The Python Package Manager That's Making pip Obsolete (And Why Everyone's Switching)

For most of my Python development, I’ve been sticking to the usual workflow with pip installing packages, setting up virtual environments, and waiting… a lot. It’s reliable, yes, but ughhh… not exactly exciting. A while back, I came across a TikTok video by Uma Abu, a software engineer at Netflix introducing UV, a blazing-fast Python tool. At the time, I watched and saved the video but didn’t think much of it.

I only had to revisit it when I found myself frustrated with slow installs and repetitive setup tasks on a new project. That’s when I realized UV wasn’t just hype, it was a genuine game-changer.

👉🏾Checkout the docs: UV

Introduction to UV

UV is a modern toolchain manager designed to simplify the installation and management of command-line developer tools, particularly in the Python ecosystem. It allows developers to install utilities like linters, formatters, and other productivity tools in isolated environments, keeping them separate from the system Python or project dependencies.

With UV, you can quickly add, remove, or update tools without worrying about version conflicts or global pollution. It also provides a consistent way to run these tools across different projects, making development more streamlined and reproducible. Essentially, UV acts as a lightweight package manager and environment manager for your developer tools, making it easier to maintain a clean and efficient workflow.

For example, installing the Python linter Ruff using UV ensures that it is ready to use across projects without affecting other Python packages or requiring complicated virtual environment setups.

Key Features

1. It’s unbelievably fast

This is the first thing you notice when using UV. UV is written in Rust, not Python, which makes a huge difference. Package installs that take minutes with pip often finish in seconds with UV. In real-world use, it’s commonly 10–100× faster than pip, especially on larger projects.

Even better: once a package is downloaded once, UV reuses it across all your projects. That means new projects install almost instantly instead of re-downloading the same dependencies over and over again.

2. One tool instead of five

With the traditional Python setup, you usually need:

pip for installing packages
venv for environments
pip-tools or something similar for lockfiles
Extra scripts to run tools consistently

UV replaces all of that.

It handles:

Dependency installation
Dependency resolution
Virtual environments
Lockfiles
Running tools and scripts

All from a single CLI. Less setup, fewer things to break.

3. Built-in environment management

With pip, you’re responsible for creating and activating virtual environments correctly. Forget to activate one and suddenly packages are installed globally… we’ve all been there.

UV manages environments for you automatically. You don’t need to think about venv commands or activation anymore. Each project stays isolated by default, which keeps your system Python clean and avoids dependency conflicts.

4. Reliable dependency resolution and lockfiles

Dependency issues are one of the most frustrating parts of Python development.

UV comes with:

A fast and stable dependency resolver
Built-in lockfiles (uv.lock)

This means everyone on your team installs the exact same versions, every time. No “it works on my machine” problems. I’ve been that person who confidently said “it’s working for me,” only to later realize my environment was quietly doing all the heavy lifting.

5. Smarter disk usage

Here’s a small detail that actually matters a lot.

If three projects use the same dependency:

pip installs it three separate times
UV installs it once and shares it safely

The result is:

Smaller environments
Less disk usage
Faster installs for every new project

Over time, this adds up… especially if you work on many projects.

UV isn’t just about speed (though the speed is hard to ignore). It’s about reducing friction.

It:

Simplifies the Python toolchain
Makes installs faster and environments smaller
Improves reproducibility across machines and teams
Removes a lot of repetitive setup work

Once you start using it, going back to pip + venv feels unnecessarily complicated.

UV Project Structure

pyproject.toml: The main configuration file for the project. It defines your dependencies, project metadata, and tool settings, acting as the single source of truth for how the project is set up.
uv.lock: The lockfile generated by UV that records the exact versions of all installed dependencies, ensuring the project installs the same way on every machine.
.venv/: The virtual environment automatically created and managed by UV. You don’t need to activate it manually, and it keeps project dependencies isolated from your system Python.
.python-version: Specifies the Python version the project uses. This helps ensure consistency across different machines and works well with tools like pyenv.
.gitignore: Lists files and directories Git should ignore, such as virtual environments, cache files, and other generated artifacts that don’t belong in version control.
What you don’t need anymore: Manual virtual environment setup, pip freeze, and additional tools for dependency management.

A Quick Hands-On Example (pip vs UV)

Let’s say you’re starting a new Python project and want to install requests and ruff.

Using pip (the traditional way)

# Create a virtual environment
python -m venv .venv

# Activate it
source .venv/bin/activate  # macOS / Linux
# .venv\Scripts\activate   # Windows

# Install dependencies
pip install requests ruff

# Freeze dependencies
pip freeze > requirements.txt

That’s already a few steps, and you have to remember to activate the environment every time.

Using UV

# Initialize the project
uv init

# Add dependencies
uv add requests ruff

That’s it.

UV:

Creates and manages the virtual environment automatically
Installs dependencies at blazing speed
Generates a lockfile (uv.lock) for reproducibility
Reuses cached packages instead of reinstalling them

No manual activation. No extra tools. No boilerplate.Running tools

With pip:

ruff .

(Only works if the environment is activated.)

With UV:

uv run ruff .

Works every time, regardless of shell state.

So is UV replacing pip?

Not officially…. but my best guess? in practice, for many developers, yes.

UV is a drop-in replacement for pip and works with the existing Python ecosystem. You don’t need to rewrite your projects or learn an entirely new system. You just get a better experience doing the same things you already do. That’s why so many teams are quietly switching.

I didn’t switch to UV because it was trendy. I switched because I was tired of slow installs, broken environments, and boilerplate setup. It doesn’t reinvent Python, it fixes the parts that have been painful for years. If you work on Python projects regularly, it’s worth trying at least once. Chances are, you won’t go back.

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