Kubernetes Cost Optimization
Container-level allocation, rightsizing, and cluster utilization.
Kubernetes makes workloads elastic — and cost allocation nearly impossible
Shared nodes, overlapping namespaces, and autoscaling groups mean the cloud bill for a cluster almost never maps cleanly to the teams and services actually consuming it. Most teams either give up on allocation entirely or fall back on rough, unreliable estimates.
Cost allocation down to the pod, using metadata you already have
OptCloud reads existing Kubernetes metadata — namespaces, labels, and resource requests — to allocate shared node cost accurately across every workload, then normalizes it alongside your cloud bill so container costs stop being a separate silo.
What Kubernetes teams get with OptCloud
Right-size pods and node pools together
OptCloud compares requested CPU and memory against real usage at the pod level, then recommends both pod-level adjustments and node pool bin-packing changes that reduce idle capacity without risking evictions.
Pods request 4 vCPU but use 1.1 vCPU on average — rightsizing recommended.
Allocate shared cluster cost accurately
We split node cost across every namespace and workload based on actual resource requests and usage — not a flat per-pod average — so teams see a true reflection of what they consume, even on heavily shared clusters.
One view across every cluster and cloud
EKS, AKS, GKE, and self-managed clusters are normalized into the same schema as the rest of your cloud spend, so Kubernetes cost stops being a report nobody looks at.