Compare Flask and FastAPI
Flask is a Python web framework for building web applications. It is based on Werkzeug and Jinja 2. It is a minimalist, 'no batteries included' framework. Yet it can be scaled extensively and support complex applications and use cases by adding required functionality as needed. It follows the philosophy that if something needs to be initialized, it should be initialized by the developer.
Fast API is a high-performance web framework for building web applications with Python 3.6+ based on standard Python type hints. It is designed to be high performance and easy to learn.
Let's see how Flask and FastAPI compare on various factors and features and which to choose when.
Type
Python microframework for building web applications.
Type
Minimalistic framework based on starlette and pydantic for building fast web applications using async IO.
Used by 397,000 projects.
New addition to the Python web frameworks family but its popularity is on the rise.
Used by
Netflix, Zillow, Lyft.
Used by
Uber (internal use,) Explosion AI, Microsoft (internal use)
1067 job openings which list Flask as a requirement.
100 job openings which list Fast API as a requirement.
Because it is minimal and doesn't have a lot of overhead, Flask is very performant. Extensions could impact performance negatively.
Flexibility
Very flexible and doesn't require users to use any particular project or code layout. (A structured approach is still recommended.)
Flexibility
Fast API is flexible to code and doesn't restrict users to a particular project or code layout.
Ease of Learning
Flask is simple and its core features are not difficult to learn. There are also plenty of online resources available to aid in learning.
Ease of Learning
Easy to learn especially for people who are new to web development. However, it doesn't have a large number of online resources, courses and tutorials.
RDBMS Support
Through Plugins or Extensions
Flask doesn't come with a built-in ORM framework. Developers can use one of many open source libraries or extensions. Such as
Flask-SQLAlchemy,
Flask-Pony, etc.
RDBMS Support
Through Plugins or Extensions
FastAPI doesn't come with built in ORM, however is compatible with
SQLAlchemy, Pydantic ORM mode.
NoSQL Support
NoSQL databases are supported through open source libraries or extensions. To use MongoDB with Flask,
Flask-PyMong is a popular choice. CouchDB, Cassandra, and DynamoDB are also supported via libraries.
Verdict Flask is a great choice if you want to develop for a NoSQL database.
NoSQL Support
Fast API supports many NoSQL databases like MongoDb, ElasticSearch, Cassandra, CouchDB and ArangoDB. Read more
here.
Admin Dashboard
Through Plugins or Extensions
No built-in admin panel, but you can use the
Flask-Admin extension. It supports a number of backends like SQLAlchemy, MongoEngine, Peewee etc.
Admin Dashboard
Yes, uses Swagger as an API documentation web user interface.
REST Support
Yes, allows developers to build REST APIs quickly.
Security
Despite being a minimalist Framework, Flask does an excellent job of addressing common security concerns like CSRF, XSS, JSON security and
more out of the box. 3rd party extensions like
Flask-Security can be used for common security measures. However, it requires that developers evaluate these extensions carefully for security risks and apply timely updates manually when vulnerabilities are discovered.
Security
FastAPI provides several tools for many security schemes in the fastapi.security module. Not enough data.
Templating Library
Flask uses
Jinja2 out of the box.
Templating Library
FastAPI supports Jinja2 for templating and also supports aiofiles for serving static files
Web Forms
No built-in support but there is
Flask-WTF extention. For SQLAlchemy support, that is, to create forms based on models, there is
WTForms-Alchemy
Web Forms
Ships with it's own
Forms, with basic features.
Authentication
Fast API supports OAuth2, JWT and simple HTTP authentiation.
Testing
Built-in support using Python's
unittest framework.
How is performance rating determined?
Performance rating is determined using reputable online benchmarks listed below.
Where is job data coming from?
Job data is collected from Indeed, Google Jobs and Stack Overflow jobs.
How is popularity calculated?
Popularity is calculated using a formula which looks at weighted score on the following publicly available data points:
- Popularity per Google Trends
- Number of GitHub Users
- Number of GitHub Stars
How is this calculated?
Ease of learning is calculated using the following data:
- Number of features and depth of tool.
- Number of online resources: articles, blogs, tutorials and YouTube videos.
- Number of courses
- Freshness of online material
For example, a microframework may not have a lot of online resources but still get a high-rating because it's minamalistic and easy to learn just by following official documentation.
If you found this useful, please help us grow by sharing this article with your followers using the sharing icons. Every share or call out will help. Thank you.
Credits
This page was made possible thanks to contributions from
Soumyaranjan Acharya who provided data and write up for Fast API.
Similar Comparisons
