Hugging Face

Operates AI model hub and inference API hosting 500,000+ models including Flux, Stable Diffusion, and others. Valued at $4.5B in 2023; Spaces platform allows developers to deploy model demos; critical distribution for open-weight models.

GitHub of AI models; hosts leaderboards defining model rankings; essential infrastructure for open-source AI ecosystem.

huggingface.co Infrastructure & APIs

United Statesprivate

At a glance

Total raised

$395.2M

last round series d

Headcount

501-1k

approximate · public range

Headquarters

New York

United States

Founded

2016

10 years active

Status

Private

Infrastructure & APIs

Latest valuation

$4.5B

Series D · 2023

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Funding history

Total raised

$395.2M

Rounds tracked

Latest stage

Series D

Latest valuation

$4.5B

Date	Stage	Amount	Valuation	Lead investors
Aug 22, 2023	Series D	$235M	$4.5B	Salesforce Ventures
May 9, 2022	Series C	$100M	$2B	Lux Capital
Mar 11, 2021	Series B	$40M	—	Addition
Dec 17, 2019	Series A	$15M	—	Lux Capital
May 23, 2018	Seed	$4M	—	A.Capital Ventures
Mar 1, 2017	Seed	$1.2M	—	Betaworks

Founder backgrounds

Clément Delangue

Co-founder & CEO

Studied political science and economics at Sciences Po Paris, then pursued management education at ESCP Business School (2008–2012) and Stanford University (2011–2012). Became one of France's top eBay sellers at age 17 before working in early-stage tech. First startup experience was at Moodstocks, building machine learning for computer vision, which was acquired by Google. Also held roles at VideoNot.es, eBay, Makesense, and Curioos before co-founding Hugging Face in 2016. Distinguished by a leadership style combining public transparency, mission-driven clarity, and deep respect for the developer community, outspoken about the need for AI to be open, inclusive, and responsibly developed.

Julien Chaumond

Co-founder & CTO

Studied applied mathematics at École Polytechnique and computer science at Télécom Paris, followed by a master's in electrical engineering and computer science from Stanford, where he worked as a research assistant. Co-founded and served as CTO at Glose, an educational reading platform, and worked as a software engineer at Paris-based startup Stupeflix before co-founding Hugging Face with Clément Delangue and Thomas Wolf in 2016. Pivotal in developing the Transformers Library and making large language models accessible and efficient for production use. Self-describes the platform as "the GitHub of machine learning", emphasizing a developer-first, community-centric approach to democratizing AI infrastructure.

Thomas Wolf

Co-founder & Chief Science Officer

Studied theoretical physics at École Polytechnique before earning a PhD from Sorbonne in quantum and statistical field theories. After research, switched to law and worked as a patent attorney for 5 years advising startups and companies, with most working on deep learning, which drew him back to machine learning. In 2016, co-founded Hugging Face with Clément Delangue and Julien Chaumond. Initially developed a chatbot app, but after releasing internal NLP tools as open source, pivoted to focus on the Transformers library, which became the default interface for working with models like BERT, GPT and T5. As Chief Science Officer, led the BigScience initiative, a year-long global research effort that culminated in BLOOM, one of the largest multilingual open-access language models. Leadership defined by creating open-source software that makes complex research, models, and datasets widely accessible.

AI bull / bear

Bull case

Hugging Face is the de facto GitHub for AI models, cementing its position as critical infrastructure for the open-source AI ecosystem. With over 500,000 models hosted—including flagship open-weight models like Flux and Stable Diffusion—it has become the distribution layer the entire industry depends on. The $4.5 billion valuation in 2023 reflects this strategic moat: developers default to the platform for discovery, deployment, and collaboration. The Spaces platform extends monetization beyond hosting into inference and demo deployment, capturing revenue as models move from research to production. Leaderboards hosted on Hugging Face define model rankings, giving the company editorial influence over which models gain traction. As open-source AI accelerates and proprietary labs face pressure to release weights, Hugging Face benefits from every new release. The platform effect is self-reinforcing—more models attract more developers, who contribute more models. With enterprises increasingly adopting open-weight models to avoid vendor lock-in, Hugging Face sits at the center of the fastest-growing segment of AI infrastructure.

Bear case

Hugging Face faces commoditization risk. Model hosting and inference APIs are undifferentiated services that cloud hyperscalers—AWS, Google Cloud, Azure—can bundle at marginal cost. These giants already offer managed AI services and have deeper enterprise relationships, superior compliance frameworks, and the ability to cross-subsidize. If AWS or Microsoft decides to prioritize open-weight model hosting, Hugging Face's distribution advantage erodes quickly. Monetization remains unproven at the $4.5 billion valuation. Hosting models is low-margin, and inference API pricing is under constant pressure from competitors like Replicate and Together AI. Many developers use Hugging Face for free, and converting them to paying enterprise customers is a slow, sales-heavy process. The leaderboard influence is soft power—it doesn't translate directly to revenue. Finally, the open-source model trend could reverse. If frontier labs like OpenAI and Anthropic maintain a significant capability lead with closed models, enterprises may prioritize proprietary APIs over self-hosted open weights, reducing demand for Hugging Face's infrastructure.

Generated by claude-sonnet-4-5-20250929 · May 1, 2026Refreshes quarterly · see disclosures

In the news

Watch

Leadership

Clément Delangue

Co-founder & CEO

in 𝕏

Julien Chaumond

Co-founder & CTO

Thomas Wolf

Co-founder & Chief Science Officer

in 𝕏

Products

Hugging Face Hub
A centralized platform and repository where users can discover, share, and collaborate on machine learning models, datasets, and demo applications. The Hub hosts 500,000+ open-source models including Flux, Stable Diffusion, and others across text, vision, audio, and multimodal domains. Users can upload models, datasets, and create interactive applications, enabling the global AI community to build together with version-controlled, Git-based infrastructure.
Transformers Library
An open-source Python library providing a unified framework for state-of-the-art pre-trained models across text, vision, audio, and multimodal tasks. It supports multiple frameworks (PyTorch, TensorFlow, JAX) and includes over 630,000 model checkpoints available on the Hub. Developers use the high-level pipeline API for simple inference or access lower-level APIs for fine-tuning and custom training across diverse machine learning tasks.
Inference API
A serverless inference service providing instant access to thousands of machine learning models for prototyping and production use. Users can run inference on text generation, image generation, embeddings, and other tasks with GPU acceleration, free tier for testing, and paid endpoints for production workloads. The API integrates seamlessly with the Hub ecosystem and supports both pay-as-you-go and dedicated inference endpoints.
Hugging Face Spaces
A deployment platform for building and hosting interactive machine learning applications with web interfaces. Users can upload demos, notebooks, or custom web apps built with frameworks like Gradio or Streamlit, and Hugging Face manages hosting with GPU acceleration options. The platform hosts over 1 million community-created demos, enabling researchers and developers to showcase models and build collaborative AI applications.

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Hugging Face

At a glance

Subscribe to see the full profile

Funding history

Founder backgrounds

Clément Delangue

Julien Chaumond

Thomas Wolf

AI bull / bear

In the news

Towards Speed-of-Light Text Generation with Nemotron-Labs Diffusion Language Models

Introducing the Ettin Reranker Family

Fine-Tuning NVIDIA Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation

The Open Agent Leaderboard

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

Unlocking asynchronicity in continuous batching

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Leadership

Products

Hugging Face Hub

Transformers Library

Inference API

Hugging Face Spaces

Open roles

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