uv vs pip vs Poetry: Which Python Package Manager Wins in 2026?

TL;DR

uv is 10–100x faster than pip and roughly 10x faster than Poetry on every benchmark that matters: cold installs, lock file resolution, virtual environment creation. After OpenAI acquired Astral in March 2026, uv also has the strongest corporate backing of any Python packaging tool. If you’re starting a new project today, use uv. If you’re publishing a library and your team already knows Poetry, it still works fine. pip is the fallback you’ll never fully escape, and that’s okay.

Three Package Managers Walk Into a Repo

Python packaging has been a mess for decades. Every few years, a new tool promises to fix it. Most of them don’t. But the current generation — uv, pip, and Poetry — actually covers three distinct philosophies, and picking the right one in 2026 depends less on benchmarks and more on what you’re building.

pip is the default. It ships with Python. It does one thing: install packages. It doesn’t manage environments, create lockfiles, or organize workspaces. Since 2008, that’s been enough.

Poetry arrived in 2018 with Cargo-like ambitions for Python. It brought lockfiles, dependency groups, virtual environment management, and library publishing under one roof. By 2023, it was the go-to for serious Python projects.

uv showed up in February 2024, written in Rust by the Astral team (the Ruff people). It started as a pip replacement, then grew into a full project manager. In March 2026, OpenAI announced it would acquire Astral and committed to keeping uv open source (it’s dual-licensed MIT/Apache 2.0). The deal hasn’t closed yet, but development continues at full speed.

The Benchmarks

I ran each tool against three scenarios: a fresh install from a lockfile (cold cache), dependency resolution for a project with 47 dependencies, and virtual environment creation. All tests on an M3 MacBook Pro, Python 3.14, averaged over five runs.

The numbers aren’t subtle. uv finishes installing before Poetry finishes resolving. In CI, the gap is even bigger because uv’s caching strategy is designed for ephemeral runners. It downloads, resolves, and installs in parallel using all available cores.

Poetry’s resolver has gotten better since 2.0, but it’s still single-threaded for the resolution step. pip-tools (pip-compile) works but feels like duct tape on a tool that was never designed for lockfiles.

Feature Comparison

Speed is one axis. Features are the other.

A few things in this table deserve more context:

uv’s universal lockfile is cross-platform by default. One uv.lock works on macOS, Linux, and Windows across Python versions. Poetry’s lockfile is also cross-platform, but uv’s format is resolution-complete — it includes every platform variant so you never re-resolve on a different OS.

Python version management is built into uv. Run uv python install 3.14 and it downloads the right build. No pyenv, no system Python conflicts. On fresh CI runners, this can shave minutes off your setup step.

Library publishing used to be Poetry’s clear advantage, but uv caught up. Since version 0.4, uv has native uv build and uv publish commands that handle building and uploading to PyPI. Poetry’s poetry publish still works great, but this is no longer a differentiator.

The OpenAI Acquisition Question

On March 19, 2026, OpenAI announced it would acquire Astral — the company behind uv, Ruff, and ty. After the deal closes, the Astral team will join OpenAI’s Codex group.

The big question is long-term trust. uv is dual-licensed under MIT and Apache 2.0, and Ruff is MIT. Neither can be pulled back. But corporate priorities shift. OpenAI says it’ll continue supporting Astral’s open source tools, and so far nothing has changed. uv 0.11.6 shipped on April 9 with regular updates.

My read: the acquisition is a net positive in the short term (more funding, more engineers) and a question mark long-term. If OpenAI deprioritizes uv in favor of Codex-specific tooling, the community will fork. MIT makes that easy. For now, I’m still betting on uv for new projects.

Migration: From pip to uv

Migrating from pip to uv takes about five minutes. uv has a uv pip interface that’s a drop-in replacement:

# Before
pip install -r requirements.txt

# After
uv pip install -r requirements.txt

Same flags, same behavior, 10–100x faster. Your requirements.txt files work unchanged. If you want to graduate to full project management:

# Initialize a uv project from existing requirements
uv init
uv add $(cat requirements.txt | grep -v "^#" | grep -v "^$")
uv lock

This generates a pyproject.toml and uv.lock. Your CI goes from this:

# Old GitHub Actions
- uses: actions/setup-python@v5
  with:
    python-version: "3.14"
- run: pip install -r requirements.txt  # 2+ minutes

To this:

# New GitHub Actions
- uses: astral-sh/setup-uv@v8
- run: uv sync  # 8 seconds

Migration: From Poetry to uv

More work, but not bad. The migrate-to-uv tool automates most of it:

uvx migrate-to-uv

This converts your pyproject.toml from Poetry’s format to PEP 621, generates a uv.lock, and updates your dependency specifcations. Manual steps after:

1. Remove the [tool.poetry] section from pyproject.toml
2. Delete poetry.lock
3. Update CI scripts to use uv sync instead of poetry install
4. Update Dockerfiles if applicable

In my experience, expect a few hours for a medium-sized project. The main friction points are Poetry plugins (which have no uv equivalent) and custom publish scripts (which need to switch to build + twine).

When to Use Each

Here’s my decision tree after managing Python projects with all three tools:

Use uv if:

• You’re starting a new project (any size)
• CI speed matters to your team
• You manage multiple Python versions
• You want workspaces for monorepos
• You’re tired of managing pyenv + pip + virtualenv + pip-tools as separate tools

Use Poetry if:

• You publish libraries to PyPI and want one-command publishing
• Your team is already productive with Poetry and has no CI bottleneck
• You rely on Poetry plugins that have no uv equivalent

Use pip if:

• You’re writing a one-off script
• You’re teaching Python to beginners (pip is what every tutorial uses)
• You’re working in a constrained environment where you can’t install extra tools

The “use pip” bucket keeps shrinking. uv can run single-file scripts with inline dependencies (uv run script.py), which eliminates the last case where pip-only was simpler.

Gotchas and Sharp Edges

uv is still at 0.11.x, which means the API can change between minor versions. In practice, the core commands (uv sync, uv add, uv run) have been stable since 0.5, but if you’re writing CI scripts, pin your uv version.

On projects with 200+ dependencies, Poetry’s resolver can consume several gigabytes of RAM. This occasionally kills CI runners with limited memory. uv’s Rust resolver uses a fraction of that.

pip recently added an experimental pip lock command (since 26.0.1) that generates pylock.toml files per PEP 751. But it’s platform-specific — you get a lockfile for the OS you ran it on, not a universal one. Before that, you needed pip-compile from pip-tools, which most teams still use.

One more thing worth knowing: uv ships as a standalone binary. It doesn’t need Python installed to run. This means you can install uv on a bare CI runner, have it download Python, create a venv, and install your dependencies. Poetry and pip both require Python to already be installed.

What’s Coming Next

uv’s roadmap focuses on deeper IDE integration and tighter hooks with OpenAI’s Codex for AI-assisted dependency management, though no timeline exists for the Codex integration.

The Poetry team continues work on resolver performance improvements. Speed is their biggest acknowledged gap, and they’ve discussed parallel resolution in GitHub issues, but nothing has shipped yet.

pip continues to get incremental updates. pip 26.0 added prerelease filtering and upload-time filtering, and 26.0.1 introduced the experimental pip lock command. pip’s scope is expanding slowly, but it’s still primarily a package installer.

FAQ

Can I use uv as a complete replacement for pip?

Yes. The uv pip command is a drop-in replacement that accepts all the same flags. For full project management, use uv init, uv add, and uv sync instead.

Is uv stable enough for production?

The core commands have been stable since version 0.5. Major companies run uv in production CI (it gets 126 million monthly downloads for a reason). Pin your uv version in CI scripts to avoid surprises.

Will Poetry be abandoned now that uv exists?

No. Poetry 2.3 shipped in early 2026 and development continues. Poetry has a different philosophy (integrated publishing, strict dependency groups) that still appeals to library authors.

Does the OpenAI acquisition mean uv will become closed source?

uv is MIT licensed. Even if OpenAI stopped development tomorrow, anyone could fork and continue the project. OpenAI has publicly committed to maintaining uv as open source.

Should I migrate existing Poetry projects to uv?

Only if CI speed or workspace support is a real pain point. Poetry works fine for established projects. If you’re starting new projects alongside legacy ones, use uv for the new ones and migrate the old ones when it makes sense. Don’t do a big-bang migration for its own sake.

Bottom Line

The Python packaging story in 2026 is the clearest it’s been in a decade. uv won the performance race so decisively that the comparison feels unfair. It also won the feature race for project management, with workspaces, Python version management, and a universal lockfile that Poetry and pip can’t match.

If you’re choosing a tool for a new Python project today, the answer is uv. Beyond the raw speed, the real draw is consolidation: one tool replaces pyenv, virtualenv, pip, pip-tools, and most of what Poetry does, all in a single binary that installs in under a second.

Most new Python projects in 2026 will start with uv init. And that’s about right.