Hugging Face, Inc.
Hugging Face's mission is to democratize good machine learning through open source and open science. It operates a collaborative platform where people and organizations build, benchmark, share, version, train and deploy machine-learning models, datasets and applications.
At a Glance
- Individual developers and ML hobbyists
- Machine-learning researchers and academics
- Startups and AI-native companies
- Enterprise data-science and engineering teams
- +2 more
AI Tools by Hugging Face, Inc.
(9)
Hugging Face Transformers
Open Source Pretrained ML Library
Hugging Face MCP Server
Hugging Face Hub MCP Server
OpenEnv
Agentic RL Training Environments
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 Models and Datasets Hub
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
Hugging Face published 'One Year Since the DeepSeek Moment' and highlighted the launch of Serge, a GitHub-native AI code-review tool.
Hugging Face announced a new, deeper Google Cloud partnership for building AI with open models, including TPU access and model security collaboration.
Hugging Face launched AI Sheets, an open-source no-code tool for building, enriching and transforming datasets with AI models.
Hugging Face launched Trackio, an open-source experiment-tracking library with a local dashboard and Spaces integration.
Products & Services
The central hosting and collaboration platform for public and private models, datasets and ML applications (Spaces), with versioning, benchmarking, sharing and deployment capabilities.
A service for creating and deploying ML-powered demos and applications using Gradio, Docker or static HTML, with optional on-demand hardware.
Open-source library and Hub integration for accessing, processing, viewing and sharing datasets for machine-learning tasks.
Open-source model-definition framework for state-of-the-art text, vision, audio, video and multimodal models, supporting inference and training across the ML ecosystem.
Market Position
Hugging Face positions itself as the open, community-driven platform and collaboration layer for ML models, datasets and applications—the 'GitHub of machine learning.' Its differentiators are a large open ecosystem, model and dataset discoverability, open-source libraries, community governance and a path from experimentation to enterprise deployment. Competitors and adjacent alternatives include GitHub, Kaggle, Google Vertex AI, AWS SageMaker/Bedrock, Azure AI, Replicate, Weights & Biases, Anyscale and proprietary model platforms such as OpenAI and Anthropic.
Leadership
Founders
Clément Delangue
French entrepreneur and product leader; worked at Moodstocks and in product/marketing roles at several startups before co-founding Hugging Face. He is the co-founder and CEO.
Julien Chaumond
French engineer and technologist; previously worked as an engineer in France's Ministry of Economy and served as an adviser to the Deputy Minister for Digital Affairs. He is the co-founder and CTO.
Thomas Wolf
French machine-learning researcher and engineer who studied engineering with Chaumond and worked in research/engineering before co-founding Hugging Face. He is the co-founder and Chief Science Officer.
Executive Team
Clément Delangue
Co-founder and Chief Executive Officer
Former product/marketing leader at Moodstocks and other startups; co-founded Hugging Face in 2016.
Julien Chaumond
Co-founder and Chief Technology Officer
Engineer who previously worked in France's Ministry of Economy and advised the Deputy Minister for Digital Affairs.
Board of Directors
Founding Story
Hugging Face was started in New York in 2016 by Delangue, Chaumond and Wolf as a conversational AI/chatbot app aimed at teenagers, named after the hugging-face emoji. The founders open-sourced the technology behind the chatbot and pivoted toward an open platform for machine-learning models, datasets and applications—the vision that became the 'GitHub of machine learning.'
Business Model
Revenue Model
A freemium open-source platform: free public hosting and community access, paid Pro/Team subscriptions, paid storage and compute, usage-based Inference Providers and dedicated Inference Endpoints, automated training, hardware and enterprise support/governance offerings. Enterprise customers can buy private Hub deployments and support.
Pricing Tiers
Personal paid plan with additional Hub and inference capabilities.
Team collaboration plan.
Enterprise plan; enterprise support and larger/custom arrangements are also available.
Volume-based storage for models, datasets, Spaces and Buckets; egress and CDN are included.
Dedicated, secure, autoscaling inference infrastructure; price varies by cloud, CPU/GPU/accelerator and size.
Target Markets
- Individual developers and ML hobbyists
- Machine-learning researchers and academics
- Startups and AI-native companies
- Enterprise data-science and engineering teams
- Cloud and infrastructure providers
- Industries building NLP, computer vision, speech, robotics, biology, chemistry and generative AI applications
- Research and reproducible open science
- Training and fine-tuning language, vision, audio, video and multimodal models
- Generative AI application prototyping and demos
- Production model hosting and inference
- Dataset creation, enrichment, inspection and sharing
- Code generation and developer productivity
- Microsoft
- Amazon/AWS
- IBM