What is a Hugging Face Space?

A Space is a hosted, shareable web app — typically built with Gradio, Streamlit, or Docker — that demonstrates a machine learning model or tool directly in the browser.

Is Hugging Face Spaces free?

Yes, Spaces offers a free tier with shared CPU or basic GPU hardware; paid tiers provide dedicated, more powerful GPUs for demos that need it.

Can I use my own model in a Hugging Face Space?

Yes. Spaces integrate directly with the Hugging Face Hub, so you can load your own uploaded models or public community models into a Space.

What frameworks can I build a Space with?

Most Spaces use Gradio or Streamlit for quick UI building, but Docker-based Spaces support essentially any web framework.

AI Tools

Hugging Face Spaces

By Hugging Face

BeginnerPlatform4.9K learners

Hugging Face Spaces is a hosting platform for sharing and running interactive machine learning demo applications directly in the browser, using frameworks like Gradio or Streamlit, backed by Hugging Face's model and dataset ecosystem.

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Definition

Hugging Face Spaces is a hosting platform for sharing and running interactive machine learning demo applications directly in the browser, using frameworks like Gradio or Streamlit, backed by Hugging Face's model and dataset ecosystem.

Overview

Hugging Face Spaces lets developers publish a working AI demo — a chatbot, image generator, or model comparison tool — as a live web app, without managing their own servers. A Space is essentially a small Git repository that Hugging Face automatically builds and runs, most commonly using Gradio or Streamlit for the UI layer, though Docker-based Spaces allow essentially any web application. Spaces are tightly integrated with the rest of the Hugging Face ecosystem: a demo can pull a model directly from the Hugging Face Hub, reference a dataset, and be embedded elsewhere via an iframe. This makes Spaces the de facto place the open-source ML community shares reproducible demos of new models, papers, and techniques — when a new open-weight diffusion model or LLM is released, a Space showcasing it typically appears within days. Free Spaces run on shared CPU or basic GPU hardware with usage limits, while paid tiers offer dedicated, more powerful GPUs for demos that need faster inference or handle more traffic. This tiered model mirrors platforms like Civitai in the image-generation community, where free community hosting coexists with paid compute for heavier workloads. Because Spaces are so easy to fork and modify, they also serve an educational purpose: a student can duplicate an existing Space, tweak the code, and immediately see the effect, making it a popular hands-on complement to courses like Hugging Face Transformers.

Key Features

Free hosting for interactive ML demos built with Gradio, Streamlit, or Docker
Direct integration with Hugging Face Hub models and datasets
One-click forking and duplication of existing demo apps
Embeddable demos via iframe for blogs and documentation
Optional paid GPU tiers for faster or higher-traffic demos
Central showcase venue for new open-source model releases

Use Cases

Sharing a live, interactive demo of a newly released open-source model

Prototyping and testing an ML application's UI before building a custom frontend

Learning by forking an existing Space and modifying its code

Comparing multiple models or checkpoints side by side in a browser

Hosting lightweight internal tools for a research or engineering team

Frequently Asked Questions

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