How do I add authentication to a cookiecutter-fastapi project?

You'll need to manually integrate authentication libraries like FastAPI Users or OAuth2, as the template doesn't include auth by default. Start by installing relevant packages and modifying the app structure to handle user management and security.

What's the difference between cookiecutter-fastapi and fastapi-fullstack?

cookiecutter-fastapi focuses on backend APIs with ML readiness and modern tooling like uv, while fastapi-fullstack often includes frontend components and more comprehensive features like admin panels. Choose based on whether you need a lean API starter or a full-stack solution.

Can I use cookiecutter-fastapi for non-ML projects?

Yes, but you may need to remove or ignore the ML-specific dependencies and configurations. The template is flexible enough for general FastAPI use, though the ML emphasis might add unnecessary complexity for simple APIs.

How to customize the generated project structure?

Modify the Cookiecutter template variables during generation or edit the Jinja2 templates directly in the source. For post-generation changes, manually adjust files and directories, but note this diverges from the opinionated setup.

Is cookiecutter-fastapi good for large enterprise teams?

It can be, due to its consistency and CI/CD integration, but large teams might find the opinionated structure restrictive if they have legacy systems or specific compliance requirements not addressed by the template.

How to update dependencies in a cookiecutter-fastapi project?

Use uv or pip to update packages in the pyproject.toml or requirements files, and ensure tests pass with the new versions. The template doesn't automate dependency updates, so manual management is required.

Open-Awesome

cookiecutter-fastapi

MITPythonv1.2.0

A Cookiecutter template for generating production-ready FastAPI projects with machine learning, uv, GitHub Actions, and Pytest.

GitHub

709 stars74 forks0 contributors

What is cookiecutter-fastapi?

cookiecutter-fastapi is a Cookiecutter template that generates a complete, production-ready FastAPI project boilerplate. It automates the initial setup by providing a structured foundation with integrated tools for machine learning, dependency management, CI/CD, and testing. This solves the problem of repetitive project configuration, allowing developers to start building features immediately.

Target Audience

Python developers, especially those working with FastAPI, who need a quick, standardized way to bootstrap new projects with modern tooling. It's ideal for teams or individuals focusing on machine learning APIs or production web services.

Value Proposition

Developers choose this template because it offers an opinionated, best-practice setup that includes essential tools like uv, GitHub Actions, and Pytest out of the box. It saves time, ensures consistency, and reduces configuration errors compared to manually setting up a FastAPI project from scratch.

Overview

Cookiecutter template for FastAPI projects using: Machine Learning, uv, Github Actions and Pytests

Use Cases

Best For

Quickly bootstrapping a new FastAPI project with a standardized structure
Building machine learning APIs that require integrated ML dependencies and workflows
Teams needing consistent project setups across multiple repositories
Projects that require pre-configured CI/CD with GitHub Actions
Developers who prefer using uv for fast and reliable Python package management
Ensuring testing best practices with a ready-to-use Pytest configuration

Not Ideal For

Projects using alternative web frameworks like Django or Flask, as the template is exclusively designed for FastAPI.
Teams with established, highly customized development workflows that conflict with the opinionated directory structure and tooling.
Simple APIs without machine learning needs, where the included ML dependencies and configurations add unnecessary overhead.

Pros & Cons

Pros

Rapid Project Bootstrap

Generates a complete FastAPI project with a standard directory structure and essential configurations, saving hours of manual setup.

ML-Ready Integration

Includes pre-configured dependencies and setup for machine learning workflows, making it ideal for data science teams building APIs.

Modern Tooling Built-in

Uses uv for fast package management and comes with GitHub Actions for CI/CD, ensuring efficient development and deployment out of the box.

Testing Foundation

Provides a configured Pytest suite, promoting reliable test execution and adherence to best practices from the start.

Cons

Limited Out-of-the-Box Features

Lacks built-in authentication, database ORM integration, or frontend components, requiring additional manual setup for common web app needs.

Tooling Assumptions

Assumes familiarity with Cookiecutter, uv, and GitHub Actions, which can be a barrier for developers new to these modern Python ecosystems.

Overhead for Simplicity

The opinionated structure and ML focus may introduce bloat for basic REST APIs, as not all projects need the full boilerplate.

Frequently Asked Questions

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