As a Senior Software Engineer in the AI field, I’ve had extensive experience working with various Python web frameworks. Today, I want to share an in-depth comparison of two popular frameworks: FastAPI and Flask.

This article will help you understand the key differences, strengths, and weaknesses of each framework, enabling you to make an informed decision for your next project.


Python has become a go-to language for web development, data science, and artificial intelligence. When it comes to building web applications and APIs, two frameworks that often come up in discussion are FastAPI and Flask. Both are lightweight and flexible, but they have distinct features and use cases. Let’s dive deep into each framework and compare them across various aspects.

FastAPI vs Flask

What is Flask?

Flask is a micro web framework for Python, released in 2010. It’s designed to be simple and easy to use, making it an excellent choice for beginners and small to medium-sized projects. Flask follows the principle of “batteries included, but removable” meaning it provides the essentials out of the box but allows for easy extension and customization.

Key features of Flask

  1. Simplicity: Flask has a small and easy-to-understand codebase.
  2. Flexibility: It allows developers to choose their tools and libraries.
  3. Jinja2 templating engine: For dynamic HTML rendering.
  4. Built-in development server and debugger.
  5. RESTful request dispatching.
  6. Support for secure cookies (client-side sessions).
  7. Extensive documentation and large community support.

Flask is widely used by companies like Netflix, LinkedIn, and Reddit for various applications.

What is FastAPI?

FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.6+ based on standard Python type hints.

It was first released in 2018 and has quickly gained popularity due to its speed, ease of use, and built-in features for API development.

Key features of FastAPI

  1. High performance: Built on Starlette and Pydantic, FastAPI is one of the fastest Python frameworks available.
  2. Fast to code: Increases developer productivity by reducing bugs and boilerplate code.
  3. Automatic API documentation: Generates interactive API docs and JSON Schema.
  4. Based on open standards: Supports OpenAPI (formerly Swagger) and JSON Schema.
  5. Built-in data validation: Uses Python type hints for request and response validation.
  6. Asynchronous support: Designed to work with async/await syntax.
  7. Dependency injection system: Makes it easy to share components across the application.

Companies like Uber, Netflix, and Microsoft have adopted FastAPI for various projects.

Performance comparison

One of the most significant differences between FastAPI and Flask is their performance characteristics.

Flask vs FastAPI Performance

Flask version upto 1.0 uses a synchronous model by default, which means it processes requests one at a time. This can lead to slower performance under high load or when dealing with I/O-bound operations. So consider using the latest versions.

FastAPI, on the other hand, is built for high performance from the ground up. It leverages asynchronous programming and Starlette’s ASGI server, allowing it to handle a large number of concurrent connections efficiently.

According to a benchmark study by Miguel Grinberg, FastAPI can be faster or slower than async Flask, depending on the web server and the Flask async type. Generally Flask on a Greenlet powered WSGI server (Meinheld / Gevent) can offer comparable throughput as an async-first ASGI framework like FastAPI. Note that Grinberg is comparing the overall performance of three parts: the Framework, the Web server and the Web application.

Here are the relevant frameworks’ throughput in the three different scenarios he tried (a higher number is better):

Framework Web Server Type Test 1 Throughput Test 2 Throughput Test 3 Throughput
Flask Meinheld Async / Greenlet 1.43 5.27 1.06
Flask Gevent Async / Greenlet 1.22 4.54 1.01
FastAPI Uvicorn Async / Coroutine 1.21 4.33 1.02
Aioflask Uvicorn Async / Coroutine 1.1
Flask uWSGI Sync 1.09 1.01 1.26
Flask Gunicorn Sync 1.00 (baseline) 1.00 (baseline) 1.00 (baseline)

Grinberg points out in his article that the exact relative results between frameworks will depend on the particular load that the server is put under, but his takeaway is that there isn’t a large difference between frameworks:

remember that the difference in performance between different frameworks or web servers isn’t going to be very significant, so choose the tools that make you more productive!

Flask adopts different approaches to async view. In Grinberg’s benchmark tests, three approaches have been implemented: Aioflask (standard python ASGI with uvicorn), Greenlet in Meinheld and Gevent. Since the Aioflask test was not fully finished, only the result of Test 1 is available, which agrees with the Flask documentation:

Flask’s async/coroutine support is less performant than async-first frameworks due to the way it is implemented.

Be aware that the Greenlet approach requires a special way of writing asynchronous operations, instead of standard python coroutines. Therefore, existing asyncio codebase needs to be patched with adapters like greenletio. Gevent relies heavily on proper monkey patching the python standard libraries, database engines and other performance critical libraries.

Grinberg addressed multiple times this in his blog:

Gevent tests did reasonably well in my benchmark and terribly in the original one. This is because the author forgot to patch the psycopg2 package so that it becomes non-blocking under greenlets.

Ease of use and learning curve

Both Flask and FastAPI are designed to be developer-friendly, but they cater to slightly different audiences.

Flask: simplicity for beginners

Flask is often praised for its simplicity and ease of use. Its minimalist design makes it an excellent choice for beginners who are just starting with web development. The framework’s core is small and easy to understand, allowing developers to gradually add complexity as needed.

Flask’s documentation is comprehensive and well-organized, making it easy for newcomers to get started. The large community around Flask also means that there are plenty of tutorials, examples, and third-party extensions available.

FastAPI: modern features with a steeper learning curve

While FastAPI is designed to be easy to use, it introduces some concepts that might be new to developers who are not familiar with modern Python features or API development best practices. The use of type hints, Pydantic models, and asynchronous programming can present a steeper learning curve for beginners.

Flask vs FastAPI learning curve

FastAPI vs Flask learning curve

However, for developers who are already comfortable with these concepts, FastAPI can significantly boost productivity. Its automatic documentation and validation features can save a lot of time and reduce the likelihood of bugs.

API Development and Documentation

When it comes to building APIs, both frameworks have their strengths, but FastAPI has a clear advantage in terms of built-in features.

Flask API development

Flask doesn’t come with built-in tools specifically for API development, but it’s flexible enough to be used for building RESTful APIs. Developers often use extensions like Flask-RESTful or Flask-RESTX to add API-specific functionality.

For API documentation, Flask relies on third-party tools like Swagger UI or ReDoc. These need to be set up manually, which can be time-consuming.

FastAPI API development

FastAPI shines when it comes to API development. It’s built from the ground up with modern API development practices in mind. Some key advantages include:

  1. Automatic request and response validation using Pydantic models.
  2. Built-in support for OpenAPI (Swagger) and JSON Schema.
  3. Automatic interactive API documentation with Swagger UI and ReDoc (like Flask).
  4. Easy handling of authentication and authorization.
FastAPI auto documentation

FastAPI auto documentation

The automatic API documentation in FastAPI is a significant time-saver. It generates interactive docs that allow developers and API consumers to test the endpoints directly from the browser.

Asynchronous programming support

The support for asynchronous programming is one of the most significant differences between Flask and FastAPI.

Flask and Asynchronous Programming

Flask was originally designed with a synchronous programming model in mind. While it’s possible to use asynchronous programming with Flask using extensions like Flask-AIOHTTP or by running Flask with an ASGI server, it was not a native feature of the framework.

This means that for applications that require handling many concurrent connections or dealing with I/O-bound operations, Flask might not be the best choice out of the box.

FastAPI and asynchronous programming

FastAPI is built on top of Starlette, an ASGI framework, which means it has native support for asynchronous programming. This allows FastAPI to handle a large number of concurrent connections efficiently.

Here’s a simple example of an asynchronous endpoint in FastAPI:

from fastapi import FastAPI
import asyncio

app = FastAPI()

async def root():
    await asyncio.sleep(1)  # Simulating an I/O operation
    return {"message": "Hello World"}

This native support for asynchronous programming makes FastAPI an excellent choice for applications that need to handle real-time data, websockets, or a large number of concurrent connections.

Data validation and type checking

Data validation is a crucial aspect of web application development, especially when building APIs. Flask and FastAPI handle this differently.

Flask data validation

Flask doesn’t come with built-in data validation. Developers typically use third-party libraries like WTForms or Marshmallow for form validation and data serialization. While these libraries are powerful, they require additional setup and configuration.

FastAPI data validation

FastAPI leverages Python’s type hints and Pydantic models for automatic request and response validation. This approach not only provides robust data validation but also improves code readability and helps catch errors early in the development process.

Here’s an example of data validation in FastAPI:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = None"/items/")
async def create_item(item: Item):
    return {"item": item}

In this example, FastAPI automatically validates that the incoming request data matches the Item model. If the data doesn’t match, it returns a detailed error message.

Community and ecosystem

The community and ecosystem surrounding a framework can greatly influence its adoption and long-term success.

Flask community

Flask has been around since 2010 and has built a large and active community over the years. This mature ecosystem offers several advantages:

  1. Extensive documentation and tutorials.
  2. A wide range of third-party extensions for various functionalities.
  3. Many Stack Overflow questions and answers.
  4. Regular updates and maintenance.

The Flask community’s size and maturity make it easier to find solutions to common problems and get help when needed.

FastAPI Community

While FastAPI is newer, its community has been growing rapidly since its release in 2018. The FastAPI community is known for its enthusiasm and the framework’s creator, Sebastián Ramírez, is very active in supporting users.

FastAPI vs Flash GitHub stars

FastAPI vs Flash GitHub stars

FastAPI’s community offers:

  1. Comprehensive and well-maintained official documentation.
  2. Growing number of third-party packages and extensions.
  3. Active GitHub discussions and issue resolutions.
  4. Regular updates with new features and improvements.

While smaller than Flask’s community, the FastAPI community is very active and supportive, especially when it comes to modern API development practices.

Premium content from UnfoldAI (ebooks, cheat sheets, tutorials)

Use cases and when to choose each framework

Both Flask and FastAPI have their strengths and are suited for different types of projects. Below you will find an image which should help you for choosing the best framework for your project:

Flask vs FastAPI usecases

Flask and FastAPI usecases

Or in more details:

When to Choose Flask

  1. Small to medium-sized web applications.
  2. Prototyping and quick MVP development.
  3. Educational projects and learning web development.
  4. Applications that don’t require high concurrency.
  5. Projects where you need full control over the components and architecture.
  6. When working with a team more familiar with traditional synchronous programming.

When to Choose FastAPI

  1. High-performance API development.
  2. Microservices architecture.
  3. Real-time applications and websockets.
  4. Projects requiring extensive data validation.
  5. Applications expecting high concurrency and needing to handle many simultaneous connections.
  6. When automatic API documentation is a priority.
  7. For teams comfortable with modern Python features like type hints and async/await syntax.


Both Flask and FastAPI are excellent Python web frameworks, each with its own strengths and ideal use cases. Flask’s simplicity and flexibility make it a great choice for beginners and small to medium-sized projects where performance isn’t a critical factor. Its large ecosystem and community support provide a wealth of resources and extensions.

On the other hand, FastAPI’s high performance, built-in async support, and automatic API documentation make it an excellent choice for modern API development, especially when dealing with high concurrency or complex data validation requirements. Its use of Python’s type hints also contributes to better code quality and developer productivity.

As a Senior Software Engineer in the AI field, I’ve found both frameworks valuable in different scenarios. For quick prototypes or simple web interfaces for machine learning models, Flask often provides the simplicity and speed of development I need. However, for building high-performance APIs that need to handle real-time data or integrate with modern front-end frameworks, FastAPI has become my go-to choice.

Ultimately, the choice between Flask and FastAPI depends on your specific project requirements, team expertise, and performance needs. Both frameworks have their place in the Python ecosystem and continue to evolve to meet the needs of modern web development.

Remember, the best framework is the one that helps you solve your specific problems efficiently. Don’t be afraid to experiment with both and see which one fits your needs best.

FastAPI vs Flask banner

Last Update: 07/07/2024