As environments scale, GKE clusters tend to accumulate artifacts from ephemeral workloads, dev environments, or incomplete job execution. PVCs can continue to retain Persistent Disks, Services may continue to expose public IPs and provision load balancers, and node pools are often oversized for steady-state demand. This results in cloud spend that is not aligned with application activity.

Organizations that lack visibility into Kubernetes-level resource usage often miss these inefficiencies because GCP billing tools surface usage at the infrastructure level, not the Kubernetes object level.

In GKE environments, it is common for unused Kubernetes resources to accumulate over time. Examples include Persistent Volume Claims (PVCs) that retain provisioned Persistent Disks, or Services of type LoadBalancer that continue to front GCP external load balancers even after the backing pods are gone. ConfigMaps and Secrets may also linger, creating operational overhead.

These orphaned objects often persist due to gaps in CI/CD teardown logic, manual testing workflows, or drift over time. While some carry negligible cost on their own, others can result in significant charges, especially storage and networking artifacts. This inefficiency applies broadly across Kubernetes platforms and is scoped here to GKE.

Node pools provisioned with large or specialized VMs (e.g., high-memory, GPU-enabled, or compute-optimized) can be significantly overprovisioned relative to the actual pod requirements. If workloads consistently leave a large portion of resources unused (e.g., low CPU/memory request-to-capacity ratio), the organization incurs unnecessary compute spend. This often happens in early cluster design phases, after application demand shifts, or when teams allocate for peak usage without autoscaling.

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