

client = app.test_client()

def test_ping():

response = client.get("/ping")

assert response.status_code == 200

assert response.get_json() == {"message": "pong"}

Like FastAPI’s TestClient, this runs without a live server. Flask’s test client integrates cleanly with pytest or unittest, making it straightforward to set up unit and integration tests.

Testing in DRF

The DRF framework extends Django’s standard testing framework through its APIClient which allows users to send JSON requests and verify responses. The DRF tests run through Django’s test runner which creates a fresh temporary test database to preserve database isolation. The tests run in an environment that matches production but their execution speed is slower than in-memory tests.

# test_drf.py

from django.urls import path

from rest_framework.test import APIClient

from rest_framework.response import Response

from rest_framework.decorators import api_view

@api_view(["GET"])

def ping(request):

return Response({"message": "pong"})

urlpatterns = [

path("ping/", ping),

]

client = APIClient()

def test_ping():

response = client.get("/ping/")

assert response.status_code == 200

assert response.json() == {"message": "pong"}

Because Django uses a full project structure this code would typically live inside an app’s tests.py file or a tests/ directory. The test client also mocks the batteries in Django so you can test complete request flows.

Local Development and Testing Workflows

When developing APIs locally, you often need to test webhook integrations from external services, share your API with frontend developers on different networks, or demo your API to clients before deployment.

This is where tunneling services become essential. Tools like ngrok or LocalXpose expose your local API to the internet temporarily. For example, with LocalXpose, if you start an API server on port 8000, you can expose it to the Internet like so:

loclx tunnel http --to localhost:8000

Your API will then be accessible at https://[subdomain].localxpose.io

This works the same way whether you’re using FastAPI, Flask, or Django REST Framework. The tunneling happens at the network level, so it doesn’t matter which framework you chose.

Logging

You need a way to track the activities going on in your Python API, this is why we have logging to help with monitoring and observability.

Python’s built-in logging module works across FastAPI, Flask, and Django REST Framework but each framework has its own integration points and best practices when it comes to setting up logs. This section compares how logging is approached in each and demonstrates a simple implementation that behaves the same way across all three.

Logging in FastAPI

FastAPI doesn’t enforce a logging configuration, so you typically work directly with Python’s logging module or configure it using Uvicorn’s logging settings when running the server. By default, Uvicorn provides basic request logs, but you can add custom loggers for application events.

FastAPI’s async nature means your logging calls should be non-blocking, but for simple use cases the standard synchronous logging module works fine.

Example of Logging in FastAPI

# app.py

import logging

from fastapi import FastAPI

logging.basicConfig(level=logging.INFO)

logger = logging.getLogger("fastapi_app")

app = FastAPI()

@app.get("/")

def read_root():

logger.info("Root endpoint accessed")

return {"message": "Logging in FastAPI"}

Logging in Flask

Flask also does not impose a logging configuration, but it integrates well with Python’s logging module. You can log directly through the app’s logger (app.logger) or configure a custom logger. Flask’s built-in logger inherits from Werkzeug’s logging setup, so you’ll see HTTP request logs automatically when running the development server.

Example of Logging in Flask

# app.py

from flask import Flask

import logging

logging.basicConfig(level=logging.INFO)

logger = logging.getLogger("flask_app")

app = Flask(__name__)

@app.route("/")

def hello():

logger.info("Root endpoint accessed")

return {"message": "This is a Flask API"}

Logging in Django REST Framework

Django comes with a more comprehensive logging system, configured via the LOGGING dictionary in settings.py. This configuration allows fine-grained control over loggers, handlers, and formatters. DRF inherits Django’s logging setup, so you can log from views, serializers, or anywhere else in your project.

Example of Logging in DRF

# settings.py

import logging

LOGGING = {

'version': 1,

'disable_existing_loggers': False,

'handlers': {

'console': {

'class': 'logging.StreamHandler',

},

},

'loggers': {

'django': {

'handlers': ['console'],

'level': 'INFO',

},

'drf_app': {

'handlers': ['console'],

'level': 'INFO',

},

},

}

# views.py

import logging

from rest_framework.decorators import api_view

from rest_framework.response import Response

logger = logging.getLogger("drf_app")

@api_view(['GET'])

def hello(request):

logger.info("Root endpoint accessed")

return Response({"message": "Logging in DRF"})

In summary, FastAPI and Flask rely on Python’s logging with minimal configuration and allow you to quickly add log statements. DRF on the other hand, uses Django’s LOGGING configuration which is more structured.

But you should also note that the core logging syntax is the same across all three so you can easily apply the same logging approach.

Deployment Considerations

FastAPI has a lightweight architecture with support for asynchronous operations, making it straightforward to deploy in containerized environments. One of the common production setups involves running it with Uvicorn or Gunicorn behind a reverse proxy such as Nginx. This option is minimal, and it suits the FastAPI structure.

Flask is small and synchronous by default so it is equally easy to deploy in a variety of environments from simple WSGI servers like Gunicorn to fully managed platforms such as Heroku.

Because it is a mature framework there are abundant deployment guides and templates, but the responsibility for configuring security, serving static files and other relevant aspects of the API still falls on you. Flask projects can become harder to containerize efficiently if the chosen extensions introduce large or incompatible dependencies.

DRF requires more preparation for deployment due to Django’s larger structure and dependencies. You need to configure the application server (often Gunicorn or uWSGI) alongside static and media file handling, database migrations, and settings for different environments.

Its built-in support for environment-specific settings, logging, and security middleware helps maintain consistency across deployments, but it generally requires more resources than FastAPI or Flask, which can affect hosting costs at scale.

• Options for Deployment The list below is the options you have when trying to deploy APIs built with any of the frameworks:

• Containerization: With containerization your API has a consistent runtime environment across all stages of the application. This approach also simplifies your CI/CD pipelines and enables easy scaling when paired with an orchestration tool.

• Kubernetes: Kubernetes orchestrates multiple containerized application instances, managing scaling, rollout, and recovery processes. It’s useful when running multiple services or when you need automated control over application availability.

• Platform as a Service (PaaS): These are platforms like Heroku, Render, Railway. They run your application in a hosted environment where the infrastructure and runtime are managed for you. This allows focusing on the application itself without handling operating system updates or server maintenance.

• Virtual Private Servers (VPS): VPS servers give you the opportunity to configure the API server however you want, even down to the operating system. You can use any VPS platform like DigitalOcean, Linode, or SSD Nodes. With VPS, you save a lot in deployment costs while maintaining full control over your infrastructure.

• Managed container services: Here the container runtime and deployment infrastructure is handled by the service provider but you have control over how containers are built and run. With managed container services, setup time is reduced while still allowing for custom container configurations.

How to Choose an API Framework for Your Project

To choose an API framework that is best for your use case you need to make use of the table provided in the beginning of this article. Let’s say for example you have a big team and what you care about the most is standardization, you would need to go through the table and see what option is the best based on standardization. The side-by-side view of the table helps you narrow down your options without getting lost in generic pros and cons.

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Conclusion

Each framework addresses different API development priorities, and aligning those with your project’s goals will prevent costly rewrites. Teams building high-concurrency services will benefit from FastAPI’s async model, while Flask remains a dependable option for controlled, minimalistic APIs.

Django REST Framework remains the strongest choice when database-backed features and admin integration dominate requirements. Future scalability depends on matching framework strengths to your application’s technical and operational demands.

By using the comparison table and detailed analysis provided in this guide, you now have the necessary insights to confidently select the Python framework that best fits your project’s technical requirements for 2025 and beyond.

Disclaimer: This content was submitted by our valued guest contributor through our Write for Us page.

Abdelhadi is a Python developer and SEO with a deep passion for the worlds of code, data, and tea. You can reach out to him via his personal website.