Anaconda

Anaconda is the trusted foundation for AI-native development, empowering over 50 million developers, data scientists, and enterprises to discover, manage, and deploy open-source data science and machine learning packages. From the Anaconda Distribution to enterprise-grade solutions for package governance and security, the platform is where serious AI work gets built.

Documentation as a growth lever

For a company powered by product-led growth, documentation is not an afterthought. It is often the first thing an engineer or data scientist turns to when evaluating whether to adopt and recommend Anaconda to their organization. The quality of the docs directly shapes that decision.

"Good DX is crucial to our product-led growth motion," says the Anaconda documentation team. "For those engineers and data scientists who evaluate our tools and decide whether to adopt and recommend Anaconda to their company, docs are often the first thing they turn to."

A toolchain held together with duct tape

Before Mintlify, Anaconda's documentation workflow looked like this: write in reStructuredText, store files across multiple repos organized by product versioning, build HTML pages with a Sphinx theme, and publish through ReadTheDocs. The tooling was cheap, but the hidden costs were enormous.

The team calculated they were losing roughly 48 hours per month to troubleshooting alone. That figure accounts for the reality that debugging sessions often pulled in multiple writers, so three hours of troubleshooting between two people meant six hours of lost productivity. Broken builds interrupted workflows constantly. Site-wide changes required updating three separate repositories. And the problems compounded: domain name changes per repo meant repeated work, search functionality was, in the team's own words, "abysmal," and there was almost no way to track how users were actually engaging with the content.

Perhaps most telling: there was effectively no engineering support dedicated to documentation. If an engineer lent a hand, it was a favor, squeezed into whatever spare time they could find. The writing team had to become far more technically savvy than most technical writers would expect to need to be.

Why Mintlify

The team had evaluated other options, including Document360, but nothing offered a meaningful enough upgrade. When Mintlify was brought to the team's attention, the value proposition was immediately clear: a modern docs workflow that would solve the technical problems they were constantly firefighting, while also elevating the look and feel of the site.

The alternative was hiring a dedicated engineer just for the documentation team, but Mintlify proved to be the far more beneficial choice.

What changed

AI-native features keep Anaconda ahead of the curve. The team sees AI readiness as a matter of survival. "Adapt or get left behind," they say. Users increasingly rely on AI rather than reading documentation directly. Through features like llms.txt, llms-full.txt, and MCP servers, agents can pull Anaconda's documentation on their own or process it efficiently. The result is that people who might never have found Anaconda's docs are now getting agent-generated responses that include Anaconda's name, services, and links to their site. And for users who do visit the docs directly, the AI Assistant helps them find answers or get questions resolved on the spot.

The writing workflow got dramatically smoother. Local development with fast preview deployments means writers can see changes take effect immediately and share previews with anyone in the company. The day-to-day writing and review process went from fragile to fluid. No more broken builds interrupting work. No more updating the same change across three separate repos, a process that used to take two to three times longer than it should.

The team avoided a six-figure hire. Instead of bringing on an engineer to maintain documentation infrastructure, Mintlify handles that layer entirely. The savings in salary alone run into the hundreds of thousands per year, and the time the writing team previously spent troubleshooting is now spent on what actually matters: creating high-quality content.

Analytics went from zero to actionable. Before Mintlify, the team had virtually no insight into how users experienced the docs. Now they regularly work through quantitative and qualitative feedback from the Mintlify analytics dashboard. For the documentation manager, this means generating data-driven reports that tell the story of how docs are improving customer onboarding and adoption.

Search actually works. Mintlify's responsive search and embedded AI Assistant let developers get answers immediately rather than filing support tickets.

The contrast with the old world is stark. As the team puts it: "Without Mintlify, our workflow would be interrupted all the time by broken builds. Site-wide changes would take two to three times as long. We'd have hardly any insight into our users' experience. Our search would still be awful. And users wouldn't have the AI Assistant at their disposal, not to mention the other AI-native features that are keeping us up to snuff with the industry."

The bottom line

Anaconda turned documentation from a cost center riddled with technical debt into a modern, AI-native experience that actively drives product adoption. The numbers speak clearly: 48 hours a month of troubleshooting eliminated, hundreds of thousands saved by not hiring a docs engineer, and a documentation platform that now generates the kind of user insights the team never had before.

With Mintlify handling the infrastructure, Anaconda's writers do what they were hired to do: create the content that helps 50 million developers build with confidence.