When Kubernetes workloads request more CPU and memory than they actually consume, nodes must reserve capacity that remains unused. This leads to lower node density, forcing the cluster to maintain more instances than necessary. Aligning resource requests with observed utilization improves cluster efficiency and reduces compute spend without sacrificing application performance.

Workloads with consistently low CPU and memory usage may no longer serve active traffic or scheduled tasks, but continue reserving resources within the cluster. These idle deployments often remain after project migrations, feature deprecations, or experimentation. Removing inactive workloads allows node groups to scale down, reducing infrastructure costs without impacting active services.

Clusters that no longer run active workloads but remain provisioned continue incurring hourly control plane costs and may also maintain associated infrastructure like node groups or VPC components. Inactive clusters often persist after environment decommissioning, project shutdowns, or migrations. Decommissioning unused clusters eliminates unnecessary operational costs and simplifies infrastructure management.

Running non-production clusters solely on On-Demand Instances results in unnecessarily high compute costs. Development, testing, and QA environments typically tolerate interruptions and do not require the continuous availability guaranteed by On-Demand capacity. Introducing Spot-backed node groups in non-production environments can significantly reduce infrastructure expenses without compromising business requirements.

When an EKS cluster remains on a Kubernetes version that has reached the end of standard support, AWS begins charging an additional Extended Support fee. These charges often arise from delays in upgrade cycles, uncertainty about workload compatibility, or overlooked legacy clusters. If the workload does not require the older version, continuing to run the cluster in this state results in unnecessary cost and technical risk.

When the EC2 instance types used for EKS node groups have a memory-to-CPU ratio that doesn’t match the workload profile, the result is poor bin-packing efficiency. For example, if memory-intensive containers are scheduled on compute-optimized nodes, memory may run out first while CPU remains unused. This forces new nodes to be provisioned earlier than necessary. Over time, this mismatch can lead to higher compute costs even if the cluster appears fully utilized.