State of Open Source on Hugging Face: Spring 2026

This post examines how the open source AI landscape has shifted across competition, geography, technical trends, and emerging communities over the past year. We primarily examine community activity on Hugging Face across many types of metrics to give a holistic view of the ecosystem.

This post builds on an earlier analysis conducted mid-2025, available here, which examined what the Hugging Face Community is building. We recommend reading additional perspectives on the open source ecosystem in and outside of Hugging Face from the Data Provenance Initiative, Interconnects, OpenRouter and a16z, and MIT and the Linux Foundation. As the Hugging Face ecosystem is distributed, analyses are a combination of Hugging Face and community members' work, each of which is appropriately credited.

Activity in the open source AI ecosystem has rapidly grown, with the number of users, model, and dataset repositories all close to doubling. In 2025, Hugging Face grew to 13 million users, more than 2 million public models, and over 500,000 public datasets. This growth signals more than increased interest in open source; it reflects a shift toward active participation, with users increasingly creating derivative artifacts such as fine-tuned models, adapters, benchmarks, and applications rather than only consuming pre-trained systems.

Data from Hugging Face | Hugging Face's two million models and counting: Graph and story by AI World

The ecosystem remains highly concentrated. Approximately half of the models on Hugging Face have less than 200 total downloads, and the top 200 most downloaded models, or 0.01% of models, comprise 49.6% of all downloads.

Specialized communities form around particular domains, languages, or problem areas, and often show sustained engagement and reuse even when their overall download counts are modest. Open source AI is best understood as a collection of overlapping sub-ecosystems rather than a single uniform market.