Hugging Face is the leading open-source AI community platform, hosting over 500,000 models, 100,000 datasets, and 200,000 Spaces (demo applications) across the machine learning ecosystem. The platform provides the Transformers library, Inference API for serverless model deployment, Inference Endpoints for dedicated hosting, and Spaces for deploying Gradio and Streamlit ML demos. Hugging Face has become the de facto hub for sharing and discovering AI models, serving both individual researchers and enterprises building AI-powered applications. The platform supports models across NLP, computer vision, audio, and multimodal domains.
Hugging Face outages commonly involve the Inference API infrastructure that serves model predictions, Hub storage backend issues affecting model and dataset downloads, and Spaces container scheduling failures. GPU availability constraints for Inference Endpoints and Spaces cause queuing and timeouts during peak demand. Large model downloads (multi-gigabyte files) are susceptible to CDN distribution failures. The Git LFS infrastructure that stores model weights can experience performance degradation during release events when popular models are published.
When Hugging Face goes down, AI researchers and developers lose access to model downloads, breaking training pipelines and deployment workflows. The Inference API stops serving predictions for applications using serverless model hosting. Spaces demos become unreachable, disrupting presentations and product showcases. Companies using Hugging Face for production model serving via Inference Endpoints experience direct application failures. The open-source AI ecosystem relies heavily on Hugging Face as infrastructure, so outages have broad community impact.
Use this page to check Hugging Face availability. The Hub, Inference API, and Spaces can experience independent outages. Check status.huggingface.co for official per-component status. If model downloads fail but the website loads, it may be a Git LFS or CDN issue rather than a full platform outage.
Model downloads can fail due to Hub outages, Git LFS storage issues, CDN distribution problems, or network connectivity. Large models (multiple gigabytes) are more susceptible to timeout failures. Try using 'huggingface-cli download' with resume support, or download from a mirror. Check if the issue affects all models or just specific repositories.
Dedicated Inference Endpoints run on provisioned GPU infrastructure and may continue operating during Hub outages if the model is already loaded. However, scaling events, new deployments, and endpoint management require the Hub API. Serverless Inference API is more likely to be affected during platform-wide incidents.
Yes, the Transformers library caches downloaded models locally by default in ~/.cache/huggingface/. Once a model is cached, it loads from disk without Hub connectivity. For CI/CD pipelines, pre-cache required models to avoid build failures during Hub outages. Use TRANSFORMERS_CACHE environment variable to control the cache location.
Monitor Hugging Face Hub and your Inference Endpoints with PinusX Uptime Monitor for free. Get Slack and email alerts within 60 seconds when services become unavailable. This is essential for production AI pipelines where model availability directly impacts application functionality.
Monitor Hugging Face uptime with PinusX. Get instant alerts when services go down.