I've Been Testing uv vs pip vs Poetry - Here's What Actually Happens

I was skeptical as fuck about uv at first. Another Rust tool claiming to "fix" Python? Sounded like bullshit. But then I tried installing the usual Django crap and pip took forever like always while uv was already done. I literally thought it failed at first because it finished so fast.

uv is fast because it's written in Rust and downloads packages in parallel instead of pip's stupid one-at-a-time approach from 2008. The official benchmarks show it's 8-10x faster than pip without caching.

Here's what actually happened when I ran uv pip install django==4.2.7 psycopg2-binary==2.9.7 pillow==10.0.1:

Resolved 8 packages in 47ms
Downloaded 8 packages in 1.8s
Installed 8 packages in 92ms
  + asgiref==3.7.2
  + django==4.2.7  
  + pillow==10.0.1
  + psycopg2-binary==2.9.7
  + pytz==2023.3
  + sqlparse==0.4.4
  + typing-extensions==4.8.0

Compare that to pip's usual horseshit (timed this on my M1 MacBook):

Collecting django==4.2.7
  Downloading Django-4.2.7-py3-none-any.whl (8.0 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.0/8.0 MB 2.1 MB/s eta 0:00:00
[waits 8 more seconds doing absolutely nothing]
Collecting psycopg2-binary==2.9.7
  Downloading psycopg2_binary-2.9.7-cp311-cp311-macosx_11_0_arm64.whl (2.1 MB)
[another 6 second pause because pip is single-threaded garbage]

uv downloads everything in parallel because it's built in Rust. pip downloads one package at a time like it's still 2008.

Memory Usage and Caching

uv uses less RAM than pip - maybe 50-60MB vs pip's 100+ MB from what I've seen. The global cache is smarter too. When I install the same packages across different projects, uv reuses cached wheels instead of downloading everything again.

Unlike pip's limited caching, uv's global cache works across virtual environments and projects. This means faster installs for Docker builds, CI/CD pipelines, and local development. Check out the Real Python tutorial for more cache optimization strategies.

When Dependency Resolution Actually Works

Here's where pip drives me insane. Try this with pip:

pip install tensorflow==2.13.0 scikit-learn==1.3.0

You'll get some variation of:

ERROR: pip's dependency resolver does not currently take into account 
all the packages that are installed. This behaviour is the source of 
the following dependency conflicts.
tensorflow 2.13.0 requires numpy<=1.24.3,>=1.21.2
scikit-learn 1.3.0 requires numpy>=1.17.3

Then pip just... installs whatever numpy version it feels like. Your environment is broken but pip doesn't care.

uv actually resolves this shit properly:

uv pip install tensorflow==2.13.0 scikit-learn==1.3.0
Resolved 15 packages in 12ms
  ✓ Found compatible numpy==1.24.3

Same with Poetry, but uv doesn't take 30 seconds thinking about it.

Where uv Actually Saves Time

Our CI was taking 6 minutes per build, and 4 of those were just installing the same 73 dependencies over and over. Switched our GitHub Actions from pip install -r requirements.txt to uv pip install -r requirements.txt and installs dropped to 45 seconds. We run this pipeline 50+ times daily, so that's 4+ hours saved. Check the uv CI guide for setup instructions.

Docker builds got faster too. uv's single binary approach means you don't need Python installed to install Python packages:

FROM alpine:3.18
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
COPY requirements.txt .
RUN /root/.local/bin/uv pip install -r requirements.txt

No more waiting for Python to bootstrap or dealing with Alpine's weird package manager.

When uv Breaks (And It Does)

uv isn't magic. I've hit these specific failures:

Packages with weird C extensions: mysqlclient==2.2.0 completely shat the bed with some 200-line Rust panic about malloc failures. Spent 2 hours going down rabbit holes on GitHub issues before saying fuck it and using pip. Check the compatibility guide for known package issues.

Corporate proxy bullshit: Our BlueCoat proxy (10.0.0.1:8080) killed uv's parallel downloads with ConnectTimeout after exactly 30 seconds. Had to set UV_HTTP_TIMEOUT=300 and UV_CONCURRENT_DOWNLOADS=1 to make it work like pip.

ARM Mac weirdness: Installing psycopg2-binary==2.9.7 on Apple M1 threw "no matching distribution found" even though the wheel exists on PyPI. pip grabbed psycopg2_binary-2.9.7-cp311-cp311-macosx_11_0_arm64.whl just fine. Still makes no fucking sense.

The error messages are complete trash compared to pip. When uv breaks, you get a Rust panic dump that tells you nothing useful. When pip breaks, at least it fails in English and tells you which package fucked up.

After dealing with all three of these for months, here's what actually works in practice.

uv for Everything That Needs to Be Fast

Data science work with uv fucking changed my workflow. Installing the ML stack (pandas==2.1.2, numpy==1.25.2, scikit-learn==1.3.0, matplotlib==3.8.0, jupyter==1.0.0) took pip 67 seconds - enough time to get coffee and check Slack. uv finished in 4.8 seconds. Check the DataCamp tutorial for ML-specific workflows. When you're spinning up new notebook environments 5-6 times daily because Jupyter keeps breaking, that difference is massive.

The dependency resolver actually works. Had some nightmare with TensorFlow and PyTorch versions fighting over CUDA. pip installed whatever the hell it wanted, everything broke. uv actually warned me about the conflict instead of silently fucking up my environment.

Docker builds are where uv really shines. Copy the binary, install packages, done. No Python bootstrap, no Alpine package manager bullshit.

Poetry Still Wins for Library Publishing

Poetry is still the best tool for publishing packages to PyPI. The whole build and publish workflow just works:

poetry init
poetry add requests
poetry build
poetry publish

I've published 3-4 packages with Poetry and it just works. The pyproject.toml setup is clean, dependency groups make sense, and version management doesn't make me want to die. Check the Poetry publishing guide for PyPI workflows.

uv's project management is getting better but it's not there yet. When you need to publish libraries, stick with Poetry.

pip When You Have No Choice

pip is getting some new features in upcoming releases - dependency groups and resumable downloads. Check the pip documentation for latest updates. Still gonna be slow as shit though.

Use pip when:

• Corporate security won't let you install anything new
• You're dealing with ancient Python versions
• Legacy systems that break if you change anything

pip is predictable, which matters more than speed sometimes. But if you have a choice, why suffer?

What Actually Breaks in Practice

uv fails on:

• Packages with custom C extension builds (mysqlclient==2.2.0 gives malloc panics, scipy==1.11.3 compilation fails on Alpine with "fatal error: openblas_config.h: No such file")
• Corporate proxy setups (BlueCoat, Zscaler hate parallel downloads and randomly timeout)
• Really old packages that assume pip's weird behavior (anything pre-2018, especially scientific stuff)

Poetry breaks when:

• You manually fuck with the .venv folder (delete it and Poetry can't find the interpreter, throws "The currently activated Python version 3.11.6 is not supported"). Check Poetry's virtual environment docs for fixes.
• Complex dependency resolution on slow machines (spent 4 minutes resolving pandas + sklearn + tensorflow deps, gave up and used requirements.txt). The Poetry GitHub issues have performance workarounds.
• Legacy packages with fucked metadata (anything using setup.py without pyproject.toml, especially ancient scientific packages). The Python Packaging User Guide explains modern standards.

pip breaks by:

• Installing incompatible versions without warning (tensorflow==2.12.0 + numpy==1.25.0 = segfaults)
• Slow dependency resolution that times out (set --timeout=300 or it gives up after 15 seconds)
• Cryptic error messages that don't help ("Failed building wheel for X" tells you nothing useful)

What I Actually Do

My current setup:

• Local development: uv first, pip when it inevitably breaks on something weird
• Data science: uv always (speed is worth dealing with the occasional failure)
• CI/CD: uv in dev, still using pip in prod until I'm more confident
• Publishing packages: Poetry (uv's not ready for this yet)
• Work stuff: pip because corporate security won't approve anything new

Switching Is Pretty Easy

Moving from pip to uv is usually just changing pip install to uv pip install. Most requirements.txt files work fine.

Moving from Poetry to uv means rewriting your entire project setup. Only worth it if you're really tired of waiting for Poetry's resolver.