Hugging Face just closed a $100 million Series C led by Lux Capital, with Sequoia and Coatue joining as major participants. The round also pulled in existing backers like Addition, a_capital, and a handful of angels including Kevin Durant and Datadog CEO Olivier Pomel. For a company that started by open-sourcing PyTorch BERT in 2018, the trajectory has been steep — and the timing feels deliberate.
The platform now hosts 100,000 pre-trained models and 10,000 datasets spanning NLP, computer vision, speech, time-series, biology, reinforcement learning, and chemistry. More than 10,000 companies are building on top of it. That’s not a community anymore; that’s infrastructure. Hugging Face has effectively positioned itself as the GitHub of machine learning, and this raise suggests investors agree that the default path for AI development runs through open collaboration rather than walled gardens.
The company is also steering BigScience, a research workshop with over 1,000 researchers working on what it calls the world’s largest open-source multilingual language model. That project matters because it directly challenges the assumption that only well-funded private labs can train frontier-scale models. Hugging Face isn’t just talking about democratizing AI — it’s training a massive model in the open, with researchers from different disciplines and institutions contributing.
The team has grown from 30 to over 120 people in the past year, adding Dr. Margaret Mitchell and the Gradio team along the way. Hugging Face says the new capital will go toward research, open-source work, products, and what it calls “responsible democratization of AI.” There’s still plenty to be skeptical about — open-source doesn’t automatically solve bias, privacy, or energy consumption, and the company acknowledges those challenges explicitly. But $100 million is a serious signal that the open approach isn’t just viable; it’s becoming the dominant one. The question now is whether Hugging Face can maintain its community-driven ethos while scaling to meet the demands of thousands of enterprise customers.
