Control Plane vs Data Plane in an AI Platform
Control plane and data plane describe two orthogonal interactions. The control plane defines runtime state through configuration and reconciliation. Clients send requests through the data plane and receive responses from the live inference path.
Explore the interactive architecture →Control plane: desired platform state
Git changes, review and merge, GitOps synchronization and Kubernetes reconciliation turn platform intent into deployed resources. Controllers continuously compare desired and actual state and correct drift.
Data plane: request-time execution
The data plane handles operations performed while an inference request is active. It uses currently deployed models, routes and policies, so its behavior directly affects latency, throughput and availability.
Why the separation matters
Version-controlled configuration makes platform changes reproducible and auditable. Continuous reconciliation makes infrastructure self-healing and extensible. The runtime path can remain performance-focused because configuration and lifecycle work happen on a separate axis.