Kubernetes has become the de facto standard for container orchestration, but its cost implications are often underestimated. As organizations scale their Kubernetes deployments, many face the harsh reality of rapidly escalating cloud bills. In fact, according to the CNCF, over 68% of enterprises report “cost management” as one of their top challenges with Kubernetes.
I’ve led cost optimization initiatives for multiple organizations, from startups to Fortune 500 companies, and repeatedly encountered the same pattern: initial Kubernetes adoption focused on capability and speed, followed by sticker shock when production workloads scaled up.
In this article, I’ll share a comprehensive approach we used to cut Kubernetes costs by 65% for a global financial services company without compromising reliability or performance. This wasn’t achieved through a single silver bullet, but rather a methodical process that examined every layer of the stack.
Before making any changes, we established a rigorous assessment framework to identify cost drivers and potential savings. This framework consisted of four key phases:
We started by implementing detailed cost attribution across all clusters. This involved:
# Example labels applied to all namespaces
apiVersion: v1
kind: Namespace
metadata:
name: payment-processing
labels:
team: platform-engineering
cost-center: tech-infrastructure
application: payment-gateway
environment: productionUsing the baseline data, we systematically hunted for waste:
This analysis yielded unexpected insights: - Development/test environments accounted for 42% of total spend - 23% of all persistent volumes were completely unused - The average CPU utilization was only 18% of requested resources
We benchmarked our costs against industry standards:
This identified specific areas where our spending diverged from best practices, particularly in our approach to multi-tenancy and resource governance.
Finally, we created a prioritization framework based on:
This produced a ranked list of optimization opportunities, allowing us to target quick wins first while planning for more complex structural changes.
Armed with our assessment, we implemented a comprehensive set of strategies across six key areas:
Our assessment revealed significant over-provisioning across the board. We implemented an iterative right-sizing program:
We created a formula to calculate appropriate resource requests based on actual utilization:
resource_request = (p95_utilization * 1.2) + bufferWhere buffer varies by application tier: - Critical services: 30% buffer - Standard services: 20% buffer - Batch jobs: 10% buffer
Rather than manual configuration, we deployed the Vertical Pod Autoscaler (VPA) in recommendation mode:
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
name: payment-api-vpa
spec:
targetRef:
apiVersion: "apps/v1"
kind: Deployment
name: payment-api
updatePolicy:
updateMode: "Auto"
resourcePolicy:
containerPolicies:
- containerName: '*'
minAllowed:
cpu: 50m
memory: 100Mi
maxAllowed:
cpu: 1
memory: 1Gi
controlledResources: ["cpu", "memory"]We first ran VPA in recommendation mode to gather data, then deployed the changes incrementally, starting with non-critical workloads.
After right-sizing workloads, we optimized our node infrastructure:
We moved from a homogeneous node model to a multi-tier approach:
We applied node taints and affinities to ensure workloads landed on the appropriate node types:
apiVersion: apps/v1
kind: Deployment
metadata:
name: memory-intensive-app
spec:
template:
spec:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: node-type
operator: In
values:
- memory-optimizedWe analyzed node usage patterns to identify opportunities for reserved instance purchases:
This approach required careful capacity planning, but resulted in 42% node cost reduction.
We increased node density by:
Beyond node-level changes, we optimized how workloads themselves ran:
Many workloads followed predictable usage patterns. We implemented time-based scaling for:
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
name: dev-scale-down
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: dev-environment
triggers:
- type: cron
metadata:
timezone: America/New_York
start: 30 17 * * 1-5 # 5:30pm weekdays
end: 30 8 * * 1-5 # 8:30am weekdays
desiredReplicas: "0"We implemented workload prioritization to improve resource allocation:
This prioritization was enforced through namespace-level resource quotas and limit ranges:
apiVersion: v1
kind: LimitRange
metadata:
name: standard-limits
namespace: standard-workloads
spec:
limits:
- default:
memory: 512Mi
cpu: 500m
defaultRequest:
memory: 256Mi
cpu: 200m
type: ContainerWe developed a comprehensive strategy for running important workloads on spot/preemptible instances:
We classified workloads based on spot-compatibility:
We deployed Karpenter to dynamically provision spot instances:
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: spot-provisioner
spec:
requirements:
- key: "karpenter.sh/capacity-type"
operator: In
values: ["spot"]
- key: "kubernetes.io/arch"
operator: In
values: ["amd64"]
- key: "topology.kubernetes.io/zone"
operator: In
values: ["us-west-2a", "us-west-2b", "us-west-2c"]
provider:
instanceTypes: ["c5.large", "c5a.large", "c5n.large", "c6i.large"]
ttlSecondsAfterEmpty: 30We implemented Pod Disruption Budgets and graceful termination handlers:
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: critical-service-pdb
spec:
minAvailable: 75%
selector:
matchLabels:
app: critical-serviceAlong with SIGTERM handlers in applications to checkpoint state and drain connections before shutdown.
This spot strategy reduced applicable workload costs by 72%.
Storage costs were a significant, often overlooked expense. We implemented:
We created a tiered storage model:
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: economy-storage
annotations:
storageclass.kubernetes.io/is-default-class: "false"
provisioner: kubernetes.io/aws-ebs
parameters:
type: st1
fsType: ext4
volumeBindingMode: WaitForFirstConsumerRather than over-provisioning initially, we implemented automatic volume expansion:
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: expandable-storage
provisioner: kubernetes.io/aws-ebs
parameters:
type: gp3
allowVolumeExpansion: trueCombined with monitoring and alerts to track growing volumes.
We moved appropriate data from persistent volumes to ephemeral storage:
apiVersion: v1
kind: Pod
metadata:
name: processing-pod
spec:
containers:
- name: processor
image: data-processor:1.0
volumeMounts:
- name: temp-data
mountPath: /tmp/processing
volumes:
- name: temp-data
emptyDir:
sizeLimit: 500MiWe implemented automated processes to identify and reclaim:
These storage optimizations reduced our storage costs by 54%.
Finally, we improved multi-tenant efficiency:
We consolidated hundreds of underutilized namespaces:
We identified duplicate services running across different teams:
Finally, we consolidated operators and controllers:
These multi-tenant optimizations reduced cluster count by 40% while maintaining logical separation between teams and environments.
The combined impact of these optimizations was dramatic:
| Metric | Before | After | Change |
|---|---|---|---|
| Monthly Kubernetes Infrastructure Cost | $387,500 | $135,625 | -65% |
| Average CPU Utilization | 18% | 57% | +217% |
| Average Memory Utilization | 32% | 68% | +113% |
| Node Count | 325 | 182 | -44% |
| Pod-to-Node Ratio | 12:1 | 32:1 | +167% |
| Storage Cost | $42,000 | $19,320 | -54% |
| Development Environment Cost | $162,750 | $40,687 | -75% |
| Cost per Transaction | $0.0072 | $0.0025 | -65% |
Most importantly, these cost reductions were achieved while: - Maintaining 99.99% service availability - Improving average response times by 18% - Supporting 34% year-over-year transaction growth
This optimization journey yielded several key insights:
Kubernetes defaults prioritize flexibility and simplicity over cost efficiency. Always question default resource allocations, taking time to understand actual utilization patterns before setting requests and limits.
The most successful organizations embed cost awareness into their ongoing operations:
Technical optimizations only go so far. Real efficiency requires changing developer behavior:
Sometimes the most effective optimization is moving a workload out of Kubernetes entirely:
The most reliable architectures are often the most cost-efficient:
Kubernetes cost optimization doesn’t require sacrificing reliability or performance. By systematically addressing resource allocation, node management, workload scheduling, spot instances, storage, and multi-tenancy, we achieved a 65% reduction in infrastructure costs while maintaining enterprise-grade reliability.
The approach outlined in this article can be adapted to organizations of any size. Start with a thorough assessment, prioritize changes based on potential impact and risk, and implement changes incrementally with careful monitoring.
Remember that optimization is a continuous journey. Build cost awareness into your engineering culture, regularly reassess your infrastructure, and stay current with the rapidly evolving Kubernetes cost optimization ecosystem.
The result will be not just lower cloud bills, but a more efficient, reliable, and scalable Kubernetes environment.
Have you implemented any of these strategies in your own Kubernetes environments? I’d love to hear about your experiences and results. Feel free to reach out to discuss your specific cost optimization challenges.