ManufacturingThis week, Anaconda raised $150 million at a $1.5 billion valuation. The 12-year-old Python tooling provider, long considered infrastructure for data scientists, now reports over $150 million in annual revenue. Earlier this year, China-based DeepSeek released DeepSeek-R1, a dense mixture-of-experts language model trained on 2,788 GPUs: a setup estimated to be 96% cheaper than similar closed models. A few weeks after that, researchers from Stanford and the University of Washington trained a competitive small language model in 26 minutes on 16 GPUs, for a total cost of $50.
None of these companies are OpenAI, Google, or Anthropic. But taken together, they point to a shift: open-source AI tooling is taking over. It is performant, cost-effective, and increasingly the preferred stack for enterprise deployment. Proprietary APIs aren’t going away, but in vertical after vertical, enterprises loom to rebuild their AI foundations around open components they can inspect, and run on their own infrastructure.
Control, Not Just Cost
According to Anaconda’s State of Enterprise Open-Source AI report, 58% of organizations use open-source tools in at least half their AI/ML projects. One-third use them in three-quarters or more. This is a strategic decision by CTOs who want control over their most critical systems: how they’re built, what data they use, where they run, and what risks they carry.
