Flask and FastAPI are popular Python micro-frameworks for developing small-scale data science and machine learning websites and applications.
When creating a Python app, you have two options: go for Flask vs. FastAPI. Both libraries offer the same features, but the implementation is different.
Comparing both web frameworks, we can see Flask is more used for mobile and web development than FastAPI:
But does this mean that Python Flask is better than FastAPI? We've compared the key pros and cons of Flask and FastAPI to help you decide which one you should choose. Let's discuss both sides now!
Table of Contents
Flask is a web framework and a Python module that allows you to create web applications easily. It has a small and simple core: a microframework without an ORM (Object Relational Manager) or similar features.
Flask is also known as a microframework since it does not offer an extensive set of features like a full stack framework. However, this allows the intuitive framework to use for many applications. The Flask framework is built on the Werkzeug toolkit and Jinja2 templating engine, which helps to create a lightweight web application with lower resource consumption. Among its cool features are URL routing and template engines. Moreover, Flask is deployed on WSGI (Python Web Server Gateway Interface). It is easily extensible with the help of third-party libraries and has a simple structure.
Uber, Microsoft, Explosion AI, and others are currently using it.
Easy to understand and start with
The jargon and syntax associated with Flask are easier to grasp than in other frameworks.
Flask supports unit testing
If you are a person who values code readability and efficiency, then you'll surely appreciate unit testing. With Flask, you can simulate various conditions and test your application's functionality to ensure it runs smoothly under all conditions.
It comes with a built-in development server
The best way to test your application is by setting up a development environment where you can simulate the production environment. The development server with the Flask framework makes this process even simpler by letting you test your application without putting it into production.
Easy to extend functionality
If you don't want to start from scratch and want to enhance the functionality of an existing application, then it is much easier to do it with Flask. Just for kicks, let's say you want to add a comment section to your application. Well, you won't have to go through the lengthy process of starting from scratch. Instead, you'll be able to easily add the desired functionality to your existing application by making a few changes in the code.
No need to worry about scalability
If you plan on making your application available on a larger scale, then you shouldn't worry about the scalability of your application. Flask is highly scalable and lets you create a large application with minimum effort.
Flask is single threaded and synchronous by default
This means that each request is handled in turn while waiting for the previous task to complete.
No out-of-the-box support for session management
The lack of session management in Flask is a major drawback because it means you have to implement the feature yourself. You'll have a hard time dealing with requests and responses that are linked to one user's interactions of your service or application if you don't have this functionality. The process isn't too complicated but still takes some time when implementing into an app.
It uses Modules
And they are shared by the framework and the developer. These are vulnerable to security flaws.
Flask is a web framework that is HTML-oriented
It is not necessarily designed to create APIs. Of course, it is possible, but it is not Flask's primary goal. Because there is no standard way of writing in Flask, it is preferable to become more familiar with the framework before embarking on a larger project.
No built-in support for database migrations
Data migration is the process of moving information from source to target databases. Users who accessed the source databases will now use the target databases. So, migrating your database and keeping track of different versions can be challenging, but it's necessary. Luckily, third-party libraries let you create a migration manager and track different database versions. But each database type will require its own library (PostgreSQL, MySQL, etc.).
To construct serverless APIs quickly and easily, you can use FastAPI a microframework for Python web development. It provides a slew of features that make creating and managing APIs a snap. The standard web server-web application interface of the framework is ASGI (Asynchronous Server Gateway Interface). Even though Jinja2 isn't required, it is the template engine of choice. FastAPI will work with any database and any library style for databases.
Netflix, Lyft, and Zillow are currently using Flask. It is the most popular Python development framework for newcomers.
FastAPI surpasses Flask in terms of performance, and it is one of the fastest Python web frameworks. Only Starlette and Uvicorn are faster. Because of ASGI, FastAPI supports concurrency and asynchronous code by declaring the endpoints.
For concurrent programming, Python 3.4 introduced Async I/O. FastAPI simplifies concurrency by eliminating the need for an event loop or async/await management. The initial path function can then be specified as coroutines using async def and await specific locations by developers.
Dependency injection support
FastAPI supports a dependency injection solution that is simple and easy to use. This method ensures that different classes are not directly dependent on one another. It makes it easier to make changes to your code, which can be helpful. This technique increases the modularity of the code and the scalability of the system by achieving inversion of control. FastAPI's path operation functions enable developers to declare relevant dependencies.
The documentation generated by FastAPI is useful. The documentation assists developers in explaining the software to others, simplifies the use of your backend by front-end engineers, and simplifies API endpoint testing.
Built-in data validation enables developers to omit proof and write more compact code. It detects incorrect data types and returns the underlying reasoning in JSON. FastAPI uses the Pydantic module to simplify validation and speed up typing. According to FastAPI's authors, it reduces developer errors by 40%.
FastAPI isn't secure. Instead, fastapi.security handles security. At the same time, it supports OAuth2.0.
Small developers group
FastAPI is eight years younger than Flask. Thus its community and educational materials are still modest. Searching reveals few books, guidelines, or lessons. Growing popularity may change this in the future.
It will depend on which library you decide to use. The Flask framework is well-suited for those looking to build up their own applications. If you have a limited amount of time and want to build a simple API, you should use the Flask framework. Despite its complexity, the FastAPI framework provides a wider range of API management and monitoring tools. The FastAPI library, on the other hand, should be used if you want to make sure your application is always up (and running) with extended functionality.
A Python application is an excellent way to bring new features and solutions to the table. However, before diving into the development process, you must decide on the framework that will power it.
Flask and FastAPI can put up Python web servers and data science programs rapidly. They deploy with the same effort. So how do you choose a web framework?
FastAPI is superior for speed and performance. Choose this latest framework if you're constructing your content delivery network and expect traffic. FastAPI's cutting-edge framework and project template will save you time. It’s also superior to Flask for creating APIs, especially microservices. Flask would only be a good choice if your company already uses it extensively.
Flask is better for simple microservices with a few API endpoints. It's excellent for constructing machine learning models and data-backed web app prototypes. It’s a good choice if you want to develop a simple app that can grow quickly and in ways you haven't considered. It's easy to use and scales well with few dependencies.
When it comes down to which one is better, it comes down to your application requirements. So, before deciding on a framework, ensure you thoroughly understand your project and its scope.
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