Even when no user workloads are active, GKE Autopilot clusters continue running system-managed pods that accrue compute and storage charges. These include control plane components and built-in agents for observability and networking. If Autopilot clusters are deployed in non-production or experimental environments and left idle, they may silently accrue ongoing charges unrelated to application activity. This inefficiency often occurs in: * Dev/test clusters that are spun up temporarily but not deleted * Clusters used for one-time jobs or training workloads * Scheduled workloads that run infrequently but don't trigger downscaling
* Billed per vCPU, memory, and ephemeral storage requested by running pods * Baseline system pods (e.g., logging agents, kube-system) incur cost even with zero user workloads * Clusters themselves do not scale to zero — an idle Autopilot cluster still incurs a minimum charge
