Best Python ORMs in 2026
A practical guide to Python ORMs for database interaction. From full-featured SQLAlchemy to lightweight alternatives, find the right ORM for your project.
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An ORM (Object-Relational Mapper) lets you interact with databases using Python objects instead of raw SQL. The right ORM dramatically improves productivity while the wrong one becomes a constant source of frustration.
This guide covers the best Python ORMs based on production usage, framework compatibility, async support, and query flexibility.
SQLAlchemy
SQLAlchemy is the most powerful and flexible Python ORM. It offers both a high-level ORM and a low-level SQL expression language, letting you drop to near-raw SQL when needed. Version 2.0 brings full async support and modern Python patterns. Works with any framework.
Best for: FastAPI, Flask, complex queries, multi-database projects
Django ORM
Built into Django, this ORM prioritizes simplicity and rapid development. The QuerySet API is intuitive, migrations are automatic, and the admin panel integrates seamlessly. Limited async support but excellent for standard Django projects.
Best for: Django projects, rapid development, admin-heavy applications
SQLModel
Created by the FastAPI author, SQLModel combines SQLAlchemy and Pydantic into a single model definition. Define your database model and API schema in one class. Perfect for FastAPI projects where you want minimal boilerplate between database and API layers.
Best for: FastAPI projects wanting unified database + API models
Tortoise ORM
An async-first ORM inspired by Django. Tortoise provides a familiar Django-like API but built from the ground up for async Python. Good choice for async frameworks when you want Django-style simplicity without SQLAlchemy's complexity.
Best for: Async projects wanting Django-style ORM simplicity
Peewee
A small, expressive ORM that's easy to learn and use. Peewee is perfect for small to medium projects where you want an ORM without the complexity of SQLAlchemy. Supports SQLite, MySQL, and PostgreSQL.
Best for: Small projects, scripts, prototypes, SQLite applications
How We Chose These Tools
ORMs were evaluated on: query flexibility (can you express complex queries?), async support (essential for modern APIs), learning curve, migration tooling, and framework integration. Rankings reflect production experience building APIs and web applications.
Frequently Asked Questions
Should I use an ORM or raw SQL?
Use both. ORMs excel at CRUD operations and maintaining clean, portable code. Drop to raw SQL for complex analytical queries and performance-critical operations. SQLAlchemy uniquely supports both approaches seamlessly.
Which ORM works best with FastAPI?
SQLAlchemy 2.0 with async support is the most mature choice. SQLModel offers a simpler experience by combining Pydantic and SQLAlchemy. Tortoise ORM is a lighter alternative. All three work well with FastAPI.
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