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I Switched to uv's Build Backend. Here's What Happened.

Tired of slow Python dependency installs? I switched from pip and venv to uv, the blazingly fast Rust-based tool. Here's why I'm never going back.

A

Alex Carter

Senior Python Developer and DevOps enthusiast focused on building efficient, scalable systems.

6 min read18 views

I Switched to uv, and I'm Never Going Back to pip + venv

Let's be honest. We've all been there. You clone a new Python project, create a virtual environment, and run pip install -r requirements.txt. Then you wait. And wait. You grab a coffee, check your emails, and come back to see it's still resolving dependencies. This slow, clunky dance between pip, venv, pip-tools, and other utilities has been a part of Python development for years. We accepted it as the cost of doing business.

Then, uv came along. From the brilliant minds at Astral, the same team that gave us the indispensable linter Ruff, uv promises to be an "extremely fast Python package installer and resolver, written in Rust."

I was skeptical at first. Another tool? Another thing to learn? But the claims of being 10-100x faster were too tempting to ignore. I decided to give it a shot for a week. Now, that week is over, and I can say with confidence: I've completely switched my workflow to uv, and I'm never looking back. Here’s why.

So, What Exactly is uv?

Think of uv as a supercharged, all-in-one replacement for the most common commands you use with pip and venv. It's a single, static binary that handles:

  • Creating and managing virtual environments (like venv or virtualenv)
  • Installing and removing packages (like pip)
  • Resolving and locking dependencies (like pip-compile from pip-tools)

Because it's written in Rust, it leverages modern programming techniques like parallelism and efficient memory management to do these tasks at a mind-boggling speed. It also introduces a global package cache, which means once you've downloaded a specific version of a package, you'll never have to download it again for any other project on your machine.

The Pain Points That Pushed Me to Switch

My old workflow wasn't terrible, but it was full of minor frictions that added up. The primary driver for my switch was speed, especially in a few key areas:

  1. Local Development: Setting up a new project or switching branches and re-installing dependencies was always a momentum-killer. A 60-second wait can completely break your flow state.
  2. CI/CD Pipelines: In our continuous integration environment, installing dependencies was consistently one of the longest steps. Shaving even 2-3 minutes off every single build saves a significant amount of time and money over a month.
  3. Tool Sprawl: My toolchain involved python3 -m venv, pip, and pip-tools. Three separate tools to manage one core part of my environment. It worked, but it always felt a bit disjointed.

The promise of a single, lightning-fast tool that could solve all three issues was the push I needed.

Getting Started: Easier Than You Think

Astral has made installation incredibly simple. On macOS and Linux, it's a single command:

curl -LsSf https://astral.sh/uv/install.sh | sh

For Windows users and those who prefer other package managers, there are instructions for Scoop, Pipx, and Homebrew. I used Pipx:

pipx install uv

And that’s it. You're ready to go. You can confirm it's installed by running:

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uv --version

My New Workflow: uv vs. pip + venv

This is where the magic happens. Here’s a side-by-side of my old commands and their new, faster uv equivalents.

1. Creating a Virtual Environment

The Old Way:

python -m venv .venv
source .venv/bin/activate

The New `uv` Way:

uv venv

That's it. uv creates the .venv folder and provides the activation command. It feels cleaner and is noticeably faster at creating the environment structure.

2. Installing Packages

This is the main event. Let's say we're installing a few common packages for a web project.

The Old Way:

pip install "django>=4.0" djangorestframework "redis<5.0"

This command would trigger a download and a potentially slow resolution process. On a fresh environment, this took my machine about 28 seconds.

The New `uv` Way:

uv pip install "django>=4.0" djangorestframework "redis<5.0"

The first time I ran this, it took about 3.5 seconds. That's an 8x speedup. But here's the real kicker: because uv uses a global cache, the second time I created a new environment and installed the same packages, it took just 0.8 seconds. It felt instantaneous.

3. Managing Requirements Files

I use pip-tools to pin my dependencies for reproducible builds. This involves a requirements.in for top-level dependencies and a requirements.txt for the fully resolved, pinned versions.

The Old Way (with pip-tools):

# Compile dependencies
pip-compile requirements.in -o requirements.txt

# Install from the lockfile
pip install -r requirements.txt

The New `uv` Way:

# Compile dependencies
uv pip compile requirements.in -o requirements.txt

# Install from the lockfile
uv pip install -r requirements.txt

The commands are identical! uv provides a drop-in replacement for the pip-compile workflow. The difference? Compiling a complex set of dependencies went from ~15 seconds to ~1 second. Installing from the massive lockfile saw similar speed improvements.

The 'Wow' Moments That Sealed the Deal

Beyond the raw speed, a few other features made me a true convert:

  • The Global Cache: I can't overstate how much of a game-changer this is. Jumping between projects and knowing that 90% of your dependencies will be installed from a local cache in milliseconds is pure bliss. You can inspect it yourself with uv cache dir.
  • Clearer Error Messages: When a dependency conflict arises, uv often provides a much clearer, more readable explanation of the dependency tree and the source of the conflict. This saves precious debugging time.
  • A Single Binary: Having one tool, uv, that does everything is just... elegant. No more wondering if I need pip, venv, or pip-tools. The answer is just uv.

Are There Any Downsides?

No tool is perfect, especially a new one. While my experience has been overwhelmingly positive, it's worth noting a few things:

  • It's New: While uv boasts high compatibility with pip, there might be rare, esoteric edge cases with complex packages or private indexes that haven't been fully ironed out. The team is incredibly responsive, but it's still a young project.
  • Doesn't Manage Python Versions: uv is not a replacement for tools like pyenv. You still need a way to manage the base Python interpreter you're using.
  • Workflow Change: While it can be a drop-in replacement, to get the most out of it, you do have to consciously change your habits. This is a small hurdle, but a hurdle nonetheless.

Final Verdict: The Switch is 100% Worth It

Switching to uv has been one of the most significant quality-of-life improvements in my Python development workflow in years. The speed is not just a novelty; it fundamentally changes how you interact with your projects. It removes friction, keeps you in a state of flow, and speeds up critical infrastructure like CI/CD pipelines.

If you're a Python developer, you owe it to yourself to spend 10 minutes installing uv and trying it on your next project. The time you save on that first uv pip install will likely be more than the time you spent setting it up.

I'm sold. The combination of pip and venv has served us well, but for me, the future of Python package management is undeniably uv.

Tags

#uv#python#pip#performance#developer tools

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