When it comes to Python package managers, the choice often comes down to uv vs pip. You may choose pip for out-of-the-box availability, broad compatibility, and reliable ecosystem support. In contrast, uv is worth considering if you prioritize fast installs, reproducible environments, and clean uninstall behavior, or if you want to streamline workflows for new projects.
In this tutorial, you’ll compare both tools. To keep this comparison meaningful, you’ll focus on the overlapping features, primarily package installation and dependency management. The decision table below can help you quickly choose between the two:
| Use Case | uv |
pip |
|---|---|---|
| You need a tool with reliable ecosystem support | — | ✅ |
| You need reproducible, locked environments | ✅ | — |
Choosing the right package installer can greatly affect your workflow as a Python developer. In this tutorial, you’ll compare uv and pip, explore their overlapping features, and learn how to pick the right tool for your project’s goals.
Get Your Cheat Sheet: Click here to download the uv vs pip cheat sheet that will help you decide which tool to use.
Take the Quiz: Test your knowledge with our interactive “uv vs pip: Managing Python Packages and Dependencies” quiz. You’ll receive a score upon completion to help you track your learning progress:
Interactive Quiz
uv vs pip: Managing Python Packages and DependenciesTest your knowledge of uv vs pip as Python package managers and learn how to pick the right tool for speed, reproducibility, and compatibility.
Metrics Comparison: uv vs pip
To help you quickly see where uv and pip differ, the table below summarizes their strengths and trade-offs in package installation and dependency management:
| Metric | uv |
pip |
|---|---|---|
| Out-of-the-Box Availability | No | Yes |
| Package installation speed | Installs JupyterLab in 2.618 seconds | Installs JupyterLab in 21.409 seconds |
| Reproducible installs | Supports reproducible installs based on native locking | Supports requirements.txt and needs pip-tools for reproducibility |
| Removal of transitive dependencies | Yes | No |
| Maturity and ecosystem support | New and growing, adoption increasing | Mature, standard tool in the Python ecosystem |
| Licensing | MIT license | MIT license |
| Supporting organization | Astral, a private company focused on high-performance developer tools for Python | Python Packaging Authority (PyPA), an official part of the Python Software Foundation (PSF) |
After this quick summary, you’ll run a more detailed analysis to learn more about the intricacies of each specific metric or feature.
Note: To learn more about pip and uv in general, you can check out these tutorials:
In the following sections, you’ll explore these metrics one by one and run a few benchmarks to help you compare both tools and decide which one better suits your specific needs.
Out-of-the-Box Availability
One big reason pip remains dominant is that it ships with Python. This means that if you install Python with the official CPython installer, then you’ll have pip available out of the box and can use it to install packages with no extra step:
$ python -m pip install requests
Once you’ve installed Python, you can use pip immediately without installing additional tools. This is convenient when you don’t have the appropriate permissions to install new software on your work computer.
Note: On Ubuntu and Debian, the default Python installation often includes the python3 package, but may not include pip. If it’s missing, then you can install it by running sudo apt install python3-pip in your terminal window.
On the other hand, uv requires an extra installation step. You can install it using the standalone installer by running the command below:
This additional setup might not be a problem for you, but it can be a blocker if you don’t have the appropriate permissions to install apps in your working environment. Fortunately, uv has other installation options that you can use to install it in your user space.
Package Installation Speed
Here’s where uv really shines compared to pip. Written in Rust and designed for speed, uv can install packages faster than pip. This is especially true when you’re working on projects with large dependency trees.
Coming up next, you’ll see how uv and pip compare for installing packages and managing dependencies.
Setting Up the Environment
Before running these benchmarks, create two clean virtual environments and clear any cached dependencies. Go ahead and open two terminal windows—one for pip and the second for uv. Then, run the following commands in the appropriate window:
pip window
$ python -m venv .pip_venv/
$ source .pip_venv/bin/activate
(.pip_venv) $ python -m pip cache purge
uv window
$ uv venv .uv_venv/
$ source .uv_venv/bin/activate
(.uv_venv) $ uv cache clean
With these commands, you create separate environments for pip and uv and clear their caches, ensuring a fair cold-start comparison.
Timing Package Installations
You can use the time command on Unix-like systems to measure the total runtime of each command. So, you’ll use this tool to measure how long pip and uv take to install external packages and libraries.
Note: In this section, you’ll use the pip interface of uv to install packages. However, the recommended approach is to use the uv add command, which fully leverages the power of uv for dependency management.
To run the benchmark, you’ll install JupyterLab, which has many dependencies and is therefore an excellent candidate for a speed test:
pip window
(.pip_venv) $ time python -m pip install jupyterlab
...
python -m pip install jupyterlab
⮑ 7.13s user
⮑ 2.26s system
⮑ 43% cpu
⮑ 21.409 total
uv window
(.uv_venv) $ time uv pip install jupyterlab
...
uv pip install jupyterlab
⮑ 0.60s user
⮑ 1.42s system
⮑ 77% cpu
⮑ 2.618 total
In this example, pip installed JupyterLab in 21.409 seconds, while uv did it in 2.618 seconds—about eight times faster. You can run the benchmark with other packages and libraries if you like.
Note: The numbers you see on your own machine may differ from those shown here. Installation times depend on factors such as your operating system, hardware, network speed, and whether packages are already cached.
For the second part of the benchmark, you’ll create a custom requirements.txt file with several dependencies and check how long pip and uv take to install them:
requirements.txt
pandas
matplotlib
scikit-learn
jupyterlab
django
Then, run the following commands without clearing the cache:
pip window
(.pip_venv) $ time python -m pip install -r requirements.txt
...
python -m pip install -r requirements.txt
⮑ 5.18s user
⮑ 1.53s system
⮑ 67% cpu
⮑ 9.968 total
uv window
(.uv_venv) $ time uv pip install -r requirements.txt
...
uv pip install -r requirements.txt
⮑ 0.45s user
⮑ 1.02s system
⮑ 67% cpu
⮑ 2.173 total
Here’s a summary of the performance comparison between pip and uv when installing the same requirements.txt file:
| Tool | User Time | System Time | Total Time |
|---|---|---|---|
pip |
5.18 seconds | 1.53 seconds | 9.97 seconds |
uv |
0.45 seconds | 1.02 seconds | 2.17 seconds |
In short, uv completed the installation in under a quarter of the time pip took. So, you can conclude that uv dramatically reduces install time compared to pip, especially on repeated or large installs. This uv capability makes it a solid choice for speeding up continuous integration (CI) workflows.
The performance advantage of uv comes from parallel downloads, system-wide caching, and Rust’s native performance.
Reproducible Installs
If you want to lock or pin your dependencies and their specific versions so that you can reproduce installs across different machines and times, then you can use pip freeze. For example, say you’re working on a project that uses Flask and requests. You’ve created a dedicated virtual environment and installed these packages. Now, you can run the following command to create the requirements.txt file:
$ python -m pip freeze > requirements.txt
This command generates the requirements file. However, this approach has several drawbacks, including the following:
- Captures all the installed packages, mixing direct, transitive, and development dependencies
- Lacks portability across platforms and Python versions, so a file created on Windows might not work on Linux
Direct dependencies are the top-level packages your project needs. In this example, these dependencies are Flask and requests. Transitive dependencies are the packages of your direct dependencies. Finally, development dependencies are packages you only need while developing the project. A typical example is pytest for running tests.
The resulting requirements.txt lists all the installed packages—direct, transitive, and development—and pins them to their current versions. This frozen snapshot of your environment can make updating any dependency a nightmare because even a small update requires regenerating the entire file.
To reproduce your environment in another system based on the requirements file, you can run the following command:
$ python -m pip install -r requirements.txt
While this command will re-create exactly what was frozen, you can’t reliably evolve the environment because you lose track of which packages were explicitly required and which were pulled in as transitive dependencies. Additionally, two developers working in parallel can create conflicting requirements.txt snapshots.
Note: To achieve reproducibility more reliably, you can use external tools like pip-tools, which you install with pip. By using pip-tools, you maintain a requirements.in file that lists your direct dependencies:
requirements.in
flask>=2.0
requests==2.31.0
Then, you can compile this file to create a requirements.txt file with pinned versions for transitive dependencies:
$ python -m venv .pip_venv/
$ source .pip_venv/bin/activate
(.pip_venv) $ python -m pip install pip-tools
(.pip_venv) $ python -m pip-compile requirements.in
The result is a lockfile-style requirements.txt file that ensures consistent installs across environments and over time.
In contrast, uv provides a more reliable approach that guarantees reproducibility and facilitates updates. Go ahead and run the following commands to scaffold a Python project with uv:
$ mkdir project/
$ cd project/
$ uv init
Initialized project `project`
Once you’ve initialized the project, you can create a dedicated virtual environment with uv and install the direct dependencies using the following:
$ uv add "flask>=2.0" "requests==2.31.0"
This command installs Flask and requests as direct dependencies, along with all corresponding transitive dependencies. It also creates a uv.lock file containing the list of current dependencies with pinned versions.
Note: When you create a virtual environment with uv venv, the environment’s folder name defaults to .venv/. You don’t need to activate the environment for uv add or similar commands to work.
Once you have this file in place, you can reproduce the working environment on another computer using the following command:
$ uv sync
This command reads the uv.lock file, installs all locked dependencies, and ensures reproducibility and consistency across environments. No more It works on my computer! exclamations.
Every time you install, update, or remove a dependency with the uv add, uv add --upgrade, or uv remove commands, uv takes care of updating the uv.lock file for you. This guarantees consistent installs across environments and over time.
Removal of Transitive Dependencies
A less visible but useful difference is that uv removes transitive dependencies when uninstalling a direct dependency. In contrast, pip leaves them behind. For example, say that you start with requests as a direct dependency for a project:
$ python -m venv .venv/
$ source .venv/bin/activate
(.venv) $ python -m pip install requests
...
(.venv) $ python -m pip list
Package Version
------------------ ---------
certifi 2025.7.14
charset-normalizer 3.4.2
idna 3.10
pip 25.0.1
requests 2.32.4
urllib3 2.5.0
After you create and activate a fresh virtual environment, the first command installs requests and several transitive dependencies, which are highlighted in the pip list output.
Now, say that later you decide to take advantage of asynchronous programming to make your code more efficient. You start using the aiohttp library and don’t need requests anymore. Then, you go and uninstall it with the command below:
(.venv) $ python -m pip uninstall requests
...
(.venv) $ python -m pip list
Package Version
------------------ ---------
certifi 2025.7.14
charset-normalizer 3.4.2
idna 3.10
pip 25.0.1
urllib3 2.5.0
The uninstall removes requests but leaves behind its transitive dependencies, as you can see in the output of pip list.
With uv, you install the direct dependencies with the add command and uninstall them with the remove command. Note that to run the commands below, you need an active uv project, for example, created with uv init:
$ uv add requests
...
$ uv pip list
Package Version
------------------ ---------
certifi 2025.7.14
charset-normalizer 3.4.2
idna 3.10
requests 2.32.4
urllib3 2.5.0
$ uv remove requests
...
$ uv pip list
The uv remove requests command uninstalls the direct dependency and all its transitive dependencies, leaving you a clean environment. You confirm this behavior because the uv pip list command doesn’t issue any output.
This feature of uv is beneficial in long-lived development environments or automated build systems, where leftover dependencies can accumulate over time and clutter the environment.
Maturity and Ecosystem Support
For many years, pip has been the default package installer in the Python ecosystem. It’s widely used and often integrates seamlessly with code editors, integrated development environments (IDEs), and build systems. It has a stable interface and well-known error messages.
In contrast, uv is relatively new. It’s being rapidly adopted for its performance and is used in modern toolchains and workflows. However, it’s not the universal default yet. Certain systems won’t recognize it, and some workflows may not support it. That said, the adoption of uv is growing quickly, much like Ruff.
If you’re working on a project that relies on toolchains and workflows that don’t support uv yet or only partially support it, then pip is the way to go. If you’re on a project that uses modern toolchains and workflows, then uv can be a good choice.
Licensing and Supporting Organization
Both pip and uv use the MIT license, which allows free use in open-source and commercial projects. However, their governance models differ:
pipis maintained by the Python Packaging Authority (PyPA), which operates under the Python Software Foundation PSF.uvis developed by Astral, which is a private company.
Also, uv is written in Rust rather than Python, so it may be harder for the Python community to maintain if Astral stops development.
If Astral’s priorities change, then uv could stagnate or shift to a commercial model. That doesn’t mean uv is unreliable, but it does introduce a degree of uncertainty that you won’t have with pip.
Decision Table: uv vs pip
Here’s a quick summary of when to use uv or pip to install packages and manage dependencies in Python:
| If you… | Choose uv |
Choose pip |
|---|---|---|
| Can’t install tools beyond the standard Python environment | — | ✅ |
| Want fast installs and dependency resolution | ✅ | — |
| Need reproducible, locked environments for multiple developers | ✅ | — |
| Favor maturity and stability | — | ✅ |
| Need long-term certainty on licensing and development | — | ✅ |
| Want a clean environment after uninstalling unneeded dependencies | ✅ | — |
Since pip is reliable and widely supported, it’s still the recommended choice for compatibility and peace of mind, especially in production environments and when you’re learning Python.
If you’re starting a new project, working in continuous integration (CI), or care deeply about speed and reproducibility, then uv is a remarkable candidate. It makes Python package installation and dependency management much more pleasant, fast, and reliable.
Additionally, if you go with uv, take some time to explore its other features, including the following:
- Managing multiple Python versions
- Managing virtual environments
- Scaffolding Python project for apps, libraries, and packages
- Building and publishing projects
You might find that uv is all you need for Python project setup and management because it goes beyond installing packages and managing dependencies. In practice, uv can do the job of tools like pyenv, venv, pip, pip-tools, build, and twine, which are often part of toolchains and workflows in Python.
Conclusion
In this tutorial, you compared two popular tools for managing Python packages: uv and pip. You saw how they handle package installation, dependency management, and environment reproducibility, as well as their differences in speed, ecosystem support, package removal, and governance models.
Choosing the right package manager is crucial for efficient and reproducible Python environments. As the ecosystem evolves, understanding the strengths and trade-offs of uv and pip will help you build faster, cleaner, and more maintainable projects.
In this tutorial, you’ve:
- Compared the overlapping features and use cases of
uvandpip - Benchmarked the package installation speed between the two tools
- Achieved reproducible environments using lock files and dependency pinning
- Cleaned up environments by properly uninstalling transitive dependencies
- Evaluated maturity, licensing, and ecosystem support for
uvandpip
With these skills, you can confidently choose the most appropriate tool for your Python projects, ensuring efficient workflows and reliable environments.
Get Your Cheat Sheet: Click here to download the uv vs pip cheat sheet that will help you decide which tool to use.
Frequently Asked Questions
Now that you have some experience with managing Python packages using uv and pip, you can use the questions and answers below to check your understanding and recap what you’ve learned.
These FAQs are related to the most important concepts you’ve covered in this tutorial. Click the Show/Hide toggle beside each question to reveal the answer.
You use pip as the standard package installer that comes with Python, while you use uv as a newer tool that also manages package installation but adds features like fast installs, dependency locking, and environment management.
You might choose uv if you want faster installation times, automatic removal of transitive dependencies, and reproducible environments through native locking, especially for new projects.
With pip, you create a requirements.txt file with pinned versions, often using pip-tools to lock transitive dependencies. With uv, you generate a uv.lock file or a compiled requirements.txt to ensure that every dependency is version-locked.
When you uninstall a package with pip, the transitive dependencies usually remain in your environment, but when you uninstall with uv, it removes both the package and its transitive dependencies, leaving your environment clean.
You should stick with pip if you need maximum compatibility, proven maturity, integration with existing toolchains and workflows, or if you can’t install external tools beyond what’s already included with Python.
Take the Quiz: Test your knowledge with our interactive “uv vs pip: Managing Python Packages and Dependencies” quiz. You’ll receive a score upon completion to help you track your learning progress:
Interactive Quiz
uv vs pip: Managing Python Packages and DependenciesTest your knowledge of uv vs pip as Python package managers and learn how to pick the right tool for speed, reproducibility, and compatibility.
