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About this tool
Answer a few questions about your Python project and get a ranked recommendation across poetry, pip-tools, uv and conda.
This planner scores uv, Poetry, pip + pip-tools and conda against your project's actual needs and returns a ranked recommendation with the reason behind every point. It uses an additive scoring model built from each tool's documented capabilities — lockfile format, PyPI publishing, binary package support, interpreter management and workspace handling — so the ranking is fully explainable, not a black box. It is for Python developers choosing (or defending) a packaging workflow for an application, a PyPI library or a data science stack.
Open Poetry vs Pip Planner on AltFTool — it loads instantly in your browser.
Paste your code or data sample into the workspace.
Pick the format, conversion, or analysis you need.
Copy the polished result straight back into your project.
Every point in the score maps to one stated reason, so you can defend the recommendation in a team discussion.
The comparison table reflects each tool's own documentation — uv.lock, poetry publish, pip-compile, conda-lock — not opinions.
The winning manager comes with the exact install and init commands to run first.
For most new projects uv is the stronger default: its Rust resolver installs 10-100x faster than pip in Astral's published benchmarks, it writes a single cross-platform uv.lock, manages Python interpreter versions itself, and has first-class workspaces. Poetry remains an excellent choice for libraries, where its mature poetry build and poetry publish flow has years of production use.
You need conda (or mamba) when your project depends on non-Python binaries that pip wheels do not cover — CUDA toolkits, GDAL/geospatial stacks, MKL, or R integration. If every dependency ships a wheel on PyPI, a pip-based tool is lighter and faster; conda's real advantage is distributing arbitrary compiled software, not managing Python packages.
pip-tools is a thin layer over pip: pip-compile pins your dependencies into an ordinary requirements.txt that any vanilla pip can install, but you keep managing virtualenvs and builds yourself. Poetry is an all-in-one tool with its own poetry.lock, automatic virtualenvs, dependency groups and PyPI publishing. Choose pip-tools when your platform expects plain pip artifacts; choose Poetry when you want one tool for the whole workflow.
Yes for anything deployed or shared: a lockfile pins every transitive dependency so your laptop, CI and production install identical versions. uv (uv.lock) and Poetry (poetry.lock) produce cross-platform lockfiles automatically; pip-tools pins a requirements.txt per platform; conda needs the separate conda-lock project. Without one, a transitive release can change your production environment silently.