Flask vs. FastAPI: Which One to Choose

When it comes to web development, picking the right framework can feel like choosing the perfect tool for a job—it’s essential to get it right. Flask and FastAPI are two of the top contenders in the Python world, each bringing something unique to the table. Whether you're working on a simple website or a high-powered API, knowing the differences between Flask and FastAPI can help you make the best decision for your project.

Flask is a micro web framework that shines when building small to medium applications, thanks to its simplicity and ease of use. On the other hand, FastAPI is a modern, high-performance framework specifically designed for building APIs with Python. It offers built-in support for asynchronous tasks, data validation, and automatic documentation. While Flask is perfect for straightforward web applications, FastAPI truly excels in scenarios that require speed, scalability, and real-time data processing.

In this article, we’ll walk you through what makes Flask and FastAPI different, using real-world examples to help you see which one is right for you. Whether you're just starting out or you’re a seasoned developer, this guide will help you choose the framework that best fits your needs.

What is Flask?

As we read in the introduction, Flask is a micro web framework that is used to build lightweight web applications with ease. The microframework word comes from the fact that it does not require any particular library or framework for building applications. It provides components like routing, request handling, etc. Flask is currently used by Netflix, Lyft or Zillow.

Key Features

1. Lightweight: Flask is a lightweight framework as it is independent of external libraries which makes it a good option for beginners to build a complex application with ease.
2. Jinja2 Templating Engine: Jinja2 is a fast, expressive, extensible templating engine. Flask comes with Jinja2 as its inbuilt templating support.
3. WSGI compliant: Flask is a WSGI application i.e. it converts incoming HTTP requests to a WSGI environment and also converts the WSGI response to an HTTP response.
4. Modular: Flask provides a structured way to build applications by dividing them into small modules.

What is FastAPI?

FastAPI is a web framework that is used to build APIs with Python 3.7+ and a standard type hint. It is used to specifically create RESTful APIs. It also provides automation for producing documentation for the service you created. It’s currently used by Uber, Microsoft, Explosion AI and others.

Key Features

• Built-in Security: Fast API provides built-in security mechanisms through features like validation and sanitization of user input.
• Scalability: FastAPI is one of the few Python frameworks that can be used to build scalable applications.
• Automatic Data Validation: Data validation ensures that the data is up to date and FastAPI provides an automatic data validation mechanism which is much faster than traditional manual validation.
• High Performance: FastAPI is a modern framework which provides performance at par with NodeJS and Go

Flask vs FastAPI: Detailed Comparison

Flask and FastAPI are both Python web frameworks that can be used to build web applications and APIs. However, there are some key differences between the two frameworks.

1. HTTP Methods

Flask

Flask is a micro framework and provides support for all HTTP methods which are GET, POST, PUT, DELETE, etc. You can use HTTP methods in Flask using decorators. This makes route handling straightforward. Following are how GET and POST methods are used in Flask.

@app.route(“/”, methods = [“GET”])
@app.route(“/”, methods = [“POST”])

FastAPI

FastAPI has more modern Python features like type hints and asynchronous support. This asynchronous support makes it easy to handle asynchronous operations and I/O-bound tasks. Following are how GET and POST methods are used in FastAPI.

@app.get(“/”)
@app.post(“/”)

2. Data Validation

• Flask: Flask does not support in-built data validation. If you try to pass the wrong type of input than the desired input type, the program will simply crash. Developers can use external libraries like WTForms or some other libraries to ensure data validation.
• FastAPI: FastAPI on the other hand provides in-built automatic data validation. FastAPI achieves it as it is integrated with Pydantic models. These models use Python-type annotations which help validate the requests and responses to and from the API.

3. Error Message Display

• Flask: Flask uses custom error handlers that have to be defined by the developers to display custom error messages. By default, HTML pages are used to display error messages in Flask.
• FastAPI: FastAPI which is integrated with Pydantic models automatically generates detailed and user-friendly error messages. By default, FastAPI displays error messages in JSON format.

4. Asynchronous Tasks

• Flask: Flask does not support asynchronous tasks but requires external resources for it. Due to its non-inherent design for asynchronous programming, the chances to achieve high performance using asynchronous tasks become difficult.
• FastAPI: FastAPI ASGI supports asynchronous tasks. Due to its inherent design for asynchronous programming, FastAPI can handle large volumes of concurrent requests and is ideal to be used for real-time applications.

5. Performance

• Flask: Flask performs well in applications that do not require I/O bound tasks and applications that do not involve handling large volumes of concurrent connections.
• FastAPI: FastAPI outperforms Flask in applications that require I/O bound tasks and applications that involve handling large volumes of concurrent connections.

6. Documentation Support

• Flask: Flask provides manual documentation support. To automate the documentation process of a Flask application, external tools like Swagger have to be used.
• FastAPI: FastAPI provides in-built automatic documentation support. It helps provide a UI for testing your service. To access these automatically generated documentation, hit the endpoint of the API that has to be tested with /docs.

7. Community Support

• Flask: Flask being an older framework has great community support. It has many resources, from tutorials and guides to third-party extensions and plugins.
• FastAPI: FastAPI is a newer framework with a small community size which is gradually increasing due to its great performance and features.

Pros and Cons of Flask and FastAPI

Pros of Flask

1. Built-in Development Server: A development environment is very different from a production environment. It is important to stimulate the production environment for your application before it is sent out for production to avoid any issues and errors. With Flask, you can test your application before actually putting it into production.
2. Unit Testing: Flask provides unit testing that can be used to simulate various conditions and test your application's functionality to ensure code readability and efficiency.
3. Scalability: With Flask, you can easily scale your application with minimal effort. Developers can easily add features on the go.
4. Easy to start: Flask is easy to understand and its syntax is also easier to grasp making it a good choice for beginners.

Cons of Flask

1. Single-Threaded and Synchronous: Flask is a single-threaded framework where each process can only start after the previous one has been completed. This nature is also called the synchronous behaviour of the application.
2. Session Management: Flask does not provide an in-built session management system thus it lies on the shoulders of the developer to link the requests and responses of a user’s interaction with your application.
3. HTML-Oriented: Writing APIs is not the main goal of Flask.
4. Database Migrations: Moving information from source to target is called data migrations. Migrating databases and keeping track of the versions is difficult and can be achieved by using third-party libraries.

Pros of FastAPI

1. Concurrency: Python 3.4 introduced Async I/O for concurrency. FastAPI simplifies concurrency as you do not need to create event loops or use async/await management.
2. Documentation Support: FastAPI provides in-built automatic documentation support. It helps provide a UI for testing your service. To access these automatically generated documentation, hit the endpoint of the API that has to be tested with /docs.
3. Dependency Injection: FastAPI comes with an in-built dependency injection solution and makes sure that classes are not interdependent. Thus, increasing the modularity and efficiency of your application. This also helps to make changes in the application without any conflicts.
4. Data Validation: FastAPI provides in-built automatic data validation. FastAPI achieves it as it is integrated with Pydantic models. These models use Python-type annotations which help validate the requests and responses to and from the API.

Cons of FastAPI

1. Security: FastAPI does not provide an in-built security system.
2. Small Community: FastAPI being a newer framework has a smaller community and less material as compared to Flask which is quite older.
3. Learning Curve: It can take time to grasp asynchronous programming and Pydantic for data validation.
4. Overhead for Small Projects: For simple projects, FastAPI's full feature set and performance focus might be unnecessary.

Flask vs FastAPI: Key Differences

@app.route(“/”, methods = [“GET”])
@app.route(“/”, methods = [“POST”])

@app.get(“/”)
@app.post(“/”)

Conclusion

In conclusion, we discussed the differences between Flask and FastAPI. Flask is a micro framework, while FastAPI is a full-stack framework. FastAPI is much better when it comes to performance and speed. Flask, on the other hand, is better when it comes to simpler applications. The features that make FastAPI a better choice for complex and data-intensive applications are dependency injections, automatic documentation support, data validation and asynchronous programming. Although, FastAPI has a smaller community as compared to Flask its high performance and efficiency have made it very popular in the market.

Also Read:

• Fast API - Gunicorn vs Uvicorn
• Deploy Machine Learning Model using Flask