Hugging Face is a technology company founded in New York in 2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf to develop open-source artificial intelligence (AI) technologies. The company initially gained prominence with its open-source "Transformers" library for natural language processing (NLP) and has since expanded into fields such as computer vision, speech, audio, multimodal systems, and agent-based architectures. As of 2025, the Hugging Face platform serves more than 5 million users and 215,000 enterprise participants worldwide.
HF Hub and Core Infrastructure
HF Hub (Hugging Face Hub) is a centralized collaboration platform for researchers and developers. Users can collaborate on models, datasets, and applications while benefiting from Git-based version control that enables model updates to be tracked. Libraries such as Transformers, Diffusers, Sentence Transformers, and Tokenizers are hosted on the Hub and are compatible with frameworks like PyTorch, TensorFlow, and JAX.
Inference Endpoints are fully managed infrastructure services that allow developers to deploy AI models into production. These models can run on dedicated CPUs (Central Processing Units), GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and AWS Inferentia 2 processors. Hugging Face offers access to these endpoints in three security tiers: public, protected, and private (accessible only via a Virtual Private Cloud – VPC).
Deployment and Security
Users can deploy models in four steps: (1) selecting the model (e.g., Transformers, Diffusers, or a custom model), (2) choosing a cloud provider and region (e.g., Europe, North America), (3) setting the security level (public, protected, or private), and (4) configuring options such as auto-scaling, log access, custom metric endpoints, and management via API (Application Programming Interface) or CLI (Command Line Interface).
Hardware and Pricing
The Inference Endpoints service starts at $0.03 per hour per CPU core and $0.50 per hour per GPU. For the Spaces feature (interactive apps), users can choose from various GPUs, including Nvidia T4, A10G, A100, L4, L40S, and H100. In addition to ephemeral storage, persistent storage options of 20 GB, 150 GB, and 1 TB are available at fixed monthly rates. Hugging Face also offers cost-effective features such as ZeroGPU (inference simulation without a GPU) and Dev Mode (Developer Mode).
Hugging Face provides a Pro plan for individual developers ($9/month) and an Enterprise Hub plan for corporate users ($20/user/month). The Pro account includes additional GPU credits, early access to features, and ZeroGPU access. The Enterprise Hub offers features such as SSO (Single Sign-On), SAML (Security Assertion Markup Language), data location selection, audit logs, resource groups, centralized token control, a private dataset viewer, and priority support.
Applications and Use Cases
The Inference Endpoints service is used by companies such as Musixmatch, Pinecone, Waymark, Sempre Health, Rocket Money, and Witty Works for tasks like natural language processing, speech recognition, and custom embedding generation. Hugging Face collaborates with major technology companies, including Amazon, Meta, Microsoft, Google, IBM, NVIDIA, Intel, and Grammarly. The platform is compliant with SOC 2 (Service Organization Control 2) and GDPR (General Data Protection Regulation), aligning with enterprise-grade security standards.
Future Outlook
Hugging Face aims to further democratize the open-source AI ecosystem in the coming years. It is continuing to develop technologies such as ZeroGPU and quantization to enable the operation of large models on low-cost hardware. The company is also exploring new solutions in multimodal architectures, personal agents, and customized enterprise models. Hugging Face’s strategic objective is to provide a secure, transparent, and high-performance open infrastructure for the training and inference of generative AI, supporting both academic and commercial use cases.
