PDM - Python Dependency Manager VS UV

Compare PDM - Python Dependency Manager VS UV and see what are their differences

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PDM - Python Dependency Manager

PDM, as described, is a modern Python package and dependency manager supporting the latest PEP standards.

UV

An extremely fast Python package and project manager, written in Rust.

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2025-11-26

PDM - Python Dependency Manager

Website: pdm-project.org
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UV

Website: docs.astral.sh
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PDM - Python Dependency Manager features and specs

No features have been listed yet.

UV features and specs

Efficiency
UV efficiently compresses URLs, allowing for shorter, more manageable links.
Customization
Users can customize their shortened URLs, making them more descriptive and easier to remember.
Analytics
UV provides comprehensive analytics, enabling users to track the performance of their links in real-time.
Privacy Features
The platform offers enhanced privacy settings, ensuring user data and link performance are kept confidential.
Integration
UV easily integrates with other applications and platforms, streamlining the URL shortening process across various workflows.

Possible disadvantages of UV

Reliability
As with any online service, there may be occasional downtime or service interruptions affecting accessibility.
Cost
Some advanced features may require a subscription or additional fees, potentially limiting access for some users.
Learning Curve
New users may experience a learning curve when navigating UV's features and interface, especially if unfamiliar with URL shortening services.
Dependency
Relying on a third-party service for URL management can pose risks if the service discontinues or changes terms.

Analysis of UV

Overall verdict

uv is an excellent, modern Python package and project manager that dramatically speeds up dependency resolution and installation while unifying tooling into a single, reliable binary.

Why this product is good

Extremely fast dependency resolution and installation, often 10-100x faster than pip thanks to its Rust implementation
Unifies multiple tools (pip, pip-tools, virtualenv, pipx, poetry-like workflows) into a single cohesive CLI
Manages Python versions itself, so you don't need separate tools like pyenv
Provides reproducible builds with a universal lockfile and clear project structure
Backed by Astral, the team behind Ruff, with active development and strong documentation
Drop-in compatible pip interface makes migration low-risk for existing projects

Recommended for

Python developers who want faster, more reliable dependency management
Teams needing reproducible environments and lockfiles across machines
Projects juggling multiple Python versions and virtual environments
Developers looking to consolidate fragmented tooling into one tool
CI/CD pipelines where install speed and reproducibility matter

PDM - Python Dependency Manager videos

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Category Popularity

0-100% (relative to PDM - Python Dependency Manager and UV)

PDM - Python Dependency Manager

UV

Front End Package Manager

20 20%

Front End Package Manager

80% 80

Developer Tools

15 15%

Developer Tools

85% 85

Kids

45 45%

Kids

55% 55

Package Manager

100 100%

Package Manager

0% 0

User comments

Share your experience with using PDM - Python Dependency Manager and UV. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, UV seems to be a lot more popular than PDM - Python Dependency Manager. While we know about 29 links to UV, we've tracked only 1 mention of PDM - Python Dependency Manager. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PDM - Python Dependency Manager mentions (1)

Publishing a LitePolis package to PyPI using Github Actions
The pyproject.toml serves as a central place for your project's configuration. It tells the build frontend tool which backend is needed to build your package. Some available options include Hatchling, Setuptools, Flit, or PDM. Below is an example of a common build backend. - Source: dev.to / over 1 year ago

UV mentions (29)

Build a Deterministic Multi-Agent Pipeline with A2A in Python
Uv for environment and dependency management. - Source: dev.to / 4 days ago
How to build an MCP server, step by step
Python 3.10 or newer and uv (recommended) or pip to manage the environment. - Source: dev.to / 9 days ago
How to Generate requirements.txt From What Your Code Actually Imports (pipreqs vs pip freeze)
We're piloting uv, Astral's Rust-based package manager, and weighing whether pyproject.toml should become the single source of dependency truth with requirements.txt generated as a lockfile artifact - which is where most modern Python workflows are clearly heading. But I keep coming back to how small the actual fix was. One tool swap turned a 214-line liability into a 23-line file that documents exactly what the... - Source: dev.to / 9 days ago
OKF for Claude Code: structured, portable memory your agent (and team) can read
To turn on automatic upkeep (consult .okf/ before tasks, write knowledge back after changes), paste templates/CLAUDE-okf.md into your project's CLAUDE.md. It's soft mode and entirely opt-in. The scripts need uv (or python3 + pyyaml). - Source: dev.to / about 1 month ago
I Let My AI Agent Build a Bedrock RAG Knowledge Base, Here Are the 2 Mistakes the AWS Agent Toolkit Caught
To follow along, you need an AWS account, a non-root IAM identity with credentials configured locally, uv installed, and the toolkit installed in your agent. The fastest path across Kiro, Claude Code, Cursor, and Codex is the AWS CLI installer, aws configure agent-toolkit; in Kiro you can instead add the AWS MCP Server to .kiro/settings/mcp.json (pin the mcp-proxy-for-aws version) and run npx skills add... - Source: dev.to / about 2 months ago