cloudpods

Why we included this project

Cloudpods manages whatever infrastructure you already have: KVM hosts and baremetal in your own rack, plus resources in public cloud accounts on AWS, Azure, GCP, and elsewhere, all exposed through one set of APIs that hide the differences between providers. It's a practical answer for teams that want to virtualize a few physical servers into a private cloud or automate baremetal provisioning without building out a heavier stack, and it doubles as a hybrid-cloud control plane when you need one view of resources spread across public and private clouds. The AI Cloud layer extends the same platform to LLM inference and GPU-backed AI container applications, with a model library that handles versioning and offline distribution. For enterprises running mixed workloads, that means operating their private cloud and their AI serving from the same place rather than stitching together separate tools.