Control plane vs workload plane in model serving infrastructure

This post introduces and details the architectural separation of control and workload planes within Baseten's multi-cloud AI model serving infrastructure. It explains how a single Kubernetes control plane manages multiple, geographically distributed Kubernetes workload planes. The control plane is responsible for user interface, API endpoints, model image building, deployment, and global load balancing. Workload planes are responsible for running model inference on GPUs, pulling images, and local load balancing. This separation addresses challenges like regional preference, data residency, GPU availability, self-hosted inference, and scaling, while allowing workload planes to adapt to environment-specific factors like GPU type, location, and cost.