In EKS environments, cluster sprawl can occur when workloads are removed but underlying resources remain. Common issues include persistent volumes no longer mounted by pods, services still backed by ELBs despite being unused, and overprovisioned nodes for workloads that no longer exist. Node overprovisioning can result from high CPU/memory requests or limits, DaemonSets running on every node, restrictive Pod Disruption Budgets, anti-affinity rules, uneven AZ distribution, or slow scale-down timers. Preventative measures include improving bin packing efficiency, enabling Karpenter consolidation, and right-sizing node instance types and counts. Dev/test namespaces and short-lived environments often accumulate without clear teardown processes, leading to ongoing idle costs.
These remnants contribute to excess infrastructure cost and control plane noise. Since AWS bills independently for each resource (e.g., EBS, ELB, EC2), inefficiencies can add up quickly. Without structured governance or cleanup tooling, clusters gradually fill with orphaned objects and unused capacity.
EKS clusters incur compute charges through EC2 nodes or Fargate profiles, storage charges from attached EBS volumes, and networking charges from load balancers created via Kubernetes Services. Additional network-level costs can include NAT Gateway hourly and per-GB data processing fees, cross-Availability Zone (AZ) data transfer, and other inter-VPC or internet-bound traffic. Orphaned resources such as unused PVCs (backed by EBS), idle Services (backed by Elastic Load Balancers), and underutilized nodes can generate persistent charges even when no active workloads are running.
