When you enter the world of Python, you will hear that many developers love Django ORM, and others love SQLAlchemy. Each of those groups will tell you to your face why you have to choose their loved library, and if we add the async part of the programming, they will brag about the capacity of their library to do it better. One is the Jedi, and the other is the Sith. 

Typically, they will love one or the other because they have a good experience with a project that uses that particular library, and they have a bad experience with a project that uses the other one. It is completely subjective because a good or bad project depends on many factors.

Here’s a secret between you and me: I don’t like either of them. So, I will try to be fair with both libraries. Although I will use the asynchronous methods of the library for this article, all the reasons for using one or the other in the conclusion are valid for synchronous, too.

First, I will give you a small introduction to each library and its asynchronous differences.

Django ORM Async

Django ORM, a widely used Object-Relational Mapping tool, introduced asynchronous support in Django 3.1. With the advent of Python’s async and await keywords, Django enables developers to write asynchronous database queries.

  •  Syntax: Django ORM async support is integrated into the existing API. You can use `await` to execute asynchronous queries, making it relatively straightforward for developers familiar with Django.
  • Database Backends: Django ORM provides asynchronous support for various database backends, including PostgreSQL, MySQL, and SQLite.
  • Concurrent Requests: Asynchronous support in Django ORM is geared towards handling concurrent requests efficiently, making it a valuable choice for high-traffic applications.
  • Compatibility: While asynchronous support is available, not all parts of Django are fully asynchronous. Certain features, like the Django admin interface, may still be synchronous.

SQLAlchemy Async

SQLAlchemy, a powerful SQL toolkit and Object-Relational Mapping library, introduced asynchronous support in SQLAlchemy 1.4. This allows developers to write asynchronous queries while taking full advantage of SQLAlchemy’s comprehensive feature set.

  • Syntax: SQLAlchemy’s approach to asynchronous queries involves using an `async with` context manager. While it may appear more verbose than Django’s syntax, it offers fine-grained control over transactions and connections.
  • Database Backends: SQLAlchemy’s async support is extensive, working with various database backends, similar to Django ORM.
  • Concurrent Requests: SQLAlchemy provides developers with fine-grained control over connection pooling and transaction isolation levels, making it suitable for both simple and complex use cases.
  • Flexibility: SQLAlchemy’s asynchronous support allows for flexibility in structuring and optimizing database queries. This can be advantageous when dealing with complex data models and query requirements.


​​Ultimately, both allow you to handle requests and execute queries asynchronously, improving the performance and scalability of your application.  Both libraries can manage asynchronous database operations effectively, but your project’s unique characteristics will influence the better fit. The choice between the two depends on your specific requirements and familiarity with the respective libraries.

Although the choice is yours, I will give you some tips:

  • Use Django ORM if:
    • You are already using Django for your web application, and your project primarily does not rely on complex queries.
    • You want a relatively smooth transition to synchronous/asynchronous programming within the Django Framework.
    • You value the convenience of Django’s built-in features like authentication, admin panel, and other high-level abstractions.
  • Use SQLAlchemy if:
    • You need to work with a variety of database backends.
    • You want granular control over database interactions in your project.
    • You have experience working with SQLAlchemy or are willing to invest time in learning it.
    • You want to take advantage of SQLAlchemy’s comprehensive toolkit for complex database tasks.