Hugging Face Spaces is a platform for hosting and sharing interactive machine learning demos and applications — supporting Gradio (auto-generated UI from Python functions), Streamlit (data dashboards), and Docker (any custom application), with free CPU hosting and paid GPU tiers (A10G at $1.05/hr, A100 at $4.13/hr), making it the easiest way to turn any trained ML model into a publicly accessible, interactive web application that anyone can try without installation.

What Is Hugging Face Spaces?

• Definition: A hosting platform (huggingface.co/spaces) that deploys ML applications from a Git repository — automatically detecting the framework (Gradio, Streamlit, or Docker), building the environment, and serving the application at a public URL.
• The Problem: You trained a great model. Now what? Sharing a .pkl file or a Colab notebook isn't useful for non-technical stakeholders. They need to click a button, upload an image, and see the result.
• The Solution: Spaces provides free hosting for interactive demos. Write a 10-line Gradio app, push to Spaces, and share a URL. Your manager, client, or the world can interact with your model instantly.

Supported Frameworks

Hardware Tiers

Gradio Example (10 lines)

import gradio as gr
from transformers import pipeline

classifier = pipeline("image-classification", model="google/vit-base-patch16-224")

def classify(image):
    results = classifier(image)
    return {r["label"]: r["score"] for r in results}

demo = gr.Interface(fn=classify, inputs="image", outputs="label")
demo.launch()

Popular Spaces

Hugging Face Spaces is the standard platform for sharing ML demos — providing free hosting for Gradio, Streamlit, and Docker applications with optional GPU hardware, enabling anyone to turn a trained model into an interactive web application accessible via a public URL in minutes.