Navigating Anaconda Licensing Changes: What You Need to Know

Understanding Anaconda’s Licensing Changes

Anaconda’s 2024 licensing changes have caused some concern, particularly among specific sectors like academic, research, and non-profit, which have relied on the platform under the understanding that it was available at no cost.

To complicate matters, there’s often a misunderstanding of what is and isn’t free when using conda, the package manager, versus the channels and distributions provided by Anaconda.

If you’re still confused, don’t worry; I’ll explain the changes in this section and hopefully clarify whether this should concern you.

So, what changed?

The heart of the changes lies in Anaconda’s definition of "Organizational Use." According to the new licensing terms, any organization with 200 or more employees or contractors is now required to purchase a paid license to use Anaconda's software.

The licensing terms clarify, in particular, that the 200-employee threshold applies not only to for-profit companies but also to government entities and non-profit organizations.

This is a shift from previous policies that had allowed universities, research institutions, and other non-profits to use Anaconda without charge (or at least that’s what some thought). Currently, these organizations must obtain licenses if they exceed the 200-employee limit.

While educational institutions are still exempt from this requirement, their usage must be limited to "curriculum-based courses." Anaconda reserves the right to monitor and ensure that the software is used strictly for educational purposes.

Now, that’s not the end of the story. It’s worth breaking down what exactly is and isn’t free to use within the Anaconda ecosystem.

Conda: The free and open-source tool

First, it’s important to clarify that conda, the package and environment management software, remains free and open-source. Anyone can use conda to create, manage, and install environments without licensing concerns. This has always been the case and continues to be so. 

Conda-compatible packages—the packages you install using conda from community channels like conda-forge—are also free to use.

Conda vs. Anaconda Distribution and defaults channel

While conda is free, the defaults channel—where Anaconda stores and curates specific packages—has different licensing rules. 

The defaults channel is part of Anaconda Distribution, a collection of data science and AI packages curated, built, and maintained by Anaconda’s engineers. This distribution, along with the defaults channel, emphasizes security, stability, and regulatory compliance, which is important for companies dealing with sensitive data or operating in highly regulated industries.

The defaults channel consists of three distinct channels: pkg/main, pkgs/r, and pkg/msys2, according to the FAQ section in the Anaconda packages repository. These channels are automatically configured in your .condarc configuration file when you install Anaconda Distribution or Miniconda.

The terms of service updates only apply to the defaults channel and Anaconda Distribution. According to these terms:

• Companies with fewer than 200 employees can continue using Anaconda Distribution and the default channel for free.
• Companies with 200 or more employees must purchase a paid license to access the default channel or use Anaconda Distribution.
• Students and educational institutions are exempt from this, and they can use the software for free, provided it is used for curriculum-based courses.

Anaconda Navigator, a desktop GUI included in Anaconda Distribution. Image source: Anaconda.

For many, this change means that what was previously considered free is now subject to licensing fees based on the number of employees. Others, who may just be using conda, may be relieved now. 

What about Miniconda?

If you're looking for a lightweight alternative to Anaconda Distribution, Miniconda is an excellent option. It includes only the core conda package manager and Python, the packages they both depend on and a small number of other useful packages.

Miniconda allows you to install and manage only the packages you need, making it a more customizable and flexible option for those who do not require the full Anaconda package set. 

Importantly, Miniconda is free and not subject to Anaconda's licensing terms as long as you avoid using the defaults channel and stick to community-driven repositories.

But why does the defaults channel require payment?

Anaconda’s defaults channel is not just a random collection of packages; it is curated and secured by Anaconda’s engineers, making it more stable and secure than community-run channels like conda-forge. This extra layer of security is important for industries that require high trust in their software, such as finance, healthcare, and government.

The course Introduction to Data Security teaches you more about keeping data safe. It’s a valuable resource, particularly if you work in the industries mentioned above.

While community channels like conda-forge offer a vast selection of packages, they rely on community contributions and best-effort security practices. These channels are excellent for research, development, and non-critical environments. 

Image source: conda-forge

Still, for companies handling sensitive data, the added security and maintenance provided by Anaconda’s default channel may justify the cost.

So, to summarize:

• Conda, the package manager, is free, open-source, and available to anyone.
• Conda-compatible packages from public channels like conda-forge are also free for anyone to use.
• The default channel and Anaconda Distribution are free to use if:
• Your organization has fewer than 200 employees, or
• Your organization qualifies as exempt (e.g., students or educational entities using the software in curriculum-based courses).
• Organizations with 200 or more employees must purchase licenses to use the default channel and Anaconda Distribution unless they fall under an exemption.

Now that we have clarified the situation, are you still considering moving away from Anaconda? If so, keep reading!

Evaluating Moving Away from Anaconda

You may consider moving from Anaconda for different reasons, either because of the licensing (and cost) concerns explained above or because you are looking for a more lightweight option.

If that’s your case, here’s a framework for evaluating whether transitioning from Anaconda is viable. 

The course Data Science for Business can help you better understand the data science workflow from a high-level perspective, which is helpful if you’re a manager considering decisions like this one.

Assessing licensing costs

Data teams need to factor in licensing fees when budgeting for software and tools. However, teams can avoid these costs by switching to free channels like conda-forge, which may be suitable for most use cases that do not require the strict security and stability of the Anaconda defaults channel.

• Consideration: If your company handles sensitive data or operates in a highly regulated industry, you may prefer the security of Anaconda’s defaults channel, even if it comes at a cost. However, community channels like conda-forge can offer a cost-free alternative for teams working on less sensitive projects.
• Key question: Is your company size, budget, and industry aligned with Anaconda’s licensing model?
  • If your company has more than 200 employees, compare the cost of Anaconda licenses with free alternatives like Miniforge or Mamba, which use conda-forge.

Security and compliance risks

Teams in industries requiring strict security and compliance measures—such as healthcare, finance, or government—might need to continue using Anaconda’s defaults channel for guaranteed security and maintenance. 

While conda-forge and other community channels are open and widely trusted, they do not provide the same level of security oversight, making them less suitable for certain regulatory environments.

• Consideration: Evaluate your security requirements. If you are dealing with sensitive or regulated data, continuing to use the defaults channel with a paid license may be necessary to ensure compliance.
• Key question: Does your company handle sensitive data or operate in regulated environments?
• If security and regulatory compliance are top priorities, paid access to Anaconda’s defaults channel may be necessary to guarantee safe and stable package management.

Workflow and environment stability

Stability is key to maintaining smooth workflows for data teams. Anaconda Distribution and the defaults channel offer a well-curated set of packages maintained and tested by Anaconda engineers to ensure stability across environments. 

Switching to an alternative like conda-forge can introduce variability, as it’s community-maintained, but this trade-off is acceptable for many research and development scenarios.

• Consideration: Teams relying on consistent, stable environments should carefully test workflows before transitioning from the defaults channel. For production-level environments, particularly those with strict stability requirements, sticking with Anaconda Distribution may still be the best choice.
• Key question: Will migrating away from Anaconda cause significant workflow disruptions?
  • If your environments are complex and heavily reliant on Anaconda’s defaults channel packages, carefully evaluate the risks of migrating and thoroughly test alternatives before switching.

The course Developing Machine Learning Models for Production can help you better understand what it means to work with complex data science models in production environments. 

Flexibility and avoiding vendor lock-in

For teams concerned about vendor lock-in, moving away from Anaconda’s defaults channel and Distribution to tools like Miniforge and Mamba (which default to conda-forge) can offer more flexibility. 

By using community-driven channels, teams gain greater control over package management and avoid the long-term costs associated with Anaconda's commercial offerings.

• Consideration: If flexibility and cost-efficiency are priorities, shifting to open-source alternatives like Miniforge and Mamba will help avoid vendor lock-in while maintaining much of the same functionality.
• Key question: Does your team need the flexibility to customize environments or avoid vendor lock-in?
  • If your team values flexibility and the ability to freely manage environments, switching to Miniforge or Mamba can provide more customization options while avoiding the costs associated with Anaconda Distribution.

Collaboration and open-source ecosystem support

Many research institutions and universities contributing to the open-source community have historically used Anaconda Distribution. 

The licensing changes impact these institutions' ability to continue using the platform without paying for access. However, community channels allow data teams to continue contributing to open-source projects without financial barriers.

• Consideration: For teams involved in academic research or open-source development, transitioning to conda-forge will allow continued use of conda-compatible packages without licensing concerns while contributing to the broader open-source ecosystem.
• Key question: Is your team involved in open-source development or research projects?
• Moving away from the defaults channel can keep your workflows cost-free while supporting the open-source community if you focus on open-source contributions or academic research.

Migrating from Anaconda: Practical Steps

So, you’ve decided to migrate from Anaconda Distribution and the defaults channel. Now what? Software migrations can be scary, and for good reason. And even when carefully planned, you can expect at least some disruption.

In this section, I discuss the considerations you need to make when planning your migration and what it may look like practically. This guide aims to help you mitigate and minimize disruption to your data science workflows.

1. Audit your current environments

Before making any changes, it’s essential to audit your current environments to identify all the packages and dependencies you’re using. The goal is to create a smooth transition to new channels, mainly if any of your environments currently rely on Anaconda’s defaults channel.

• Export environment details: Use conda env export to create an environment.yml file that lists all the installed packages and dependencies in your existing environment:

conda env export > environment.yml

• Review your dependencies: Check the environment.yml file for any packages sourced from Anaconda’s defaults channel, as these may need to be replaced with equivalents from conda-forge.

2. Set up an alternative, like Miniforge or Mamba

We have written a complete guide on alternatives to Anaconda to help you navigate the options available. I recommend you take a moment to consider alternatives and select the best based on your specific needs.

In this section, I explain how you can switch to Miniforge or Mamba, lighter alternatives that default to the conda-forge channel.

• Miniforge: A minimal installer that sets conda to use the free and open-source conda-forge channel by default.
• Mamba: A faster alternative to conda, built on the same package management system but offering faster dependency resolution and environment creation.

Steps for Miniforge:

• Install Miniforge:

curl -LO https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh

For up-to-date and operating system specific instructions, consult the Miniforge repository.

• Configure conda-forge as the defaults channel:

conda config --add channels conda-forge
conda config --set channel_priority strict

Steps for Mamba:

• Install Mamba:

conda install mamba -n base -c conda-forge

For fresh installation instructions, check the mamba documentation.

3. Recreate your environments using conda-forge

Once you’ve installed Miniforge or Mamba (or any other alternative), the next step is to recreate your existing environments using conda-forge as the primary package source.

Conda-forge offers many packages, many of which have equivalents for those previously available from Anaconda’s defaults channel.

• Create the environment using your exported environment.yml file:
  • With Miniforge:

conda env create -f environment.yml

• • With Mamba:

mamba env create -f environment.yml

4. Address packages from Anaconda’s defaults channel

If your environments included packages from Anaconda’s defaults channel, you will need to replace these with equivalents from conda-forge or other community channels.

• Identify defaults channel packages: Check your environment.yml file for any dependencies sourced from the defaults channel.
• Install replacements from conda-forge:

conda install -c conda-forge <package_name>

If packages aren’t available in conda-forge, you may need to manually search for alternatives or install them using pip.

• Resolve conflicts: If any packages have version conflicts, adjust the versions in your environment.yml file or manually resolve the issues during installation.

5. Test your new environment

After recreating your environments with conda-forge or other alternatives, it’s essential to test them to ensure that all dependencies are installed correctly and your workflows remain functional. This is particularly important if you’re migrating production environments or critical workflows.

• Activate the new environment:

conda activate <environment_name>

• Run workflows: Test scripts, models, or applications that rely on the environment to ensure everything functions as expected.
• Debug any issues: If packages are missing or incompatible, search for alternatives or update the environment as needed.

6. Update CI/CD pipelines

If your team relies on Continuous Integration/Continuous Deployment (CI/CD) pipelines to automate workflows, you’ll need to update these scripts to reflect the new environment setup.

• Update CI/CD scripts: Replace any Anaconda-specific commands with Miniforge or Mamba equivalents.
• Recreate environments in CI pipelines: Ensure pipelines are set up to use conda-forge rather than the defaults Anaconda channel.
• Test the CI/CD pipelines: Run complete tests of your CI/CD processes to confirm that everything functions smoothly with the new environment.

7. Documentation and team training

Migrating to a new environment management system requires updating internal documentation and making sure team members are familiar with the new setup. 

Although Miniforge and Mamba operate similarly to conda, the new processes should be communicated to avoid confusion.

• Update internal documentation: Include instructions for environment creation, package installation, and managing dependencies using conda-forge.
• Conduct training sessions: Provide training for team members to help them get comfortable with the new tools and workflows.
• Monitor team adoption: Ensure all team members follow the updated processes, especially when working in shared environments.

Conclusion

Anaconda’s licensing changes have pushed some teams to explore alternative tools that offer more flexibility and cost savings. While the platform remains valuable for some companies and industries, open-source alternatives allow teams to manage their environments without added costs or licensing complexities.

Your team can maintain effective workflows without disruption by planning carefully, auditing your current setups, and transitioning to free, community-driven tools.

If you’re ready to expand your data science knowledge, make sure to check the following courses:

• CI/CD for Machine Learning
• Intermediate Python for Developers
• Understanding Modern Data Architecture