The premier infrastructure for hosting and sharing machine learning applications at scale.
Hugging Face Spaces serves as the definitive ecosystem for deploying and discovering machine learning applications in 2026. Architecturally, it functions as a git-integrated Git-to-Deployment pipeline that abstracts away the complexities of cloud orchestration and infrastructure management. Built on top of a robust Kubernetes-based backend, it supports native integration with Gradio, Streamlit, and Docker-based environments. The platform's market position is cemented by its 'ZeroGPU' infrastructure, which utilizes Nvidia A100/H100 clusters to provide short-burst high-performance compute to the community for free. For production workloads, it offers 'Upgrade to Hardware' options ranging from T4 GPUs to high-memory A100 instances. Its 2026 positioning emphasizes 'Collaborative AI Dev,' where teams can private-host internal tools using OAuth-protected spaces, persistent storage volumes, and seamless connections to the Hugging Face Hub's 2M+ models and datasets. It is the industry standard for rapid prototyping, research dissemination, and portfolio building for AI practitioners.
A serverless GPU infrastructure that allows Spaces to share a pool of Nvidia A100/H100 GPUs for transient tasks using a dynamic scheduler.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Network-attached storage volumes (up to several TBs) that persist data even when the Space instance restarts.
Full control over the container environment, allowing for custom OS packages, CUDA versions, and multi-service architectures.
An interactive VS Code environment running directly inside the Space hardware for real-time debugging.
Encrypted environment variable storage accessible by the runtime but hidden from public repository views.
Built-in support for Hugging Face Login, allowing apps to identify users and manage permissions.
Underlying Kubernetes infrastructure manages instance availability and sleep cycles based on traffic.
Comparing multiple models side-by-side without local hardware.
Registry Updated:2/7/2026
Collect user feedback via built-in flaggers.
Hosting a public interface for Diffusion models with high traffic.
Securely searching proprietary PDFs using RAG.