prefix

What changed

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1. 9/14/2026

   Updated: best for, features, integrations, limitations, plans, platforms, sources, summary, use cases, walkthrough

   See recorded changes

   bestFor

   Before: ["Engineers in machine learning, data science, robotics, scientific computing, HPC, simulation, and embedded systems.","Teams needing reproducible environments across diverse platforms and workflows including CI and deployment."]

   After: ["Machine learning projects requiring GPU-ready environments with PyTorch, CUDA, and Transformers.","Teams needing supply chain security with trusted publishing and Sigstore attestations integrated into the conda ecosystem."]

   features

   Before: ["Reproducible, cross-platform environments with one lockfile for consistency across machines.","Fast installs up to 10× faster than conda or mamba using parallel downloads, Rust-native solver, and smart caching.","Built-in task system allowing running code with one command after cloning a Pixi project.","Supports over 36,000 open source packages including Python, R, C++, CUDA, Fortran, and more from conda-forge ecosystem.","Supply chain security with trusted publishing and Sigstore attestations built into the conda ecosystem."]

   After: ["Reproducible, cross-platform environments with a single lockfile.","Fast installs using parallel downloads, Rust-native solver, and smart caching.","Built-in task system to run code with one command.","Global CDN for package distribution available in Pro and higher plans.","Trusted publishing with GitHub Sign-in, Google Sign-in, and Passkey authentication."]

   integrations

   Before: []

   After: ["Access to conda-forge ecosystem including Python, R, C++, CUDA, Fortran, and more."]

   limitations

   Before: []

   After: ["Free plan limits to 1 user, 1 private channel, 500 Mb private package storage, and 3 Gb public package storage.","Pro plan includes 5 users and 10 Gb private/public package storage; extra users and storage incur additional fees."]

   plans

   Before: []

   After: [{"name":"Pro","note":"Automatically extracted from the linked pricing source. Seat allowances, usage limits, taxes and terms are not confirmed — check them with the provider.","annual":null,"monthly":42,"perSeat":false,"currency":"EUR","features":[],"extraUnit":null,"unitLabel":"","maximumSeats":null,"minimumSeats":1,"includedUnits":null,"startingPrice":false,"seatsConfirmed":false},{"name":"Enterprise","note":"Contact the provider for a quote.","annual":null,"monthly":null,"perSeat":false,"currency":"EUR","features":[],"extraUnit":null,"unitLabel":"","maximumSeats":null,"minimumSeats":1,"includedUnits":null,"startingPrice":false,"seatsConfirmed":false}]

   platforms

   Before: ["Supports Windows, macOS, and Linux platforms for reproducible environments."]

   After: ["Cross-platform support for Windows, macOS, and Linux environments."]

   sources

   Before: [{"url":"https://prefix.dev/","label":"prefix.dev – solving software package management"},{"url":"https://rattler-build.prefix.dev/latest/getting_started/","label":"Getting started - Rattler-Build"}]

   After: [{"url":"https://prefix.dev/","label":"prefix.dev – solving software package management"},{"url":"https://prefix.dev/pricing","label":"prefix.dev – solving software package management"},{"url":"https://rattler-build.prefix.dev/latest/getting_started/","label":"Getting started - Rattler-Build"}]

   summary

   Before: "Data science environments with tools like Jupyter, pandas, SciPy, reproducible across Windows, macOS, and Linux. Reproducible, cross-platform environments with one lockfile for consistency across machines. Fast installs up to 10× faster than conda or mamba using parallel downloads, Rust-native solver, and smart caching."

   After: "Prefix.dev provides a modern software package management platform that enables reproducible, cross-platform environments with over 36,000 packages supporting multiple languages and GPU & SIMD. It ensures consistent environments across machines and CI with a single lockfile and offers fast installs up to 10× faster than conda or mamba."

   useCases

   Before: ["Data science environments with tools like Jupyter, pandas, SciPy, reproducible across Windows, macOS, and Linux.","Robotics development with reproducible ROS 1 & 2 workspaces on laptops, CI, and robots without Docker.","Machine learning environments with PyTorch, CUDA, scikit-learn, and Transformers, supporting multiple GPUs and optimization targets."]

   After: ["Data science environments with Jupyter, pandas, SciPy, reproducible across Windows, macOS, and Linux.","Robotics development with reproducible ROS 1 & 2 workspaces on laptop, CI, and robot without Docker."]

   walkthrough

   Before: ["Clone any Pixi project and run it instantly using the built-in task system.","Use one lockfile to ensure identical software environments across laptops, CI, and coworkers' machines.","Build the package using 'rattler-build build' command which creates isolated environment, runs build script, packages result, and runs tests."]

   After: ["Install Pixi by running a curl command to execute an install script.","Clone any Pixi project and run it instantly using the built-in task system.","Use a single lockfile to ensure identical software environments across devices and CI.","Create a recipe.yaml file describing package metadata, build scripts, and dependencies.","Build the package using the command rattler-build build --recipe recipe.yaml, which creates an isolated environment, runs the build script, packages the result, and tests it."]