conda is a language-agnostic, cross-platform package and environment manager. Where pip installs Python packages from PyPI, conda installs packages for Python, R, C/C++, Fortran, and other languages from conda channels and resolves dependencies across all of them in a single graph. It is widely used in scientific computing because it can install non-Python libraries like CUDA, MKL, HDF5, and GDAL alongside the Python packages that depend on them.

What is the difference between conda and pip?

pip installs Python packages from PyPI and assumes a Python interpreter already exists. conda installs packages from conda channels (such as the default Anaconda channel or conda-forge), can manage non-Python dependencies, and treats the Python interpreter itself as a package. Mixing pip and conda in the same environment is risky because conda's solver cannot see pip-installed packages, which can lead to dependency conflicts.

Is conda free to use?

Conda the package manager is open source and free for everyone. The default Anaconda channel, however, requires a paid license for organizations with more than 200 employees. To avoid commercial restrictions, install Miniforge (which defaults to conda-forge) or configure your channels to exclude the Anaconda repository.

What is the difference between conda, Miniconda, and Anaconda?

Conda is the package and environment manager itself. Miniconda is a minimal installer that ships only conda and Python. Anaconda is a distribution that bundles conda with 1,500+ pre-installed scientific Python packages. Miniforge is a third option that defaults to the conda-forge channel and avoids the commercial license restrictions attached to the Anaconda channel.

When should I use conda instead of uv or pip?

Use conda when a project depends on non-Python compiled libraries (CUDA, MKL, HDF5, GDAL, NetCDF) or spans multiple languages like Python and R. For pure-Python projects, uv provides faster installs, tighter PyPI integration, and a simpler project-local workflow than conda's global-environment-by-default model.

Conda

by Tim Hopper · Markdown

Scientific Python

Conda is a language-agnostic, cross-platform package and environment manager. Where pip installs Python packages from PyPI, conda resolves dependencies across Python, R, C/C++, Fortran, and other languages in a single dependency graph.

Note

Conda is the package manager. Anaconda is a distribution that bundles conda with 1,500+ pre-installed packages. See Understanding the Conda/Anaconda Ecosystem for how conda, Miniconda, Miniforge, and Anaconda relate.

Key Features

Cross-language dependency resolution. A single environment specification can pin Python, R, CUDA toolkits, and compiled C/Fortran libraries together. The solver tracks version constraints across all of them.
Python version management. Conda treats the Python interpreter as a package. An environment file can pin python=3.12.4, and conda installs that exact version alongside everything else.
libmamba solver (default since 23.10). Conda’s dependency resolver uses libmamba, a C++ solver ported from the mamba project. It reduced solve times by 50-80% compared to the legacy Python solver. An experimental rattler-based solver written in Rust is also available.
Multiple distribution paths. Available through Miniconda (minimal), Anaconda (batteries-included), or Miniforge (defaults to conda-forge, no commercial license restrictions).
Reproducible environment export. conda env export captures every package, version, and build string. conda env export --from-history records only explicitly requested packages for cross-platform sharing.
Lockfile support. The conda CLI generates and installs native lockfiles, bringing reproducibility closer to what pixi and uv offer.

When to Use Conda

Conda solves problems that Python-only package managers cannot when a project depends on non-Python compiled libraries (CUDA, MKL, HDF5, GDAL, NetCDF) or spans multiple languages. Data science and scientific computing teams that need a single environment covering Python and R packages alongside system-level C/Fortran libraries are the primary audience.

For pure Python projects, uv provides faster installs, tighter PyPI integration, and a simpler workflow. See Why should I choose conda? and uv vs. Pixi vs. Conda for scientific Python for detailed comparisons.

Notable Features

Sharded repodata (CEP 16). Conda fetches package metadata more efficiently from channels that support sharding, reducing the data transferred before a solve.
conda-pypi plugin (public beta). The official direction for installing PyPI packages inside conda environments. Records every PyPI install in conda-meta where the solver can track it, replacing ad-hoc pip install commands.
Formalized specifications (CEPs 29-38). Ten Conda Enhancement Proposals standardize package naming, version comparison, MatchSpec syntax, the .conda format, repodata, and conda-lock.yml. Conda, mamba, pixi, and rattler share a single specification.
Python 3.14 support. The minimum supported Python version is 3.10.

Pros

Resolves Python and non-Python dependencies in a single graph
Manages Python interpreters alongside packages
conda-forge provides 30,000+ community-maintained packages with automated builds
Cross-platform: Linux, macOS, Windows, aarch64
libmamba solver makes resolution competitive with newer tools
Enterprise-friendly with private channel and mirror support

Cons

Packages come from conda channels, not PyPI, creating a parallel ecosystem. Mixing conda and pip in one environment risks dependency conflicts because pip-installed packages are invisible to conda’s solver.
The default Anaconda channel requires a paid license for organizations above 200 employees. Avoidable by using conda-forge via Miniforge, but teams must configure channels deliberately.
Environments are larger than typical Python virtual environments because they bundle compiled system libraries.
The global-environment-by-default workflow feels heavier than the project-local model in uv or pixi.

Learn More

Handbook pages

Official documentation

Last updated on August 8, 2026

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