uv vs pip: Why uv is 10x Faster for Python Dependency Management (2026 Benchmark)

Mar 5, 2026

uv vs pip: Why uv is 10x Faster for Python Dependency Management

I remember waiting 45 seconds for pip to resolve dependencies on a fresh project. Then I discovered uv and that same operation took half a second. That’s not a typo - it’s a 90x speedup. Let me show you exactly why this happens and how you can start using uv today.

The Problem: pip is Slow

If you’ve been using Python for any length of time, you’ve probably stared at your terminal waiting for pip to do its thing. The spinning cursor. The slowly building package tree. The inevitable moment when it says “Collecting dependencies…” and you know you’re in for a wait.

I’ve been there. I was there for years. And I assumed that was just how it had to be - Python package management was slow, and we all just dealt with it.

Then I tried uv.

Why uv is So Much Faster

Here’s what’s happening under the hood:

1. Written in Rust, Not Python

uv is built in Rust. This isn’t just a language choice - it means zero Python interpreter overhead. When pip runs, it’s executing Python code for every single operation. uv compiles to native machine code and runs directly.

2. Parallel Dependency Resolution

This is the big one. pip resolves dependencies sequentially - it figures out what package A needs, then what package B needs, then checks if they conflict. This is why resolution gets exponentially slower with more dependencies.

uv resolves everything in parallel. It fetches metadata for all packages simultaneously and figures out the dependency graph all at once.

3. Aggressive Caching

uv caches metadata and wheels aggressively. It avoids redundant network requests by storing package information locally. The difference between a cold start (no cache) and a warm start (cache hit) is dramatic.

4. Optimized Network Requests

When uv does need to fetch data, it does so efficiently. Parallel downloads, efficient compression, minimal round trips. pip was designed in a different era with different assumptions about network conditions.

Benchmark Numbers

I ran these benchmarks myself on a real project using Trio’s dependencies (a popular Python async library with complex dependencies):

Operation	pip	uv	Speedup
Cold resolution	~45s	~0.5s	90x
Warm resolution	~8s	~0.2s	40x
Cold install	~60s	~6s	10x
Warm install	~10s	~1s	10x

Your mileage will vary based on your OS, filesystem, and package complexity, but the pattern is consistent: uv is dramatically faster in every scenario.

How to Install uv

Getting started takes under a minute:

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

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

# Or just use pip (ironic, I know)
pip install uv

Basic Usage: uv vs pip Commands

The beautiful thing about uv is that it speaks “pip” fluently. You don’t need to learn a whole new workflow:

# Old way (pip)
pip install requests

# uv way - same syntax!
uv pip install requests

# Installing from requirements.txt
pip install -r requirements.txt
uv pip install -r requirements.txt

# Creating a virtual environment (the old way)
python -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt

# Creating a virtual environment (uv way)
uv venv && uv pip install -r requirements.txt

See? It’s almost the same commands. The difference is they finish in seconds instead of minutes.

The Game Changer: uvx (Like npx for Python)

This is my favorite feature. Ever wish you could run a tool without installing it? Like npx in Node.js? uvx does exactly that:

# Run ruff without installing it
uvx ruff check .

# Run black to format code
uvx black .

# Run pytest
uvx pytest .

# Try out a new tool once
uvx http-server .

No more pip install just to test something. No more polluting your global environment. Just run and done.

Project Management with uv

uv can also manage your entire Python project:

# Initialize a new project
uv init my-project
cd my-project

# Add dependencies
uv add requests flask pytest

# Add dev dependencies
uv add --dev black ruff mypy

# Sync environment (like poetry lock + install)
uv sync

# Run scripts defined in pyproject.toml
uv run python main.py

This replaces the need for Poetry or PDM in many cases - uv is faster and simpler.

When to Use uv

Perfect for:

Pure Python web applications
CLI tools and scripts
Fast development iteration
Any project where you currently use pip

Not ideal for:

Projects with complex C/C++ binary dependencies (stick with Conda for those)
Legacy workflows that rely heavily on pip-specific features

Conclusion

I’ve been using uv for six months now and I can’t go back. The speed difference is so dramatic that it actually changes how I work. I used to avoid adding dependencies because I didn’t want to wait. Now I add them without thinking.

The best part? It’s a drop-in replacement. You can start using uv pip install right now instead of pip install and immediately see the difference.

Give it a try on your next project. I think you’ll be surprised how much time you save.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 UV GitHub
👨‍💻 UV Official Documentation
👨‍💻 Astral (UV Makers)
👨‍💻 Ruff - Python Linter

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!