How We Cut Kubernetes Costs by 65% Without Sacrificing Reliability

Introduction: The Cost Challenge in Enterprise Kubernetes

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.

Methodology: Assessment Approach

Before making any changes, we established a rigorous assessment framework to identify cost drivers and potential savings. This framework consisted of four key phases:

Phase 1: Establishing a Cost Baseline

We started by implementing detailed cost attribution across all clusters. This involved:

1. Tagging Strategy:
   • Business unit, team, application, environment
   • Cost center, project, and initiative

   # 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: production

2. Monitoring Setup:
   • Kubecost deployment across clusters
   • Custom Prometheus metrics for business KPIs
   • Historical trending with 30/60/90-day comparisons
3. Unit Economics Analysis:
   • Cost per transaction
   • Cost per customer
   • Cost per API call
   This analysis revealed that our Kubernetes costs had grown 3.5x in the past year, while transaction volume had only increased 1.8x.

Phase 2: Waste Identification

Using the baseline data, we systematically hunted for waste:

1. Cluster-level analysis:
   • Underutilized nodes
   • Oversized control planes
   • Idle regional deployments
2. Workload analysis:
   • Resource utilization versus requests/limits
   • Idle or abandoned workloads
   • Deployment scaling patterns
3. Storage analysis:
   • Unused persistent volumes
   • Oversized volume requests
   • Inappropriate storage classes

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

Phase 3: Benchmark Comparison

We benchmarked our costs against industry standards:

1. CNCF reference architectures
2. Similar-sized organizations in the same industry
3. Cloud provider recommended practices

This identified specific areas where our spending diverged from best practices, particularly in our approach to multi-tenancy and resource governance.

Phase 4: Prioritization Framework

Finally, we created a prioritization framework based on:

1. Estimated cost reduction
2. Implementation complexity
3. Risk to production environments
4. Long-term sustainability

This produced a ranked list of optimization opportunities, allowing us to target quick wins first while planning for more complex structural changes.

Cost Reduction Strategies

Armed with our assessment, we implemented a comprehensive set of strategies across six key areas:

1. Resource Right-Sizing with Real Metrics

Our assessment revealed significant over-provisioning across the board. We implemented an iterative right-sizing program:

The Resource Allocation Formula

We created a formula to calculate appropriate resource requests based on actual utilization:

resource_request = (p95_utilization * 1.2) + buffer

Where buffer varies by application tier: - Critical services: 30% buffer - Standard services: 20% buffer - Batch jobs: 10% buffer

Implementation with Vertical Pod Autoscaler

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.

Results

• Average CPU request reduction: 47%
• Average memory request reduction: 38%
• Zero performance incidents post-implementation

2. Node Optimization Techniques

After right-sizing workloads, we optimized our node infrastructure:

Cluster Topology Redesign

We moved from a homogeneous node model to a multi-tier approach:

1. System nodes:
   • Reserved for cluster-critical components
   • Sized for stability rather than cost efficiency
2. General purpose nodes:
   • For most standard workloads
   • Balanced for cost and performance
3. Compute-optimized nodes:
   • For CPU-intensive workloads
   • Higher cost justified by better performance
4. Memory-optimized nodes:
   • For memory-intensive applications
   • Better price-performance for specific workloads

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-optimized

Reserved Instance Strategy

We analyzed node usage patterns to identify opportunities for reserved instance purchases:

1. Base capacity (24/7 workloads):
   • 3-year reserved instances
   • 60-70% discount from on-demand pricing
2. Predictable variable capacity:
   • 1-year reserved instances
   • 40-50% discount from on-demand pricing
3. Unpredictable variable capacity:
   • Spot/preemptible instances with fallback strategies

This approach required careful capacity planning, but resulted in 42% node cost reduction.

Node Density Optimization

We increased node density by:

1. Improving scheduler efficiency:
   • Custom priority functions
   • Pod topology spread constraints
2. Implementing advanced bin-packing:
   • Pod-to-node ratio increased from 12:1 to 32:1
   • Reduced node count by 40%

3. Workload Scheduling Improvements

Beyond node-level changes, we optimized how workloads themselves ran:

Time-based Scaling

Many workloads followed predictable usage patterns. We implemented time-based scaling for:

1. Development environments:
   • Scaled down to minimum during nights/weekends
   • Reduced run-time by 65%

   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"

2. Batch processing jobs:
   • Scheduled during low-demand periods
   • Leveraged spot instance pricing
3. Regional applications:
   • Follow-the-sun scaling across global clusters
   • Reduced global capacity requirements by 25%

Workload Priority Classification

We implemented workload prioritization to improve resource allocation:

1. Critical workloads:
   • Guaranteed QoS class
   • No oversubscription
2. Standard workloads:
   • Burstable QoS class
   • Moderate oversubscription
3. Best-effort workloads:
   • BestEffort QoS class
   • High oversubscription

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: Container

4. Spot/Preemptible Instance Implementation

We developed a comprehensive strategy for running important workloads on spot/preemptible instances:

Workload Classification

We classified workloads based on spot-compatibility:

1. Spot-Native: Designed for interruption
   • Stateless
   • Checkpointing capability
   • Short processing times
2. Spot-Tolerant: Can handle some interruption
   • Replicated services
   • Non-critical paths
   • Graceful degradation
3. Spot-Intolerant: Cannot handle interruption
   • Stateful services
   • Critical path operations
   • Long-running transactions

Spot Orchestration with Karpenter

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: 30

Graceful Interruption Handling

We 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-service

Along with SIGTERM handlers in applications to checkpoint state and drain connections before shutdown.

This spot strategy reduced applicable workload costs by 72%.

5. Storage Optimization

Storage costs were a significant, often overlooked expense. We implemented:

Storage Class Tiering

We created a tiered storage model:

1. Performance tier:
   • SSD-backed
   • For databases and latency-sensitive workloads
   • Premium pricing
2. Standard tier:
   • Balanced performance
   • For most applications
   • Mid-range pricing
3. Economy tier:
   • HDD-backed
   • For logs, backups, archival data
   • Lowest pricing

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: WaitForFirstConsumer

Automatic Volume Expansion

Rather 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: true

Combined with monitoring and alerts to track growing volumes.

Ephemeral Storage Utilization

We moved appropriate data from persistent volumes to ephemeral storage:

1. Cache data:
   • Redis/Memcached for distributed caching
   • EmptyDir volumes for local caching
2. Temporary processing data:
   • Moved to emptyDir volumes
   • Reduced persistent volume requirements

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: 500Mi

Storage Reclamation

We implemented automated processes to identify and reclaim:

1. Orphaned volumes:
   • PVs without claims
   • PVs from terminated namespaces
2. Oversized volumes:
   • Volumes with <30% utilization after 30 days
   • Rightsizing recommendations

These storage optimizations reduced our storage costs by 54%.

6. Multi-Tenant Efficiency

Finally, we improved multi-tenant efficiency:

Namespace Consolidation

We consolidated hundreds of underutilized namespaces:

1. Development environments:
   • Team-based namespaces instead of per-developer
   • Shared development clusters with strict resource quotas
2. CI/CD environments:
   • Dynamic provisioning/deprovisioning
   • Resource reclamation after job completion

Service Consolidation

We identified duplicate services running across different teams:

1. Common databases:
   • Consolidated into managed database services
   • Implemented multi-tenant patterns with logical separation
2. Monitoring and logging:
   • Centralized collection infrastructure
   • Team-specific views and dashboards

Shared Operator Model

Finally, we consolidated operators and controllers:

1. Single CertManager instance:
   • Managing certificates across all namespaces
   • Reduced control plane load
2. Centralized Prometheus:
   • Federation model with central aggregation
   • Reduced storage and compute duplication

These multi-tenant optimizations reduced cluster count by 40% while maintaining logical separation between teams and environments.

Before/After Metrics

The combined impact of these optimizations was dramatic:

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

Lessons Learned

This optimization journey yielded several key insights:

1. Default Configurations Are Rarely Optimal

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.

2. Cost Optimization Is an Ongoing Process, Not a Project

The most successful organizations embed cost awareness into their ongoing operations:

• Weekly cost reviews as part of engineering standups
• Automated alerts for cost anomalies
• Cost impact assessments for new feature deployments

3. Developer Behavior Drives Cloud Costs

Technical optimizations only go so far. Real efficiency requires changing developer behavior:

• Cost visibility at the team level
• Chargeback or showback mechanisms
• Celebrating cost efficiency alongside feature delivery
• Training on Kubernetes resource management

4. Not All Workloads Belong in Kubernetes

Sometimes the most effective optimization is moving a workload out of Kubernetes entirely:

• Batch jobs with predictable resource needs often cost less on dedicated instances
• Extremely stable services may be more cost-effective as VMs
• Highly variable, stateless web applications might be better as serverless functions

5. Reliability and Cost Efficiency Are Not Opposing Goals

The most reliable architectures are often the most cost-efficient:

• Right-sized resources improve scheduler efficiency
• Appropriate autoscaling reduces both resource waste and risk of undersizing
• Multi-region deployments can be both more reliable and more cost-effective with follow-the-sun scaling

Conclusion

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.