Kubernetes FinOps: Decision Framework for Cost Optimization Strategies

Calendar February 22, 2026
Kubernetes
Intermediate
Cost-Analysis, Resource-Sizing, Capacity-Planning, Budget-Enforcement, Chargeback-Implementation
Finops, Cost-Optimization, Kubecost, Opencost, Rightsizing, Spot-Instances, Cluster-Autoscaler, Resource-Quotas, Chargeback, Showback
Kubectl, Kubecost, Opencost, Prometheus, Grafana, Goldilocks, Karpenter

Kubernetes FinOps: Decision Framework for Cost Optimization#

FinOps in Kubernetes is the practice of bringing financial accountability to infrastructure spending. The challenge is not a lack of cost-saving techniques – it is knowing which ones to apply first, which combinations work together, and which ones introduce risk that outweighs the savings. This article provides a structured decision framework for selecting and prioritizing Kubernetes cost optimization strategies.

The Five Optimization Levers#

Every Kubernetes cost optimization effort works across five levers. Each has a different risk profile, implementation effort, and savings ceiling.

Resource Requests and Limits: CPU, Memory, QoS, and OOMKilled Debugging

Calendar February 22, 2026
Kubernetes
Intermediate
Resource-Sizing, Oomkilled-Debugging, Capacity-Planning
Resources, Cpu, Memory, Qos, Oomkilled, Limits, Requests
Kubectl, Vertical-Pod-Autoscaler

Resource Requests and Limits#

Requests and limits control how Kubernetes schedules pods and enforces resource usage. Getting them wrong leads to pods that get evicted, throttled to a crawl, or that starve other workloads on the same node.

Requests vs Limits#

Requests are what the scheduler uses for placement. When you request 500m CPU and 256Mi memory, Kubernetes finds a node with that much allocatable capacity. The request is a guarantee – the kubelet reserves those resources for your container.

Kubernetes Cost Optimization: Rightsizing, Resource Efficiency, and Waste Reduction

Calendar February 21, 2026
Kubernetes
Intermediate
Cost-Analysis, Resource-Sizing, Capacity-Planning, Cluster-Optimization
Cost-Optimization, Rightsizing, Resource-Requests, Kubecost, Vpa, Goldilocks, Karpenter, Cluster-Autoscaler, Finops
Kubectl, Prometheus, Grafana, Kubecost, Goldilocks, Karpenter

Kubernetes Cost Optimization#

Most Kubernetes clusters run at 15-30% actual CPU utilization but are billed for the full provisioned capacity. The gap between what you reserve and what you use is pure waste. This article covers the practical workflow for finding and eliminating that waste.

The Cost Problem: Requests vs Actual Usage#

Kubernetes resource requests are the foundation of cost. When a pod requests 4 CPUs, the scheduler reserves 4 CPUs on a node regardless of whether the pod ever uses more than 0.1 CPU. The node is sized (and billed) based on what is reserved, not what is consumed.

Vertical Pod Autoscaler (VPA): Right-Sizing Resource Requests Automatically

Calendar February 21, 2026
Kubernetes
Intermediate
Autoscaling-Configuration, Resource-Sizing, Capacity-Planning
Vpa, Autoscaling, Resource-Requests, Right-Sizing, Vertical-Scaling, Goldilocks
Kubectl, Helm, Goldilocks

Vertical Pod Autoscaler (VPA)#

Horizontal scaling adds more pod replicas. Vertical scaling gives each pod more (or fewer) resources. VPA automates the vertical side by watching actual CPU and memory usage over time and adjusting resource requests to match reality. Without it, teams guess at resource requests during initial deployment and rarely revisit them, leading to either waste (over-provisioned) or instability (under-provisioned).

What VPA Does#

VPA monitors historical and current resource usage for pods in a target Deployment (or StatefulSet, DaemonSet, etc.) and produces recommendations for CPU and memory requests. Depending on the configured mode, it either reports these recommendations passively or actively applies them by evicting and recreating pods with updated requests.