Hugging Face, Inc.
To democratize good machine learning, one commit at a time. Hugging Face is building the world's largest open-source AI community and platform for machine learning models, datasets, and applications.
At a Glance
- AI researchers and academics
- Machine learning engineers and data scientists
- Enterprise organizations (Fortune 500 companies)
- Startups and indie developers
- +10 more
AI Tools by Hugging Face, Inc.
(6)smolagents
Lightweight Python AI Agent Library
Hugging Face Learn
Free AI and ML Courses
Sentence Transformers
Python Sentence Embedding Library
Hugging Face Chat
Browser Chat for Hugging Face Models
Transformers.js
Browser ML Library for JavaScript
Hugging Face
ML Model Hub and Deployment
Discussions
Hugging Face: Democratizing NLP and Transformers
Hugging Face has become a central hub for NLP models and datasets. How are you leveraging their platform and tools in your projects?
Latest News
Introducing Daggr: Chain apps programmatically, inspect visually
One Year Since the DeepSeek Moment - analyzing China's open-source AI ecosystem
Hugging Face Lays Off 4% of Staff (approximately 10 people from sales team)
Hugging Face Acquires Pollen Robotics to Sell Open-Source Humanoid Robots
Products & Services
Open-source library for PyTorch, TensorFlow, and JAX providing pretrained state-of-the-art models for natural language processing, computer vision, and audio/speech processing tasks. Supports models like BERT, GPT, DistilBERT, and thousands more.
Central platform for hosting, sharing, and collaborating on machine learning models, datasets, and applications. As of January 2026, hosts over 2.4 million models and 730,000+ datasets.
Library that makes sharing and accessing datasets easier for machine learning workflows.
Open-source library for creating interactive machine learning demos and user interfaces with just a few lines of code. Powers Hugging Face Spaces.
Market Position
Hugging Face positions itself as the 'GitHub of machine learning' and the world's largest open-source AI community. Unlike proprietary, vertically integrated competitors like OpenAI and Anthropic, Hugging Face operates as an open, neutral ecosystem that hosts many different model families and open-source tools. The company differentiates through a grassroots, community-first approach where democratization of AI is core to its mission. With over 5 million users, 12 million total users reported in some sources, and 2.4 million models (January 2026), it has become the go-to hub for open-source AI builders. The platform emphasizes openness, collaboration, ethics, and making AI accessible to everyone rather than building closed systems. Hugging Face is described as potentially a bigger player than OpenAI or Anthropic in terms of community size and open model ecosystem.
Leadership
Founders
Clément Delangue
Co-founder and CEO. From La Bassée, Northern France. Previously worked at Moodstocks (acquired by Google), eBay, and co-founded VideoNot.es and UniShared. Studied at ESCP Business School with additional studies at Stanford University and Indian Institute of Management Bangalore. Has product and marketing expertise.
Julien Chaumond
Co-founder and CTO. Computer engineer and elite mathematician. Previously worked at France's Ministry of Economy and Finance. Co-founded Glose. Studied electrical engineering and computer science at Stanford University and École Polytechnique. Met the other founders through an online Stanford engineering class.
Thomas Wolf
Co-founder and Chief Science Officer (CSO). PhD in Statistical and Quantum Physics from Pierre and Marie Curie University. Former European Patent Attorney at Cabinet Plasseraud and research intern at Lawrence Berkeley National Laboratory. Trained scientist turned patent lawyer who transitioned to AI/ML.
Executive Team
Clément Delangue
Co-Founder and CEO
ESCP Business School, Stanford University, Indian Institute of Management Bangalore. Previously at Moodstocks (Google), eBay. Co-founded VideoNot.es and UniShared.
Julien Chaumond
Co-Founder and CTO
Stanford University, École Polytechnique. Former advisor at French Ministry of Economy and Finance. Co-founder of Glose.
Board of Directors
Founding Story
Hugging Face was founded in 2016 in New York City by three French entrepreneurs who met through an online Stanford engineering class and study group. They originally aimed to build a fun, open-domain conversational AI chatbot targeted at teenagers, named after the hugging face emoji. The pivotal moment came in late 2018 when Google released the BERT model. Hugging Face produced and open-sourced a PyTorch implementation within a week, which gained massive community traction. This success led the company to formally pivot in 2019 from the consumer chatbot to becoming an open-source hub and infrastructure platform for machine learning. The company was built on a philosophy of democratizing AI and making advanced ML accessible to everyone.
Business Model
Revenue Model
Freemium and usage-based business model with multiple revenue streams: (1) Subscription revenue from PRO accounts ($9/month) and per-user seat licenses from Team ($20/user/month) and Enterprise plans ($50+/user/month); (2) Consumption-based infrastructure (IaaS) revenue from on-demand compute usage for hosting ML applications (Spaces) and deploying production models (Inference Endpoints); (3) Storage revenue based on volume (per TB) and repository visibility; (4) Enterprise services including expert support, legal/compliance features, and personalized support.
Pricing Tiers
Central platform for exploring, experimenting, collaborating, and building ML technology. Includes model evaluation, dataset viewer, and Git-based collaboration.
10x private storage capacity, 20x included inference credits, 8x ZeroGPU quota with highest queue priority, Spaces Dev Mode, ZeroGPU Spaces hosting, ability to publish blog articles, dataset viewer for private datasets, and a Pro badge.
SSO/SAML support, storage regions selection, audit logs, resource groups for access control, repository usage analytics, auth policies, centralized token control, private dataset viewer. All members receive ZeroGPU and Inference Providers PRO benefits.
All Team plan benefits plus highest storage, bandwidth, and API rate limits, managed billing with annual commitments, legal and compliance processes, and personalized support.
Managed storage for ML models and datasets. Volume discounts available at 50TB+, 200TB+, and 500TB+ tiers.
On-demand compute for hosting ML applications. Ranges from CPU Basic (Free) to Nvidia A100 8x ($20/hour) and L40S 8x ($23.50/hour). ZeroGPU available for free with dynamic usage.
Dedicated deployment service across AWS, Azure, GCP. CPU instances ($0.01-$0.54/hour), Accelerator instances ($0.75-$12/hour), GPU instances ($0.50-$80/hour) supporting NVIDIA T4, L4, L40S, A10G, A100, H100, H200, B200.
Target Markets
- AI researchers and academics
- Machine learning engineers and data scientists
- Enterprise organizations (Fortune 500 companies)
- Startups and indie developers
- Healthcare and pharmaceuticals
- Finance and banking
- Natural language processing (NLP)
- Computer vision
- Audio and speech processing
- Text generation and chatbots
- Image generation and editing
- Sentiment analysis
- Intel
- Pfizer
- Bloomberg
- eBay