Hugging Face, Inc.
Hugging Face describes itself as the AI community building the future and is on a mission to democratize good machine learning. It provides an open platform where the machine-learning community collaborates on models, datasets, and applications.
At a Glance
- Individual developers and AI hobbyists
- Academic and industrial researchers
- Startups building AI products
- Large enterprises and Fortune 500 companies
- +3 more
AI Tools by Hugging Face, Inc.
(15)
safeagents
Local Proxy Blocking Risky Agent Tool Calls
Hugging Face AI Sheets
No Code AI Dataset Builder
Lighteval
Open Source LLM Evaluation Toolkit
agent-eval (is-it-agentic-enough)
Library Agent Friendliness Evaluator
Agent Manager from Huggingface
Browser Manager for Coding Agents
Hugging Face Datasets
Open Source ML Dataset Library
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
Discussions
SMHugging 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
safeagents repository reached its latest public commits, adding harness integrations and fixing SSE streaming; the project is an Apache-2.0 local safety proxy for AI coding agents.
Hugging Face announced native TorchTPU support and deeper use of Google TPUs for open-model workloads.
Published State of Open Source on Hugging Face: Spring 2026, reporting 13 million users, more than 2 million public models, and over 500,000 public datasets in 2025.
Announced a new and deeper Google Cloud partnership spanning repository delivery, Vertex AI, GKE, Cloud Run, TPUs, security, and Inference Endpoints.
Products & Services
The central collaborative repository and platform for discovering, versioning, sharing, and deploying open machine-learning models, datasets, and Spaces.
Open-source library and model ecosystem for pretrained transformer models across NLP, vision, audio, and multimodal tasks.
Open-source library and Hub repository tools for accessing, processing, streaming, and sharing machine-learning datasets.
Hosted interactive ML applications and demos, supporting Gradio, Streamlit, Docker, and optional CPU/GPU/ZeroGPU compute.
Market Position
Hugging Face is positioned as the open-source, community-centered hub and distribution layer for AI models, datasets, applications, and tooling—the GitHub-like collaboration layer for machine learning—combined with inference and deployment services. It competes and overlaps with model registries and platforms such as GitHub, Google Vertex AI/Model Garden, AWS SageMaker, Azure AI, Replicate, and TensorFlow Hub, while differentiating through its breadth of openly shared artifacts, cross-framework ecosystem, community, and vendor-neutral access to many models and inference providers.
Leadership
Founders
Clément Delangue
Co-founder and CEO. Previously worked with eBay and Mention and had an early machine-learning/computer-vision startup experience at Moodstocks, which was later acquired by Google; he studied management at ESCP Business School.
Julien Chaumond
Co-founder and CTO. French engineer and software developer; he worked on open-source/public-interest technology, including publishing French laws on Git, before co-founding Hugging Face.
Thomas Wolf
Co-founder and Chief Science Officer. Physicist with a Ph.D. in statistical and quantum physics from Sorbonne University; former patent attorney and creator/advisor of major open-source ML libraries including Transformers and Datasets.
Executive Team
Clément Delangue
Co-founder and Chief Executive Officer
Entrepreneur with previous experience at eBay, Mention, and Moodstocks; leads Hugging Face's business and mission.
Julien Chaumond
Co-founder and Chief Technology Officer
Engineer and open-source/public-interest technology developer; leads the company's technology direction.
Board of Directors
Founding Story
Hugging Face was started in New York in 2016 by Delangue, Chaumond, and Wolf as a chatbot app aimed at teenagers, named after the hugging-face emoji. After open-sourcing the model behind the chatbot, the founders pivoted toward an open platform for sharing and collaborating on machine-learning models and later broadened it to datasets, applications, and deployment.
Business Model
Revenue Model
A freemium platform monetized through PRO, Team, and Enterprise subscriptions; paid hosted compute and Spaces hardware; pay-as-you-go serverless Inference Providers; and dedicated, autoscaling Inference Endpoints. Enterprise customers also pay for private collaboration, security, support, governance, and deployment capabilities.
Pricing Tiers
Includes $2 of monthly inference usage and premium Hub/compute features.
Organization collaboration and billing features; Team or Enterprise organizations receive $2 monthly inference credits per seat.
Enterprise collaboration, security, governance, and support features.
Dedicated inference infrastructure priced by instance and usage.
On-demand CPU/GPU/ZeroGPU hardware with usage-based instance pricing.
Target Markets
- Individual developers and AI hobbyists
- Academic and industrial researchers
- Startups building AI products
- Large enterprises and Fortune 500 companies
- Cloud providers and hardware ecosystems
- Government, public-sector, and sovereign-AI initiatives
- Research and open science
- Training, fine-tuning, evaluation, and sharing of machine-learning models
- Dataset discovery, preparation, and distribution
- Rapid prototyping and demonstrations in hosted Spaces
- Generative-AI and ML inference in development and production
- Enterprise private model hosting, governance, security, and compliance
- Kustomer
- Google Cloud
- Microsoft Azure
- Amazon Web Services/Amazon SageMaker