Hugging Face

What does Hugging Face do?

Hugging Face runs a shared hub for models, datasets, and AI apps, with Spaces for demos and apps, Inference Providers for hosted model access, and Inference Endpoints for dedicated deployments. The platform is built around collaboration: teams can browse and publish assets, use the dataset viewer, and move from experimentation to production without leaving the ecosystem. Its public surface is large, with 2M+ models, 500k+ datasets, and more than 500k available apps. At scale, Hugging Face serves more than 50,000 organizations and supports more than 10,000 AI teams. The hub is Git-based, and the storage layer is optimized for large files and high-throughput uploads and downloads. The ecosystem also includes open-source building blocks like Transformers, Diffusers, Datasets, and Text Generation Inference. Named customers and users on the site include Google, Microsoft, Grammarly, Writer, and NVIDIA.

Why use Hugging Face?

• Its hub combines models, datasets, apps, and deployment options, so teams can move from experimentation to serving without stitching together separate tools.
• Git-based collaboration and large-scale public discovery make it easier to version assets and reuse community work.
• Inference Providers surface multiple providers in one place, which gives buyers a way to compare latency, context, and structured output support.
• Enterprise controls like SSO, audit logs, resource groups, and storage regions support teams with stricter governance needs.
• Storage Buckets and per-TB storage pricing make it practical to manage large model and dataset files in the same ecosystem.

Who is Hugging Face for?

• ML engineers who need a central hub for publishing and reusing models.
• Research teams who want to share datasets and papers with collaborators.
• Product teams who need to ship AI demos and apps through Spaces.
• Platform teams who need hosted inference and dedicated endpoints for production workloads.
• Enterprise admins who need access controls, auditability, and storage governance.

What are Hugging Face's key features?

What does Hugging Face integrate with?

• SSO
• VS Code
• Stripe
• novita
• together
• fireworks-ai
• deepinfra
• scaleway
• ovhcloud
• sambanova
• nscale
• groq
• cerebras
• hyperbolic
• Hugging Face Hub
• vLLM
• TGI
• SGLang
• TEI

What are Hugging Face's use cases?

How does Hugging Face work?

1. Connect your first Models, Datasets, or Spaces asset and import the repository you want to publish or reuse. Use the hub to organize files, metadata, and versions before sharing anything broadly.
2. Choose the right access and governance settings with Single Sign-On, Audit Logs, and Resource Groups. Assign permissions, review actions, and keep sensitive work separated by team or project.
3. Set up hosting or demos with Inference Providers, Inference Endpoints, or Spaces. Pick Advanced Compute Options when you need more capacity, then launch the app or endpoint for testing.
4. Store and manage files with Storage Buckets, Private Storage, and Regions. Keep data in the right location, control visibility, and use the built-in storage tools as projects grow.
5. Monitor usage with Analytics and adjust Token Management as teams expand. Revisit quotas, permissions, and deployment settings to keep models, datasets, and apps running smoothly.

How much does Hugging Face cost?

Frequently asked questions