Fast Python Frameworks: FastAPI Explained

FastAPI is a fast-response Python framework whose design emphasizes performance and flexibility. Inspired by predecessor frameworks such as Flask and Django, it supports async/await. and more modern development is possible.In this article, we will explore the characteristics of FastAPI and its benefits in detail.


FastAPI is a Python web framework designed for speed and low overhead, with an emphasis on performance and flexibility.

FastAPI features include support for routing, request and response processing, and input validation. It also has a plugin-based architecture, making it easy for developers to extend its functionality.

FastAPI is based on Flask and Django, and is designed to return responses at high speed when building an API that returns JSON as a response.

Here, we will introduce the basic usage of FastAPI using the following versions.

FastAPI v0.68.0
Python 3.9.7

Basic concepts of FastAPI


Routing is a mechanism for receiving HTTP requests from clients and processing them accordingly. FastAPI can use decorators to precisely match requests for different URL paths and HTTP methods.

from fastapi import FastAPI

app = FastAPI()

# define route for GET method
def read_root():
  return {"Hello": "World"}


Defining Routes for Sods'/items/')
def create_item(item: dict):
  return item

The above example uses FastAPI decorators to define routing. @app.get('/') handles a GET request to the root URL (’/’). Similarly,'/items/') will handle a POST request to the ‘/items/’ URL.

Parameters and Request Body

FastAPI allows you to retrieve data from the client using route parameters, query parameters, and request bodies.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
  name: str
  price: float
  is_offer: bool = None

def read_item(item_id: int, q: str = None):
  return {"item_id": item_id, "q": q}'/items/')
def create_item(item: Item):
  return item

In the above example, the read_item function gets the route parameter item_id and the query parameter q. The create_item function gets the data of the Item model from the request body.

Response model

FastAPI allows you to explicitly specify the format of your API’s responses by using response models. This automatically generates API documentation and validates response data.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
  name: str
  price: float
  is_offer: bool = None'/items/', response_model=Item)
def create_item(item: Item):
  return item

In the above example, we specify the Item model as the response model for the create_item function. This ensures that the response returned by this function follows the Item model.

Features of FastAPI

High Speed

FastAPI is a very fast framework, designed on Starlette (async server) and Pydantic (data validation). This enables asynchronous request processing and fast data validation, greatly improving performance.

Ease and Intuition

FastAPI is a Python type

Use hints to automatically validate API parameters and request bodies and serialize data. This makes the code intuitive, easier to read, and prevents bugs.

Data validation

FastAPI uses Pydantic for data validation. This ensures the integrity of request and response data and automatically generates error messages.

Automatic documentation

FastAPI automatically generates documentation for your API. This allows you to see details such as API endpoints, parameters, request bodies and responses.

FastAPI vs Flask

Both FastAPI and Flask are popular Python-based web frameworks, but each has different characteristics. In this article, we’ll compare FastAPI and Flask and take a closer look at the benefits and appropriate use cases for each framework.

Speed ​​and efficiency

FastAPI is known as a fast and efficient framework. Asynchronous processing and faster data validation ensure excellent performance even under heavy loads. Flask, on the other hand, is also very popular, but is a bit slower than FastAPI.

Conclusion: FastAPI provides fast performance and has great efficiency in high-load environments.

Extensibility and plugin ecosystem

FastAPI has excellent extensibility. By leveraging Python’s type hints and decorators, you can easily add and customize functionality.

Flask, on the other hand, is a simpler framework and its extensibility is more limited than FastAPI. Plugins exist for Flask, but they don’t have as wide a plugin ecosystem as FastAPI.

Conclusion: FastAPI provides a great plugin ecosystem for extensibility and flexibility.

Learning curve and opening

Departure speed

Flask is a very simple framework that has a relatively short learning curve and is easy to handle for beginners. The development speed is also high, and applications can be built quickly.

FastAPI, on the other hand, has a slightly steeper learning curve and requires more in-depth knowledge and understanding. This is due to FastAPI’s optimization for speed and efficiency. FastAPI may take some time to learn, but the tradeoff is the ability to build high performance applications.

Conclusion: Flask has a shorter learning curve and faster development speed, while FastAPI has a slightly steeper learning curve, but allows you to build fast and efficient applications.

Use case

FastAPI is ideal for situations where high performance is required, such as fast API servers, microservices, and real-time applications. Especially when large-scale traffic or real-time data processing is required, the advantages of FastAPI become more pronounced.

Flask, on the other hand, is mainly used for simple application/prototype development and middleware processing. For small or monolithic applications, Flask is more than adequate.

Conclusion: FastAPI is good for high performance applications, Flask is good for simple applications and middleware usage.


FastAPI is a very useful framework when developing web applications as it is fast, highly scalable and offers additional features such as data validation and type hinting support.

Compared to Flask, FastAPI has better performance and offers great features such as clean implementation of asynchronous processing.

But Flask’s

It is also valued for its simplicity and intuitiveness, making it especially suitable for beginners and small projects.

Which framework you choose depends on a number of factors, including your project requirements, development team skill set, and expected performance.

FastAPI and Flask are designed as tools to serve different needs. Therefore, which