5Hugging Face Blog·
SkyPilot and Hugging Face integrate for zero-egress cloud storage across AI workloads
Hugging Face and SkyPilot have announced an integration enabling AI workloads to run on any cloud provider while storing data and model artifacts on Hugging Face Hub with zero egress fees. The partnership addresses a common pain point in multi-cloud AI infrastructure where data transfer costs create vendor lock-in. This is relevant for practitioners running distributed training or inference across cloud providers.
Related guides (3)
Related events (8)
Hugging Face and AWS Partner to Make AI More Accessible
Hugging Face announced a strategic partnership with Amazon Web Services to expand access to AI models and tools. The collaboration aims to integrate Hugging Face's model hub and libraries more deeply with AWS infrastructure and services. This represents a significant enterprise deployment and cloud distribution move for the open-source AI ecosystem.
Hugging Face and Google Partner for Open AI Collaboration
Hugging Face and Google have announced a partnership focused on open AI collaboration, expanding access to Hugging Face models and tools on Google Cloud Platform. The deal deepens integration between Hugging Face's model hub and Google's cloud infrastructure, enabling easier deployment of open-source models via GCP services. This follows a pattern of major cloud providers forming strategic alliances with leading open-source AI platforms.
Hugging Face and Google Cloud Announce New Partnership
Hugging Face has announced a new partnership with Google Cloud, framed around building an open AI future. The blog post outlines collaboration between the two organizations, though the body content is not provided. This partnership likely involves deeper integration of Hugging Face's open-weights model hub and tooling with Google Cloud's infrastructure and services.
SkyPilot: multi-cloud AI workload orchestration framework trending on GitHub
SkyPilot is an open-source Python framework for running, managing, and scaling AI workloads across Kubernetes, Slurm, 20+ cloud providers, and on-premises infrastructure through a single unified interface. The project has accumulated over 10,000 GitHub stars with 49 new stars today, indicating sustained practitioner interest. It addresses the multi-cloud and hybrid infrastructure management problem common in production AI deployments.
Hugging Face and FriendliAI Partner to Supercharge Model Deployment on the Hub
Hugging Face and FriendliAI have announced a partnership to integrate FriendliAI's inference infrastructure directly into the Hugging Face Hub. The collaboration aims to simplify and accelerate model deployment for developers accessing models through the Hub. This expands the ecosystem of inference providers available on Hugging Face's platform.
Featherless AI Joins Hugging Face Inference Providers
Hugging Face has added Featherless AI as a new inference provider in its Inference Providers ecosystem. Featherless AI specializes in serverless inference for open-weight models, expanding the range of third-party compute options available through the Hugging Face platform. This integration allows developers to route model inference requests to Featherless AI directly via the Hugging Face API and model hub.
Hugging Face Launches Inference Providers on the Hub
Hugging Face has introduced Inference Providers on the Hub, a feature that allows users to run models hosted on the Hub through third-party inference providers directly from the platform. This integration consolidates access to multiple inference backends under a unified interface, reducing friction for developers who want to deploy or test models at scale. The announcement positions Hugging Face as a marketplace layer connecting model authors with inference infrastructure providers.
Hugging Face and Microsoft Deepen Collaboration: Cloud to Developers
Hugging Face and Microsoft announced an expanded collaboration integrating Hugging Face's model hub and tools more deeply into Microsoft Azure and developer workflows. The partnership extends existing cloud integrations to make open-weight models and ML tooling more accessible via Azure infrastructure. This represents a continued strategic alignment between the leading open-source ML platform and Microsoft's cloud ecosystem.