HuggingFace Spaces
What is HuggingFace Spaces?
HuggingFace Spaces is a platform for AI builders that turns model demos and AI apps into shareable web experiences. It includes Image Generation, Text Generation, OCR, Chatbots, and Advanced Compute Options, and ties into the Hugging Face Hub and inference tooling. Teams like TencentARC, Google, and Mistral AI use it. Plans run PRO Account $9/month, Team $20/user/month, and Enterprise $50/user/month.
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At a glance
- Hugging Face Spaces is best for AI builders who want to publish and share model demos quickly.
- PRO Account $9/mo; Team $20/user/mo; Enterprise $50/user/mo; Hugging Face Hub free
What does HuggingFace Spaces do?
Hugging Face Spaces turns model demos and AI apps into shareable web experiences, with a gallery-style directory, launch flow, and hosting options that let creators publish quickly and iterate in public. The page surfaces task categories like Image Generation, Text Generation, OCR, Chatbots, and Model Benchmarking, so users can browse by use case rather than infrastructure. Spaces also ties into the broader Hugging Face ecosystem, including the Hugging Face Hub and inference tooling, so apps can sit close to models and datasets instead of living in a separate stack. At scale, the directory spans 1.3M Spaces and more than 500k available apps, with examples from teams like TencentARC, Google, and Mistral AI. The platform also offers ZeroGPU Spaces, Dev Mode, and hardware choices ranging from free CPU Basic to GPU instances, while the surrounding platform adds storage, analytics, and managed deployment paths for larger teams.
Why use HuggingFace Spaces?
- It combines app discovery and hosting, so builders can publish and browse AI demos in one place.
- The Hugging Face ecosystem connection keeps apps close to models, datasets, and inference tooling.
- ZeroGPU Spaces and on-demand hardware let teams start small and scale compute when needed.
- Public sharing makes it easier to gather feedback on prototypes without packaging a separate deployment.
- Enterprise controls like SSO, audit logs, and resource groups support larger organizations.
Who is HuggingFace Spaces for?
- ML engineers who want to ship interactive demos without building a separate frontend stack.
- Research teams who need a public place to show experiments and benchmark results.
- Product teams who want to prototype AI features with shareable, browser-based apps.
- Open-source maintainers who want an easy way to distribute runnable model experiences.
What are HuggingFace Spaces's key features?
Image Generation
Create images from text prompts across Hugging Face Spaces, with outputs shared through the Hugging Face Hub for easy publishing and reuse.
Video Generation
Generate video content in Spaces workflows, letting teams prototype media apps and distribute them through the Hugging Face Hub.
Text Generation
Run text generation apps on Spaces with support for vLLM, TGI, and SGLang, which helps teams serve chat and completion experiences.
Speech Synthesis
Build voice apps that synthesize speech in Spaces, using Hugging Face Hub models to ship audio experiences without managing separate infrastructure.
OCR
Extract text from images and documents in Spaces, supporting document workflows that pair OCR with Hugging Face Hub-hosted models.
Chatbots
Deploy chatbot apps on Spaces and connect them to Hugging Face Hub models, making it easier to publish conversational demos and assistants.
Single Sign-On
Control access with SSO, including SAML and OIDC in Team plans, so organizations can manage user login centrally.
Advanced Compute Options
Use advanced compute options for Spaces, including ZeroGPU Spaces and Dev Mode, to run heavier apps with more flexible execution.
What does HuggingFace Spaces integrate with?
- SSO
- VS Code
- Hugging Face Hub
- vLLM
- TGI
- SGLang
- TEI
What are HuggingFace Spaces's use cases?
ML demos without frontend work
ML engineers who want to ship interactive demos without building a separate frontend stack use HuggingFace Spaces to publish browser-based apps fast. They lean on Chatbots and Text Generation to let reviewers try the model immediately, then use Advanced Compute Options when the demo needs heavier inference.
Benchmark showcases for research teams
Research teams who need a public place to show experiments and benchmark results use HuggingFace Spaces to turn findings into shareable pages. They combine Model Benchmarking with Data Visualization so visitors can compare outputs in the browser, and add OCR or Visual QA when the experiment needs richer multimodal proof.
Prototype AI features for product teams
Product teams who want to prototype AI features with shareable, browser-based apps use HuggingFace Spaces to test ideas with stakeholders before a full build. They use Image Generation or Speech Synthesis to demo the experience, then rely on Single Sign-On for controlled internal access.
Runnable model experiences for maintainers
Open-source maintainers who want an easy way to distribute runnable model experiences use HuggingFace Spaces to package a project into something anyone can open and try. They use Text Generation and Chatbots to make the repo feel alive, while Advanced Compute Options helps keep the experience usable as traffic grows.
How does HuggingFace Spaces work?
- Connect your first model or dataset from the Hugging Face Hub, then open a new Space and choose the app type that matches your demo.
- Build the interface around Text Generation, Image Generation, or Chatbots, and wire the app logic directly into the Space so visitors can interact in the browser.
- Tune runtime settings with Advanced Compute Options or ZeroGPU Spaces when your demo needs more capacity, faster queues, or a smoother launch under load.
- Share the Space publicly or restrict access with Single Sign-On, then use Resource Groups and Audit Logs to manage who can view and edit it.
- Keep iterating from Dev Mode, publish updates without git overhead, and watch Analytics to see which demos attract users and where they drop off.
How much does HuggingFace Spaces cost?
PRO Account
$9per month- 2× public storage capacity
- 20× included inference credits
- 8× ZeroGPU quota and highest queue priority
- Spaces Dev Mode & ZeroGPU Spaces hosting
- Personal blog publishing
- Dataset Viewer for private datasets
- Show your support with a PRO badge
Team
$20per user per month- SSO support (SAML & OIDC)
- Data location control with Storage Regions
- Detailed action reviews with Audit Logs
- Granular access control via Resource Groups
- Repository usage Analytics
- Centralized token control and approvals
- Dataset Viewer for private datasets
- Compute options for Spaces
- All organization members get ZeroGPU and Inference Providers PRO benefits
Enterprise
$50per user per month- All benefits from the Team plan
- Highest storage, bandwidth and API rate limits
- Automated user management with SCIM provisioning
- Security and access controls
- Managed billing with annual commitments
- Legal and Compliance processes
- Dedicated support
Data Storage: Base
$12/TB/mo- Public repositories
- $18/TB/mo
- Private repositories
Data Storage: 50TB+20% off
$10/TB/mo- Public repositories
- $16/TB/mo
- Private repositories
Data Storage: 200TB+25% off
$9/TB/mo- Public repositories
- $14/TB/mo
- Private repositories
Data Storage: 500TB+33% off
$8/TB/mo- Public repositories
- $12/TB/mo
- Private repositories
Hugging Face Hub
free- Join the open source Machine Learning movement!
Frequently asked questions
What is HuggingFace Spaces?
HuggingFace Spaces is a platform for AI builders that turns model demos and AI apps into shareable web experiences. It includes Image Generation, Text Generation, OCR, Chatbots, and Advanced Compute Options, and ties into the Hugging Face Hub and inference tooling. Teams like TencentARC, Google, and Mistral AI use it. Plans run PRO Account $9/month, Team $20/user/month, and Enterprise $50/user/month.
How much does HuggingFace Spaces cost? Is it free?
HuggingFace Spaces has a free plan, with paid tiers including PRO Account at $9per month, Team at $20per user per month, Enterprise at $50per user per month.
What is HuggingFace Spaces used for? Who is it for?
HuggingFace Spaces is used for Image Generation, Video Generation, and Text Generation. It's built for ML engineers, Research teams, and Product teams.
Does HuggingFace Spaces have an API and what does it integrate with?
HuggingFace Spaces doesn't publish a public API. It integrates with SSO, VS Code, Hugging Face Hub, vLLM, TGI, and 2 more.
Editor's read
Check whether your deployment needs Team or Enterprise controls before publishing. SSO, audit logs, resource groups, and storage region controls are tied to higher tiers, so access and compliance requirements can change the plan you need.
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