If you've managed Python dependencies on a production project, you know the pain. For years, the ecosystem lacked a clear standard, and the result was a pile of competing tools: pip, virtualenv, pip-tools, pipenv, Poetry, conda, and more. Each solved some problems while creating new ones. Teams spent hours debugging version conflicts, fighting slow CI installs, and arguing about which tool to standardize on.
In 2026, that landscape has finally consolidated around a few clear options. The emergence of uv, a Rust-based package manager from Astral (the company behind Ruff), has fundamentally changed expectations around performance. Poetry remains a solid choice for teams that need battle-tested project lifecycle management. And pip-tools is still the reliable option for teams that want to stay close to pip while gaining reproducibility.
Here's how to navigate these choices and pick the right tool for your projects.
What You're Actually Solving For
Before diving into specific tools, it helps to understand the core problems. Python dependency management needs to handle several concerns: dependency resolution (figuring out which versions of packages can work together), reproducibility (ensuring the same dependencies install across environments), virtual environments (isolating project dependencies from each other), lock files (recording exact versions for deterministic builds), and separating development dependencies from production ones.
Different tools emphasize different aspects. Your choice depends on which concerns matter most for your workflow.
uv: Setting the New Standard
uv is the most significant change to Python tooling in years. Built in Rust by Astral, it aims to replace pip, pip-tools, pipx, pyenv, virtualenv, and more with a single, blazingly fast tool.
The performance claims are not exaggerated. uv installs packages 10 to 100 times faster than pip, depending on caching. What used to take 30 seconds now takes 300 milliseconds. In CI/CD pipelines where dependency installation happens on every build, this adds up to real time and cost savings.
If you want to see the numbers for yourself, the Python Package Manager Shootout provides continuously updated, automated benchmarks comparing uv, Poetry, pip-tools, pipenv, pdm, and pixi across real-world operations. The benchmarks use Sentry's dependency list as a non-trivial test corpus and run every six hours on GitHub Actions. The results are stark: for a cold install from a lock file, uv completes in about three seconds versus Poetry's roughly 11 seconds and pip-tools' roughly 33 seconds. Lock file generation shows an even wider gap, with uv at about eight seconds cold versus Poetry's 22 seconds and pip-tools' 35 seconds. And for adding a single package, uv is nearly instantaneous (under one second) while Poetry takes about three seconds and pip-tools about six.
The adoption numbers tell a similar story. uv now pulls roughly 75 million monthly downloads on PyPI, surpassing Poetry's approximately 66 million. The Wagtail CMS team documented this crossover point in their post "uv overtakes Poetry", noting that uv has also overtaken pip as the primary installer in CI environments. A caveat: PyPI download stats are heavily inflated by CI pipelines, so they reflect tooling momentum more than individual developer adoption. But the trend is clear.
# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create a new project
uv init my-project
cd my-project
# Add dependencies
uv add django requests
# Install from existing requirements.txt
uv pip install -r requirements.txt
# Run scripts in the managed environment
uv run python manage.py runserverWhat makes uv compelling beyond speed:
- Drop-in pip compatibility.
uv pip installworks exactly likepip install. Migration is trivial. - Python version management. uv can install and manage Python interpreters, replacing pyenv. Change the version in
.python-version, push to production, and uv installs the new Python version automatically. Something that takes minutes with pyenv takes seconds with uv. - Universal lock file. A single
uv.lockworks across platforms, unlike pip-tools which generates platform-specific output. - Single binary. No Python required to install uv. It's a standalone executable.
When to Use uv
- New projects where you want modern tooling
- CI/CD pipelines where install time matters
- Projects that need both dependency management and Python version management
- Teams migrating from pip or pip-tools who want a low-friction upgrade
Poetry: The Established Choice
Poetry takes a different approach. It's not just a package installer; it's a complete project management tool. It handles dependency resolution, virtual environments, building packages, and publishing to PyPI, all through a unified interface.
Poetry is still widely used, with roughly 66 million monthly downloads on PyPI, and actively maintained, with version 2.3.2 shipping in February 2026. It's not going anywhere. But the conversation around it has shifted. Where it was once the default recommendation for any new Python project, it's now the specific recommendation for teams that need its particular strengths.
# Create a new project
poetry new my-project
cd my-project
# Add dependencies
poetry add django
poetry add --group dev pytest black
# Install all dependencies
poetry install
# Run commands in the virtual environment
poetry run python manage.py runserver
# Build and publish
poetry build
poetry publishPoetry uses pyproject.toml as its configuration file, which has become the standard for Python project metadata. The poetry.lock file captures exact dependency versions for reproducible installs.
Key strengths:
- Dependency groups. Cleanly separate dev, test, and production dependencies. This is more sophisticated than uv's current approach. Poetry lets you define arbitrary groups (dev, test, docs, ci) and install them selectively with
poetry install --with dev. - Publishing workflow. Built-in support for building and publishing packages to PyPI. If you're maintaining a library, this is the smoothest path from code to distribution.
- Mature ecosystem. Years of production use, extensive documentation, and a large community.
The main tradeoff is speed. Poetry is significantly slower than uv for dependency installation and resolution. The Python Package Manager Shootout benchmarks quantify this: on Sentry's real-world dependency set, Poetry takes roughly three times longer than uv on cold installs and nearly three times longer on lock file generation. For small projects this won't matter. For large projects with many dependencies, the difference is noticeable, and in CI pipelines, it costs real money.
When to Use Poetry
- Libraries you'll publish to PyPI
- Projects that need structured, named dependency groups
- Teams that want mature, well-documented tooling
- Situations where publishing workflow matters more than install speed
pip-tools: The Reliable Workhorse
pip-tools takes a minimal approach. It adds exactly two capabilities to pip: pip-compile generates a pinned requirements.txt from a requirements.in file, and pip-sync ensures your environment matches the compiled requirements exactly.
At roughly 19 million monthly downloads, pip-tools has a smaller but loyal following, primarily teams with existing requirements.txt workflows who want reproducibility without changing their toolchain.
# requirements.in - your direct dependencies
django>=4.2
requests
celery# Compile to requirements.txt with all transitive dependencies pinned
pip-compile requirements.in
# Sync your environment to match
pip-sync requirements.txtThe generated requirements.txt includes every transitive dependency with exact versions and comments showing why each package is included:
# requirements.txt generated by pip-compile
#
amqp==5.2.0
# via kombu
asgiref==3.8.1
# via django
billiard==4.2.0
# via celery
celery==5.4.0
# via -r requirements.in
...pip-tools supports pyproject.toml as input, so you can use modern project configuration while keeping the familiar requirements.txt workflow.
Layered Requirements
pip-tools shines for layered dependency management. Create separate files for different environments:
# requirements/base.in - production dependencies
django>=4.2
psycopg[binary]
gunicorn
# requirements/dev.in - development additions
-r base.txt
pytest
black
mypy
# requirements/ci.in - CI-specific
-r base.txt
pytest
coverage# Compile each layer
pip-compile requirements/base.in -o requirements/base.txt
pip-compile requirements/dev.in -o requirements/dev.txt
pip-compile requirements/ci.in -o requirements/ci.txtWhen to Use pip-tools
- Existing projects already using
requirements.txt - Deployment platforms that require
requirements.txtoutput - Teams that want minimal tooling changes
- Simple applications that don't need full project management features
Making the Choice
Here's a practical decision framework:
Choose uv if you're starting a new project, CI/CD speed matters to your workflow, you want a single tool for dependencies, environments, and Python versions, or you're migrating from pip and want a drop-in upgrade.
Choose Poetry if you're building a library to publish, you need sophisticated dependency groups, your team values the publishing workflow, or you're already using it and it's working well.
Choose pip-tools if you have existing projects using requirements.txt, your deployment platform requires requirements.txt, or you want the most minimal tooling change possible.
A Note on Docker
Many Django teams feel dependency management pain most acutely inside Docker builds, and this is where uv's speed advantage is most dramatic. A RUN uv pip install -r requirements.txt in a Dockerfile is significantly faster than the pip equivalent, which means faster image builds and shorter CI feedback loops. uv also ships as a standalone binary, so you can copy it into a multi-stage Docker build without needing pip or Python in the builder stage.
Migration Strategies
From pip to uv
The easiest migration. Replace pip with uv pip in your commands:
# Before
pip install -r requirements.txt
# After
uv pip install -r requirements.txtThat's it. Your existing requirements files work unchanged.
From pip-tools to uv
uv can read requirements.in files directly:
# Compile with uv instead of pip-compile
uv pip compile requirements.in -o requirements.txt
# Or migrate to uv's native format
uv init
uv add django requestsFrom Poetry to uv
uv can import from Poetry's pyproject.toml:
# In a Poetry project directory
uv init --import
# This reads pyproject.toml and creates uv.lockBest Practices Regardless of Tool
Whichever tool you choose, some practices are universal:
- Always use lock files. Whether it's
requirements.txtfrompip-compile,poetry.lock, oruv.lock, commit your lock file. It's the only way to ensure reproducible builds. - Separate direct and transitive dependencies. Your input file (
requirements.in,pyproject.toml) should contain only the packages you directly import. Let the tool figure out the rest. - Update dependencies intentionally. Don't let dependencies drift. Schedule regular updates, run tests, and commit the updated lock file as a discrete change you can roll back if needed.
- Pin your Python version. Specify the Python version your project requires. All modern tools support this in
pyproject.toml.
So, Which One?
The Python dependency management ecosystem has finally matured. uv has emerged as the default recommendation for new projects: it's fast, comprehensive, and its compatibility with existing tools makes migration low-risk. Poetry remains an excellent choice for library authors and teams that need its structured publishing workflow. And pip-tools is ideal for incremental improvements to existing pip-based projects.
The best tool is the one your team will actually use consistently. Pick one, establish conventions, and stick with it across your projects.
We've been building production Django and Python applications for over a decade, and we've migrated real client projects across all three of these tools. If you're dealing with slow CI builds, dependency conflicts, or just need help modernizing your Python toolchain, let's talk.
