Local Speed, Smooth Deploys: Heroku Adds Support for uv

Ah, another day, another deep dive into the ever-evolving world of Python development! Today, let’s talk about something near and dear to every Pythonista’s heart – managing those crucial external packages. For years, pip has been our trusty companion, the workhorse that gets the job done. But the landscape is shifting, and a new contender has entered the arena, promising speed, efficiency, and a fresh approach: uv.

As a Python developer constantly striving for smoother workflows and faster iterations, the buzz around uv has definitely caught my attention. So, let’s roll up our sleeves and explore the benefits of using uv as your Python package manager, taking a look at where we’ve come from and how uv stacks up. We’ll even walk through setting up a project for Heroku deployment using this exciting new tool.

A trip down memory lane: The evolution of Python package management

To truly appreciate what uv brings to the table, it’s worth taking a quick stroll down memory lane and acknowledging the journey of Python package management.

In the early days, installing Python packages often involved manual downloads, unpacking, and running setup scripts. It was a far cry from the streamlined experience we have today. Then came Distutils, which provided a more standardized way to package and distribute Python software. While a significant step forward, it still lacked robust dependency resolution.

Enter setuptools, which built upon Distutils and introduced features like dependency management and package indexing (the foundation for PyPI). For a long time, setuptools was the de facto standard, and its influence is still felt today.

However, as the Python ecosystem grew exponentially, the limitations of the existing tools became more apparent. Dependency conflicts, slow installation times, and the complexities of managing virtual environments started to become significant pain points.

This paved the way for pip (Pip Installs Packages). Introduced in 2008, pip revolutionized Python package management. It provided a simple and powerful command-line interface for installing, upgrading, and uninstalling packages from PyPI and other indices. For over a decade, pip has been the go-to tool for most Python developers, and it has served us well.

But the increasing complexity of modern Python projects, with their often intricate web of dependencies, has exposed some of pip’s performance bottlenecks. Resolving complex dependency trees can be time-consuming, and the installation process, while generally reliable, can sometimes feel sluggish.

Another challenge with the complexity of modern applications is package versioning. Lockfiles that pin project dependencies have become table stakes for package management. Many package management tools use them. Throughout the course of the evolution of package management in Python, we’ve seen managers such as Poetry and Pipenv, just to name a few. However, many of these projects don’t have dedicated teams. Sometimes this results in them not being able to keep up with the latest standards or the complex dependency trees of modern apps.

This is where the new generation of package management tools, like uv, comes into play, promising to address these very challenges, with a dedicated team behind them.

Enter the speed demon: The benefits of using uv

uv isn’t just another package manager; it’s built with a focus on speed and efficiency, leveraging modern programming languages and data structures to deliver a significantly faster experience. Here are some key benefits that have me, and many other Python developers, excited:

1. Blazing Fast Installation: This is arguably uv’s headline feature. Written in Rust from scratch using a thoughtful design approach uv significantly outperforms pip in resolving and installing dependencies, especially for large and complex projects. The difference can be dramatic, cutting down installation times from minutes to seconds in some cases. This speed boost translates directly into increased developer productivity and faster CI/CD pipelines.
2. Efficient Dependency Resolution: uv employs sophisticated algorithms for dependency resolution, aiming to find compatible package versions quickly and efficiently. While pip has made improvements in this area, uv’s underlying architecture allows it to handle complex dependency graphs with remarkable speed. This reduces the likelihood of dependency conflicts and streamlines the environment setup process.
3. Drop-in Replacement for pip and venv: One of the most appealing aspects of uv is its ambition to be a seamless replacement for both pip and venv (Python’s built-in virtual environment tool). It aims to handle package installation and virtual environment creation with a unified command-line interface. This simplifies project setup and management, reducing the cognitive load of juggling multiple tools.
4. Compatibility with Existing Standards: uv adheres to existing Python packaging standards like pyproject.toml (PEP 621). This means that projects already using these standards can easily adopt uv without significant modifications. It reads and respects your existing pyproject.toml files, making the transition relatively smooth. uv is built with a strong emphasis on modern packaging practices, encouraging the adoption of pyproject.toml for declaring project dependencies and build system requirements. This aligns with the direction the Python packaging ecosystem is heading.
5. Improved Error Messaging: While pip’s error messages have improved over time, uv, being a newer tool, has the opportunity to provide more informative and user-friendly error messages, making debugging dependency issues easier.
6. Potential for Future Enhancements: As a relatively new project with a dedicated development team, uv has the potential to introduce further optimizations and features that could significantly enhance the Python development experience. The active development and growing community support are promising signs.

How to use uv with Heroku

Now, let’s put some of this into practice. Imagine we’re building a simple Python web application (using Flask, for instance) that we want to deploy to Heroku, and we want to leverage the speed and efficiency of uv in our development and deployment process.

Here’s how we can set up our project:

1. Install uv

There are a variety of options to install uv, depending on your operating system. For a full list, take a look at the official Installation Guide site. I’m going to install it using Homebrew:

~/user$ brew install uv

2. Create the project directory and initialize uv

~/user$ uv init my-app
~/user$ cd my-app
~/user/my-app$ ls -a

In doing that, uv generates several project files

my-app/
├── main.py
├── pyproject.toml
├── README.md
└── .python-version

Our main.py looks like this:

def main():
    print("Hello from my-app!")

if __name__ == "__main__":
    main()

We can run this with the uv run main.py command which does a few things for us. In addition to actually running main.py and generating the “Hello from my-app!” output, uv also generates a virtual environment for the project and generates a uv.lock file which describes the project. More on that in a bit.

3. Expanding the project… slightly.

Let’s take this project a bit further and turn it into a Flask app that we can deploy to Heroku. We’ll need to specify our dependencies, Flask and Gunicorn for this example. We can do this using pyproject.toml.

Using pyproject.toml:

The uv generated pyproject.toml file looks like this:

[project]
name = "my-app"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires_python =">=3.13"
dependencies = []

To add dependencies we use the uv add command.

~/user/my-app$ uv add Flask
~/user/my-app$ uv add gunicorn

This accomplishes a couple of things:

First, it adds those packages to the pyproject.toml file:

[project]
name = "my-app"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires_python =">=3.13"
dependencies = [
    "Flask>=3.1.1",
    "gunicorn>=23.0.0",
]

Second, it updates the uv.lock file for dependency management.

4. Updating main.py

Let’s update the code in main.py to be a basic Flask web application

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello_world():
    return "Hello from uv on Heroku!"

if __name__ == '__main__':
    app.run(debug=True)

5. Preparing for Heroku deployment:

Heroku needs to know how to run your application. For a Flask application, we typically use Gunicorn as a production WSGI server. We’ve already included it in our dependencies.

We’ll need a Procfile in the root of our project to tell Heroku how to start our application:

web: gunicorn main:app

Here, app refers to the name of our Flask application instance in main.py.

6. Deploying to Heroku:

Now, assuming you are in the project working directory, have the Heroku CLI installed, and have logged in, you can create a local git repository and Heroku application:

~/user/my-app$ git init
~/user/my-app$ heroku create python-uv  # Replace python-uv with your desired app name
~/user/my-app$ git add .
~/user/my-app$ git commit -m "Initial commit with uv setup"

The Heroku CLI will create a remote in your git repository, but you check to make sure it’s there before you and push your code

~/user/my-app$ git remote -v
heroku https://git.heroku.com/python-uv.git (fetch)
heroku https://git.heroku.com/python-uv.git (push)
~/user/my-app$ git push heroku main

Heroku will detect your Python application, install the dependencies (based on .python-version, uv.lock and pyproject.toml), and run your application using the command specified in the Procfile.

The future is bright (and fast!)

We’re excited to announce that Heroku now natively supports uv for your Python development. By combining uv’s performance with Heroku’s fully managed runtime, teams can ship faster with greater confidence in their environment consistency. This reduces onboarding time, eliminates flaky builds, and improves pipeline performance.

While uv is still relatively new, its potential to significantly improve the Python development workflow is undeniable. The focus on speed, efficiency, and modern packaging standards addresses some of the long-standing frustrations with existing tools.

As the project matures and gains wider adoption, we can expect even more features and tighter integration with other parts of the Python ecosystem. For now, even the significant speed improvements in local development are a compelling reason for Python developers to start exploring uv.

The journey of Python package management has been one of continuous improvement, and uv represents an exciting step forward. If you’re a Python developer looking to boost your productivity and streamline your environment management, I highly recommend giving uv a try. You might just find your new favorite package manager!

Try uv out on Heroku

Whether you’re modernizing legacy apps or spinning up new services, uv gives you the speed and flexibility you need—now with first-class support on Heroku. Get started with uv on Heroku today.