Winning with AI isn’t about the fanciest models—it’s about solving infrastructure first

Joining Anaconda as Chief Product and Technology Officer deepened my appreciation for just how essential Python has become to the enterprise technology stack. Over the past year, I’ve witnessed organizations wrestling with a fundamental tension: The breakneck pace of AI advancement demands speed and experimentation, yet enterprise realities require security, governance, and reliability. From DeepSeek’s cost-efficient R1 model challenging industry assumptions to OpenAI’s o1 and o3 models pushing reasoning boundaries, 2025 proved that the AI revolution isn’t slowing down. What has become crystal clear through countless conversations with our customers is the most sophisticated AI capabilities mean nothing if teams can’t deploy them safely, consistently, and at scale.

This tension crystallized for me when I learned about Dr. Jun Ding’s team at McGill University. Using our platform, they identified a $10 hypertension drug that could potentially replace a $10,000-per-month treatment for idiopathic pulmonary fibrosis, a fatal lung disease with 100% mortality. Their breakthrough wasn’t just about the AI model they built; it was about having the foundation to iterate rapidly, reproduce results reliably, and deploy with confidence. The organizations winning with AI aren’t necessarily those with the biggest budgets or the fanciest models. They’re the ones who solved the unglamorous but critical infrastructure challenges first: dependency management, model governance, reproducible environments, and secure supply chains. As our customer base quadrupled to more than one million users last year, their message was unmistakable: Python is no longer just a tool for data scientists. It’s mission-critical infrastructure that spans from research teams to production systems, and the old approaches simply can’t keep pace.

Everything we’ve built this year has been in direct response to these challenges. We evolved the Anaconda Platform to deliver the unified foundation enterprises need by combining trusted distribution, simplified workflows, real-time insights, and governance controls in one place. Our partnership with Prefix.dev brought modern package management capabilities that accelerate developer workflows while maintaining the security guardrails IT teams require. But as we watched teams successfully deploy models into production, a new gap emerged: the explosion of open-source AI models created a different kind of infrastructure challenge. With more than 1.7 million models now available on platforms like Hugging Face, organizations face the same supply chain security risks with AI models that they once faced with Python packages. That’s why we launched AI Catalyst. Not as a pivot, but as a natural evolution of the same philosophy. Just as Core provides trusted, vetted Python packages with governance controls, AI Catalyst brings that same rigor to the AI model lifecycle, offering curated, optimized models with built-in security validation and organizational policy enforcement. Together, they address the full spectrum of what enterprises need to move from AI experimentation to AI production.

We’re not just transforming our platform, we’re restructuring how Python and AI gets done across enterprise teams

Introducing two purpose-built product suites

To address these evolving challenges, we’ve restructured Anaconda’s offerings into two focused product suites that work together to create a complete platform for Python and AI foundation:

Anaconda Core: Your trusted Python foundation, solving for dependency sprawl

For over a decade, we’ve been the backbone of enterprise Python, tackling what pip can’t: the complex world of Python’s dependencies that are critical for scientific computing and AI workloads. This product suite encapsulates what millions have relied on since our founding, which offers:

  • 50M users access to our defaults channel with the same trusted installers you’ve relied on for over a decade
  • Thousands of validated, secure packages with automated vulnerability assessment, detailed security reporting, and policy enforcement to ensure every package meets enterprise requirements
  • Advanced dependency resolution that prevents runtime conflicts between compiled packages, automatically handling installation and version control to eliminate the “dependency hell” that slows down data science teams
  • Complete air-gapped support with offline deployment capabilities, secure installers, and simple update mechanisms for regulated environments
  • Private package integration (self-hosted deployments) allowing you to host and share proprietary packages within your organization with the same compliance filtering and security guarantees as public packages
  • All packages securely built through Anaconda’s infrastructure by our internal team of experts

Whether you’re deploying on cloud, on-premises, containerized, or fully air-gapped environments, Anaconda Core provides the secure Python foundation that enterprises depend on for mission-critical AI and data science workloads.

AI Catalyst: Introducing an enterprise AI development suite to accelerate what you’re building next.

  • Made its debut at AWS re:Invent with our self-hosted model catalog as the inaugural offering
  • Delivers transparent, governed, and optimized AI development at scale
  • Bridges the gap between open-source innovation and enterprise requirements

Our launch of open-source models at re:Invent marks just the beginning. As we expand AI Catalyst over the coming months and years, we’re building toward a comprehensive platform that makes AI application development more accessible by centralizing the artifacts and workflows needed to build GenAI-powered applications. Our roadmap includes expanded inference capabilities, broader model support, curated templates for common use cases, and the entire ecosystem enterprises need to build, deploy, and govern AI applications with confidence.

Looking ahead: Our 2026 vision

The foundation we’ve built in 2025 enables us to tackle even bigger challenges. Our 2026 roadmap reimagines every aspect of the Python ecosystem: from how packages are distributed to how developers interact with their tools. We’re committed to making enterprise Python secure by default, eliminating the remaining friction between innovation and compliance, and ensuring these capabilities are accessible to every developer from day one.

Check out what the team is working on next →
See our VPs of Product share the detailed vision and initiatives for enterprise Python and AI.

Explore and build your Python and AI foundation today

Your teams are spending 30-40% of their time fighting dependency conflicts, vetting AI models, and building workarounds instead of delivering value. We’ve solved this. For over a decade, Anaconda has quietly powered Python data science for 50 million users and 95% of Fortune 100 companies. Now, with our platform’s product suites, Anaconda Core and AI Catalyst, we’re eliminating the impossible choice between enterprise innovation and security. You get the only platform that seamlessly manages binary dependencies at scale, curated production-ready models with built-in governance, and complete reproducibility from development to production.

This isn’t another tool to manage, it’s the foundation that replaces quick fixes with a secure and scalable infrastructure you can build on. The future of enterprise AI is having both innovation and control. And that future is here now.

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