venv vs uv: Choosing the Right Python Virtual Environment
Every Python project has its own dependencies.
Mixing them system-wide quickly leads to version conflicts, broken setups, and unpredictable behavior.
That’s why Python virtual environments exist: they isolate dependencies per project, keeping development safe and reproducible.
For years, developers have relied on venv, Python’s built-in solution.
More recently, modern tools like uv have emerged, offering faster and more integrated dependency management.
In this post, we compare venv vs uv side by side to help you decide which one fits your workflow.
What is venv?
venv is Python’s built-in virtual environment module.
It creates an isolated directory containing a separate Python interpreter and installed packages.
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
Why developers use venv
- Comes bundled with Python
- Works everywhere
- No additional tooling required
- Simple and predictable
Limitations of venv
- Dependency syncing is manual
- Requires
pip+requirements.txt - No built-in lockfile mechanism
- Environment and dependency management are separate steps
What is uv?
uv is a modern Python package and environment manager written in Rust.
Instead of managing virtual environments and dependencies separately, uv integrates both workflows into a single, fast tool.
Technically,
uvis not a new type of virtual environment.
It automates the creation and management of a standard.venvbehind the scenes.
uv venv
uv add polars pandas requests
uv run app.py
uv sync
Why developers switch to uv
- Extremely fast dependency resolution
- Automatic synchronization
- Uses
pyproject.toml - Built-in reproducibility
- Cleaner workflow
venv vs uv: Side-by-Side Comparison
| Task | venv | uv |
|---|---|---|
| Create environment | python -m venv .venv | uv venv |
| Activate | manual activation | auto-handled |
| Install dependencies | pip install -r requirements.txt | uv add package |
| Dependency sync | manual | uv sync |
| Speed | standard | very fast |
| Config file | requirements.txt | pyproject.toml |
| Lockfile support | external tools required | built-in |
When Should You Use venv?
venv is still a great choice when:
- You need maximum portability
- You're working on minimal setups
- You don’t want additional tooling
- You're deploying to constrained environments
It remains the most universally compatible option.
When Should You Use uv?
uv shines when:
- You work on active projects
- You care about speed
- You want reproducibility by default
- You prefer modern Python tooling
- You’re already using
pyproject.toml
In my own workflow, once I started using uv, I gradually stopped using plain venv with pip for active projects.
The integrated dependency management and automatic synchronization removed a lot of small frictions I used to accept as normal.
While venv still works perfectly well, uv simply fits better into modern Python development.
Final Thoughts
Both venv and uv solve the same core problem: managing Python virtual environments safely.
If you value simplicity and universal compatibility, venv is still a solid default.
But if you want a faster, more integrated workflow with built-in reproducibility, uv is a compelling modern alternative.
In the end, it’s not about replacing one with the other, it’s about choosing the right tool for your context.
