Presentation

Anaconda is an environment and platform focused on Python, data science, scientific computing and artificial intelligence.

Its historical product, Anaconda Distribution, provides a ready-to-use installation including Python, the conda package and environment manager, Jupyter Notebook, JupyterLab and a collection of commonly used libraries.

One of its main goals is to simplify a frequent challenge in the Python ecosystem: installing multiple libraries with their dependencies while maintaining reproducible and isolated environments.

Anaconda also provides Navigator, a graphical interface for managing packages, environments and applications without always using the terminal.

The offering has gradually expanded with Anaconda Core and professional services designed to secure, control and distribute the packages used across organizations.

As of October 3, 2026, the current version of Anaconda Distribution is 2026.07-1.

Features

Anaconda Distribution

Anaconda Distribution is the traditional starting point.

The installer provides Python, conda, Jupyter and several hundred popular packages aimed notably at data science, scientific computing and machine learning.

This approach makes it possible to obtain a consistent environment quickly without having to install every component separately.

Distribution is available for Windows, macOS and Linux.

conda

conda is an open-source package, dependency and environment manager.

Unlike a manager limited to Python libraries, conda can also install other components and manage multiple versions of Python or other languages.

It resolves the required dependencies and then installs compatible sets of packages.

This approach is particularly useful in scientific projects where certain libraries depend on complex native components.

Isolated environments

conda can create multiple independent environments on the same machine.

One project can, for example, use a particular version of Python and NumPy while another keeps different versions.

This separation reduces dependency conflicts and makes it easier to reproduce an environment on another machine.

Environments can be described in files to make sharing with a team or rebuilding them easier.

Anaconda Navigator

Anaconda Navigator provides a graphical interface for managing the main elements of an Anaconda environment.

Users can create environments, search for and install packages, manage different channels and launch applications such as JupyterLab without entering every command manually.

Navigator therefore provides a more accessible entry point for people who want to use conda without working exclusively in a terminal.

It is available on Windows, macOS and Linux.

Jupyter Notebook and JupyterLab

Anaconda Distribution includes Jupyter Notebook and JupyterLab.

These tools make it possible to combine executable code, text, equations, results and visualizations inside interactive documents.

This format is particularly common in data exploration, scientific experimentation, education and model prototyping.

Notebooks remain independent projects from Anaconda: Distribution mainly simplifies their installation and integration into a consistent Python environment.

Packages and Anaconda Repository

Anaconda maintains repositories containing thousands of packages for Python, data science and AI.

Packages provided by Anaconda are built and distributed as conda packages to simplify installation together with their dependencies.

Additional channels can also be configured, notably conda-forge, a separate community project with a very large catalog.

Each package retains its own license: using Anaconda does not automatically change the rights associated with installed libraries.

Miniconda

Miniconda is a lighter alternative to Anaconda Distribution.

It mainly installs conda, Python and a minimal set of components.

Users then add only the libraries required by their project.

This approach is suitable when precise control over the environment is preferred and installing a large number of packages from the start is unnecessary.

Data science and artificial intelligence

Anaconda is particularly common for installing libraries used in data analysis, scientific computing and machine learning.

An environment can combine data manipulation tools, visualization libraries, notebooks and machine-learning frameworks.

The main purpose of Anaconda is not to replace these libraries, but to simplify their installation and coexistence.

It can therefore serve as a foundation for Python environments dedicated to experimentation and AI development.

Anaconda Core

Anaconda Core extends package management toward professional use cases.

Paid tiers add more shared workspace, governance, access control and security capabilities.

Business notably adds vulnerability analysis, auditing, centralized access management and package security information.

More advanced deployments can also run on private infrastructure or on-premises.

Anaconda Desktop

Anaconda is also developing Anaconda Desktop, currently presented in beta.

This newer application aims to combine graphical management of conda environments, AI model discovery and local model execution inside a more modern interface.

It currently complements Navigator rather than immediately replacing the traditional Anaconda Distribution environment.

Its beta status means that its features and positioning may still evolve.

Use cases

Install a scientific Python environment

A user can install Anaconda Distribution and immediately have access to Python, Jupyter and a collection of common libraries.

This approach reduces the number of steps required to start a data science project.

Separate multiple projects

conda can create a different environment for each project.

Each environment keeps its own versions of Python and required libraries without modifying the others.

Work with notebooks

Anaconda makes it easy to launch Jupyter Notebook or JupyterLab inside the selected environment.

The notebook can then be used to explore data, perform calculations, create charts or test a model.

Reproduce an environment

Project dependencies can be described and then recreated on another machine.

This makes it easier to share an environment between developers, researchers or workstations.

Manage Python within an organization

Professional plans allow a company to exercise greater control over available packages, track vulnerabilities and centralize parts of their distribution.

This approach becomes particularly useful when many users rely on the same Python ecosystem.

Conclusion

Anaconda simplifies an essential part of the Python ecosystem: installing, isolating and maintaining environments composed of many dependencies.

Anaconda Distribution remains a particularly accessible solution for getting started with data science thanks to Python, conda, Jupyter and its collection of preinstalled packages.

Miniconda provides a lighter alternative for users who prefer to build their environment gradually, while Navigator offers a graphical interface for the main conda features.

For teams, Anaconda Core adds security, governance and centralized package management capabilities.

Anaconda is not required to use Python, however. pip, venv, conda-forge and other managers can build environments without relying on Anaconda's commercial repositories.

For quickly setting up reproducible Python environments, working with Jupyter and managing complex dependencies for data or AI projects, Anaconda nevertheless remains a particularly practical solution.

Points to consider

  • Anaconda Distribution 2026.07-1 is the current version as of October 3, 2026.

  • Anaconda is not entirely open source. conda and many integrated components are open source, but Anaconda Distribution, repositories and platform services are governed by Anaconda's commercial terms.

  • Use of the platform remains free in several situations, notably for personal non-commercial use and for commercial organizations with no more than 200 employees or contractors.

  • A commercial organization with more than 200 employees or contractors normally needs to purchase Business licenses to use the Anaconda platform and repositories, unless an applicable exemption applies.

  • Anaconda Core Free is listed at $0, Starter at US$15 per user per month and Business at US$50 per user per month. More advanced offers are available on request.

  • conda itself does not require a commercial Anaconda license. Payment conditions notably concern the use of Anaconda offerings and repositories depending on the organization's context.

  • conda-forge is separate from Anaconda's commercial repositories. Miniforge can notably be used with conda-forge without requiring a commercial Anaconda license linked to the defaults repository.

  • Miniconda and Anaconda Distribution are not equivalent. Miniconda provides a minimal environment, while Distribution installs a much larger collection of packages from the start.

  • Every installed library has its own license. Accessing a package through Anaconda does not automatically grant additional rights to it.

  • Anaconda Distribution is large. For a project that only needs a few dependencies, Miniconda, Miniforge or a standard Python installation may be lighter.

  • Mixing conda and pip requires care. The two managers may resolve dependencies differently; isolating projects and controlling installation order is preferable.

  • Anaconda Desktop is still in beta. Its interface and AI model-related features may therefore evolve.