By ByteBot
uv is 10-100x faster than pip. Installing JupyterLab takes 2.6 seconds with uv versus 21.4 seconds with pip—an 8.2x speedup. The Python community assumes it’s because uv is written in Rust while pip is Python. However, a detailed technical analysis published December 26 reveals the truth is more interesting. Standards compliance and architectural decisions matter far more than the programming language. Moreover, pip could implement most of uv’s speed improvements tomorrow without rewriting a single line in Rust.
Standards Work Did the Heavy Lifting
Four Python Enhancement Proposals (PEPs) made modern fast package management possible. First, PEP 518 (2016) introduced pyproject.toml for declaring build dependencies without executing code. Then PEP 517 (2017) separated build frontends from backends, eliminating setuptools coupling. Subsequently, PEP 621 (2020) standardized dependency declarations readable through TOML parsing rather than Python execution. Finally, PEP 658 (2022) embedded package metadata directly in PyPI’s Simple Repository API, enabling dependency resolution without downloading wheels at all.
The timing matters. Specifically, PyPI added PEP 658 support in May 2023, while uv launched February 2024. “A tool like uv couldn’t have shipped in 2020,” the analysis notes. In other words, standards infrastructure had to evolve before performance breakthroughs were possible.
Architecture Eliminates Work
uv Python performance comes from what it doesn’t do. Specific design choices eliminate entire categories of work that pip performs:
Ignoring Python version upper bounds prevents resolver backtracking. Most upper bounds are defensive guesses that turn out wrong, causing unnecessary exploration of dependency combinations. Meanwhile, the first-index-wins strategy stops searching after finding a package in the first configured index, blocking dependency confusion attacks while saving network requests.
Additionally, uv drops .egg support entirely (the obsolete pre-wheel format), ignores pip.conf configuration inheritance, skips bytecode compilation by default, and requires virtual environments rather than touching system Python. Furthermore, stricter spec enforcement rejects malformed packages instead of applying fallback logic.
These are language-agnostic optimizations. Any tool could adopt them.
What pip Could Implement Without Rust
HTTP range requests leverage zip archive structure to fetch only the metadata from wheel files. Parallel downloads fetch multiple packages simultaneously instead of sequentially. Metadata-only resolution parses TOML and wheel metadata natively without spawning Python subprocesses. The PubGrub resolver uses conflict-driven clause learning from SAT solvers to skip similar dead-end dependency combinations. Global caching with hardlinks means installing the same package into ten virtual environments consumes the same disk space as one.
“pip could implement parallel downloads, global caching, and metadata-only resolution tomorrow,” the analysis states. No Rust required.
Where Rust Actually Helps
Rust provides three real advantages for uv Python performance. First, thread-level parallelism avoids Python’s GIL restrictions, enabling native thread sharing essential for exploring version combinations during dependency resolution. Second, a single static binary eliminates Python interpreter startup costs for each subprocess invocation. Third, compact version representation packs versions into u64 integers for fast comparison and hashing—over 90% of versions fit in one u64.
These matter, but Hacker News commenters argue algorithmic improvements would yield 5-8x speedups in Go or optimized Python, not just Rust’s marginal gains. The top insight: “Treating Python packaging as a well-specified systems problem instead of a pile of historical accidents.”
The Uncomfortable Question
uv has surpassed pip in CI environments for major Python projects. Specifically, it accounts for 66% of CI downloads for Wagtail, 60% for FastAPI, and 43% for Django. The tool is replacing pip where performance matters most.
This raises the question nobody wants to ask: Why did it take a commercial company (Astral, makers of Ruff) to deliver what the Python community wanted for years? After all, standards existed. Optimizations were language-agnostic. pip maintainers knew about these techniques.
The Hacker News discussion surfaced competing theories: organizational dysfunction in volunteer-driven projects, consensus-building paralysis, or simply that commercial incentive structures outperform FOSS governance for certain problems. “Projects like NumPy demonstrate that open-source efforts accomplish comparable feats without commercial backing,” one commenter noted. However, NumPy had institutional funding and full-time contributors.
Astral’s business model adds nuance. The tools remain free and open-source. Monetization comes from services built on top—pyx, their Python-native package registry launched in August 2025. Vertical integration: Ruff (linter) + uv (package manager) + ty (type checker in development) + pyx (registry). Commercial control of critical infrastructure in exchange for performance and user experience improvements.
Design Decisions Matter More Than Language
“uv is fast because of what it doesn’t do, not because of what language it’s written in,” the analysis concludes. Standards work enabled innovation. Architectural decisions eliminated unnecessary work. Rust helped with parallelism and binary distribution, but those are secondary factors.
The lesson extends beyond Python packaging. Performance debates often fixate on implementation language when design choices and infrastructure standards matter more. The uncomfortable question persists: Can established FOSS projects modernize when volunteers maintain them, or does commercial backing provide the focus and resources needed for breakthroughs?
Python packaging has an answer. The community gets to decide if they like it.
