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.
Oversized instances within Auto Scaling Groups lead to inflated baseline costs, even when scaling adjusts the number of instances dynamically. When workloads consistently use only a fraction of the available CPU, memory, or network capacity, there is an opportunity to downsize to smaller, less expensive instance types without sacrificing performance. Right-sizing helps balance capacity and efficiency, reducing compute spend while preserving workload stability.
Detection:
This inefficiency occurs when an EC2 instance remains in a running state but is not actively utilized. These instances may be remnants of past projects, forgotten development environments, or temporarily created for testing and never decommissioned. If an instance shows consistently low or no CPU, network, or disk activity—and no active connections—it likely serves no operational purpose but continues to generate ongoing compute and storage charges.
This inefficiency arises when a virtual machine is left in a stopped (deallocated) state for an extended period but continues to incur costs through attached storage and associated resources. These idle VMs are often remnants of retired workloads, temporary environments, or paused projects that were never fully cleaned up. Without clear ownership or automated cleanup, they can persist unnoticed and accumulate avoidable charges.
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.
Workloads are sometimes deployed in specific AWS regions based on legacy decisions, developer convenience, or perceived performance requirements. However, regional EC2 pricing can vary significantly, and placing instances in a suboptimal region can lead to higher compute costs, increased data transfer charges, or both. In particular, workloads that frequently communicate with resources in other regions—or that serve a user base concentrated elsewhere—can incur unnecessary costs. Re-evaluating regional placement can reduce these costs without compromising performance or availability when done strategically.
GCP VM instances are often provisioned with more CPU or memory than needed, especially when using custom machine types or legacy templates. If an instance consistently consumes only a small portion of its allocated resources, it likely represents an opportunity to reduce costs through rightsizing. Without proactive reviews, these oversized instances can remain unnoticed and continue to incur unnecessary charges.
Azure VMs are frequently provisioned with more vCPU and memory than needed, often based on template defaults or peak demand assumptions. When a VM operates well below its capacity for an extended period, it presents an opportunity to reduce costs through rightsizing. Without regular usage reviews, these inefficiencies can persist indefinitely.
EC2 instances are often overprovisioned based on rough estimates, legacy patterns, or performance buffer assumptions. If an instance consistently uses only a small fraction of its provisioned CPU or memory, it likely represents an opportunity for rightsizing. These inefficiencies persist unless usage is periodically reviewed and instance types are adjusted to align with actual workload requirements.
