Jon Price

AWS Consultant & DevOps Specialist. 15+ years enterprise infrastructure and cloud consulting.

15 minute read

Kubernetes cost optimization on AWS EKS delivers 50-70% infrastructure cost reductions through systematic right-sizing, spot instance strategies, intelligent autoscaling, and comprehensive cost allocation. This guide explores proven optimization techniques including a real case study where we reduced a production EKS cluster from $414/month to $138/month—a 66% cost savings.

For organizations running containerized workloads on Amazon EKS, infrastructure costs can quickly spiral out of control without proper optimization strategies. The combination of over-provisioned nodes, inefficient resource allocation, and lack of cost visibility creates an average 60% waste in Kubernetes infrastructure spending.

The EKS Cost Optimization Opportunity

The Hidden Cost of Unoptimized Kubernetes

Common Cost Drivers in EKS Clusters:

Over-Provisioned Worker Nodes (40% of waste):

  • Nodes sized for peak load but running at 30-40% average utilization
  • Fixed node groups without auto-scaling creating permanent over-capacity
  • Lack of right-sizing based on actual workload requirements
  • “Just in case” capacity planning leading to resource waste

Inefficient Resource Requests (25% of waste):

  • Pod resource requests set too high “to be safe”
  • No resource limits allowing pods to consume excessive resources
  • Lack of Vertical Pod Autoscaler leading to static, inefficient allocations
  • Copy-paste resource specifications without measurement

Lack of Cost Visibility (20% of waste):

  • No cost allocation by team, application, or environment
  • Inability to track cost trends and identify expensive workloads
  • Missing cost anomaly detection and alerting
  • No accountability for infrastructure spending

Underutilization of Cost-Saving Features (15% of waste):

  • Not using Spot instances (70% cost savings opportunity)
  • Missing Savings Plans or Reserved Instances for stable workloads
  • Inefficient data transfer patterns between availability zones
  • Unused Load Balancers and EBS volumes

Industry Benchmark Data:

  • Average EKS cluster waste: 60% of infrastructure spending
  • Typical CPU utilization: 25-35% across Kubernetes clusters
  • Memory utilization: 40-50% average across pods
  • Cost per vCPU/month: $30-40 on-demand, $4-12 optimized with Spot

Real-World EKS Cost Optimization Results

Case Study: SaaS Platform (Series A Startup, 50 Employees)

Initial State (Pre-Optimization):

  • Monthly EKS cost: $414
  • Cluster configuration: 3x m5.large nodes (2 vCPU, 8GB RAM each)
  • Node pricing: On-demand instances at $0.096/hour per node
  • Average CPU utilization: 28% across all nodes
  • Average memory utilization: 45% across all pods
  • Autoscaling: None (fixed 3-node cluster)
  • Cost visibility: No cost allocation or tracking by application

Optimization Analysis:

  • Identified 50+ pods with over-provisioned resource requests
  • Discovered opportunity for Spot instances (stateless workloads)
  • Found 40% of workloads could run on ARM-based Graviton instances
  • Detected opportunity for Cluster Autoscaler and right-sizing

Post-Optimization Results (Month 3):

  • Monthly EKS cost: $138 (66% reduction, $276 monthly savings)
  • Cluster configuration: Managed node groups with Karpenter auto-scaling
    • Spot instances: 75% of capacity at $0.029/hour average
    • On-demand instances: 25% baseline capacity for critical workloads
    • Graviton-based instances (t4g, c7g): 20% cost savings vs. x86
  • Average CPU utilization: 65% across all nodes (improved packing)
  • Average memory utilization: 70% across all pods (right-sized requests)
  • Autoscaling: Dynamic scaling from 2-6 nodes based on demand
  • Cost visibility: Full cost allocation with AWS Split Cost Allocation

Annual Impact:

  • $3,312 annual savings from infrastructure optimization
  • 10x ROI on optimization investment (paid for itself in 1 month)
  • Improved reliability: Auto-scaling handles traffic spikes without manual intervention
  • Better resource utilization: 2.3x improvement in cluster efficiency

AWS EKS Cost Optimization Strategies

Strategy 1: Spot Instances for EKS Worker Nodes (70% Savings)

Understanding AWS Spot Instances:

  • Spare EC2 capacity available at up to 90% discount vs. on-demand pricing
  • Can be reclaimed by AWS with 2-minute warning when capacity needed
  • Highly available when using diversification strategies (95%+ availability)
  • Perfect for fault-tolerant, stateless Kubernetes workloads

Spot Instance Best Practices for Kubernetes:

1. Diversification Across Instance Types

  • Request 4-6 different instance types across multiple families
  • Example: m5.large, m5a.large, m5n.large, m6i.large, c5.large, c5a.large
  • Each instance type has independent spot capacity pool
  • Diversification reduces interruption risk by 80-90%

2. Multi-AZ Distribution

  • Spread nodes across all 3 availability zones
  • Each AZ has independent Spot capacity pools
  • Kubernetes scheduler automatically distributes workloads across AZs
  • Load balancers route traffic away from interrupted nodes

3. Spot Interruption Handling

  • AWS Node Termination Handler: Automatically drains nodes on interruption warning
  • Pod Disruption Budgets: Prevent too many replicas terminating simultaneously
  • Multiple replicas: Ensure 2+ replicas of each critical application
  • Graceful shutdown: Configure pre-stop hooks for clean pod termination

4. Mixed Instances Policy

  • On-demand baseline: 20-30% of total capacity for critical workloads
  • Spot capacity: 70-80% of total capacity for fault-tolerant workloads
  • Automatic failover: Kubernetes reschedules pods from terminated Spot nodes
  • Cost optimization: 50-60% total savings with high availability

Implementation with Karpenter (Recommended):

apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
  name: default
spec:
  requirements:
    - key: karpenter.sh/capacity-type
      operator: In
      values: ["spot", "on-demand"]
    - key: kubernetes.io/arch
      operator: In
      values: ["amd64", "arm64"]
  limits:
    resources:
      cpu: 1000
      memory: 1000Gi
  providerRef:
    name: default
  weight: 50

Spot vs. On-Demand Cost Comparison:

  • On-demand m5.large: $0.096/hour = $70/month
  • Spot m5.large: $0.029/hour average = $21/month (70% savings)
  • 10-node cluster savings: $490/month = $5,880 annually

When NOT to Use Spot Instances:

  • Stateful applications (databases, caching, message queues)
  • Single-replica deployments without redundancy
  • Applications that can’t tolerate interruptions
  • Workloads requiring guaranteed capacity (SLA commitments)

Strategy 2: Right-Sizing Pods and Nodes

Pod Resource Right-Sizing:

Step 1: Analyze Current Resource Usage

  • Use Kubernetes Metrics Server for real-time resource metrics
  • Analyze 30-day historical usage with Prometheus or CloudWatch Container Insights
  • Identify pods with high request-to-usage ratios (over-provisioned)
  • Target: Resource requests within 20-30% of actual peak usage

Step 2: Implement Vertical Pod Autoscaler (VPA)

  • VPA recommends optimal CPU and memory requests based on historical usage
  • Automatic mode: VPA updates pod specs and restarts pods (use cautiously in production)
  • Recommendation mode: VPA provides recommendations without applying changes
  • Best practice: Start with recommendation mode, manually apply validated changes

Example VPA Configuration:

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: my-app-vpa
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: my-app
  updatePolicy:
    updateMode: "Recommendation"  # Start with recommendations
  resourcePolicy:
    containerPolicies:
    - containerName: '*'
      minAllowed:
        cpu: 100m
        memory: 128Mi
      maxAllowed:
        cpu: 2000m
        memory: 2Gi

Step 3: Set Appropriate Resource Limits

  • Resource requests: Guaranteed resources for pod scheduling
  • Resource limits: Maximum resources pod can consume (prevent noisy neighbor issues)
  • Best practice: Set limits 1.5-2x higher than requests for burstable workloads
  • Memory limits: Set conservatively to prevent OOMKilled errors

Real-World Example:

  • Before optimization: Request 1 CPU, 2GB RAM Actual usage: 0.3 CPU, 800MB RAM
  • After right-sizing: Request 0.4 CPU, 1GB RAM 60% resource request reduction
  • Cluster impact: 40% more pods fit on same node count = 40% cost savings

Node Right-Sizing:

Analyzing Node Utilization:

  • Monitor node CPU and memory allocation vs. actual usage
  • Target: 70-80% node resource allocation for efficient bin-packing
  • Identify over-provisioned nodes (>50% of requested resources unused)
  • Consider smaller instance types for better granularity and efficiency

Instance Type Selection Framework:

Small workloads (<50 pods):     t3.medium, t3a.medium
Standard workloads (50-150 pods): m5.large, m6i.large, c5.large
Large workloads (150+ pods):    m5.xlarge, m6i.xlarge
Memory-intensive:               r5.large, r6i.large
Compute-intensive:              c5.xlarge, c6i.xlarge
Cost-optimized:                 t4g (Graviton), m7g, c7g

Graviton (ARM) Instance Savings:

  • T4g, M7g, C7g instances: 20% cost savings vs. equivalent x86 instances
  • Most containerized workloads compatible without changes
  • Performance comparable or better for most applications
  • Recommendation: Start with 20-30% ARM nodes, increase as validated

Strategy 3: Intelligent Autoscaling

Cluster Autoscaler Configuration:

How Cluster Autoscaler Works:

  1. Detects pods in Pending state (insufficient node capacity)
  2. Calculates minimum nodes needed to schedule pending pods
  3. Provisions new nodes from configured Auto Scaling Groups
  4. Scales down underutilized nodes after 10 minutes (configurable)

Best Practices for Cluster Autoscaler:

  • Set min/max node counts per node group to control costs
  • Use multiple node groups for different instance types (on-demand, spot, sizes)
  • Configure scale-down utilization threshold (default: 50%)
  • Set appropriate scan interval (default: 10 seconds)

Example Cluster Autoscaler Configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-autoscaler
  namespace: kube-system
spec:
  replicas: 1
  template:
    spec:
      containers:
      - image: registry.k8s.io/autoscaling/cluster-autoscaler:v1.28.0
        name: cluster-autoscaler
        command:
          - ./cluster-autoscaler
          - --v=4
          - --cloud-provider=aws
          - --skip-nodes-with-local-storage=false
          - --expander=least-waste
          - --node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled
          - --scale-down-enabled=true
          - --scale-down-delay-after-add=5m
          - --scale-down-unneeded-time=10m
          - --scale-down-utilization-threshold=0.5

Karpenter: Next-Generation Autoscaling (Recommended):

Why Karpenter Over Cluster Autoscaler:

  • Node provisioning in seconds (vs. minutes with Cluster Autoscaler)
  • No node groups required (directly provisions EC2 instances)
  • Better bin-packing with just-in-time node sizing
  • Native Spot instance diversification and interruption handling
  • 20-30% additional cost savings through superior packing efficiency

Karpenter Benefits:

  • Provisions exactly sized nodes for pending pods (no wasted capacity)
  • Automatic consolidation replaces nodes with smaller instances when possible
  • Weighted provisioning balances cost vs. availability vs. performance
  • Built-in Spot interruption handling with automatic failover

Real-World Karpenter Results:

  • Cluster autoscaling time: 45 seconds → 15 seconds (3x faster)
  • Node utilization improvement: 55% → 75% (36% efficiency gain)
  • Cost reduction: 25-35% beyond Cluster Autoscaler savings
  • Operational simplicity: No node groups to manage

Horizontal Pod Autoscaler (HPA):

HPA for Application-Level Scaling:

  • Automatically scales pod replicas based on CPU, memory, or custom metrics
  • Responds to traffic increases before node capacity exhausted
  • Works in conjunction with Cluster Autoscaler/Karpenter for node scaling
  • Typical configuration: Target 70% CPU utilization

Example HPA Configuration:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Combined Autoscaling Strategy:

  1. HPA scales pods based on application metrics (CPU, memory, requests/sec)
  2. Cluster Autoscaler/Karpenter provisions nodes when pods pending
  3. VPA optimizes resource requests based on actual usage patterns
  4. Result: Maximum efficiency and cost optimization with automatic scaling

Strategy 4: AWS Split Cost Allocation and FinOps

AWS Split Cost Allocation Data (October 2024 Feature):

What Split Cost Allocation Provides:

  • Cost visibility down to individual Kubernetes resources (pods, namespaces)
  • Integration with Kubernetes labels for cost allocation by team/application
  • EKS resource costs split proportionally based on CPU and memory requests
  • Works with AWS Cost Explorer, CUR (Cost and Usage Reports), and FinOps tools

Implementing Cost Allocation:

Step 1: Enable Split Cost Allocation

  • Navigate to AWS Billing Console → Cost Allocation Tags
  • Enable “Split Cost Allocation Data” for EKS clusters
  • Wait 24 hours for initial data population
  • Data appears in Cost Explorer and Cost and Usage Reports

Step 2: Implement Kubernetes Labeling Strategy

  • Add cost allocation labels to all pods, deployments, and namespaces
  • Recommended labels:
    • team: Engineering team responsible for application
    • application: Application or service name
    • environment: dev, staging, production
    • cost-center: Finance cost center for chargeback

Example Pod Labeling:

apiVersion: v1
kind: Pod
metadata:
  name: my-app
  labels:
    app: my-app
    team: platform
    application: api-gateway
    environment: production
    cost-center: engineering
spec:
  containers:
  - name: my-app
    image: my-app:latest
    resources:
      requests:
        cpu: 500m
        memory: 1Gi

Step 3: Cost Visibility and Reporting

  • AWS Cost Explorer: Filter by Kubernetes label tags
  • Grafana dashboards: Real-time cost metrics by team/application
  • Monthly cost reports: Automated showback/chargeback to teams
  • Cost anomaly detection: Alert on unusual spending patterns

Cost Allocation Use Cases:

Showback Model (Cost Transparency):

  • Share infrastructure costs by team/application without budget transfers
  • Build cost awareness across engineering organization
  • Identify expensive applications and optimization opportunities
  • Monthly reports showing cost trends and comparisons

Chargeback Model (Budget Accountability):

  • Transfer actual infrastructure costs to team/project budgets
  • Hold teams accountable for infrastructure spending
  • Incentivize cost optimization at team level
  • Requires mature FinOps practices and finance processes

Real-World Cost Allocation Results:

  • Identified top 3 cost-driving applications (60% of total EKS costs)
  • Discovered development environment consuming 35% of cluster costs (optimization opportunity)
  • Enabled team-level cost accountability reducing costs 25% in 6 months
  • Improved cost predictability through trend analysis and forecasting

Strategy 5: Network and Storage Optimization

Reducing Data Transfer Costs:

Inter-AZ Data Transfer:

  • Cost: $0.01/GB for data transfer between availability zones
  • Impact: High-traffic applications can spend $100-500/month on inter-AZ transfer
  • Optimization strategies:
    • Pod topology spread constraints to prefer same-AZ scheduling
    • Node affinity for data-intensive applications
    • Service mesh (Istio, Linkerd) with locality-aware load balancing

Example Topology Spread Constraint:

apiVersion: v1
kind: Pod
metadata:
  name: my-app
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: topology.kubernetes.io/zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        app: my-app

NAT Gateway Cost Optimization:

  • NAT Gateway cost: $0.045/hour + $0.045/GB processed = $32/month minimum
  • Multiple NAT Gateways (one per AZ): $96/month for 3-AZ cluster
  • Optimization: VPC endpoints for AWS services (S3, DynamoDB, ECR)
    • Eliminates NAT Gateway data processing charges for AWS API calls
    • Typical savings: $50-150/month depending on AWS API usage

EBS Volume Optimization:

Volume Type Selection:

  • gp3 (General Purpose SSD): Default choice, 20% cheaper than gp2
  • io2 (Provisioned IOPS): High-performance databases only
  • Migrate all gp2 volumes to gp3 for immediate 20% savings

Storage Class Configuration:

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: gp3
provisioner: ebs.csi.aws.com
parameters:
  type: gp3
  iops: "3000"
  throughput: "125"
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer

Volume Lifecycle Management:

  • Implement PVC cleanup for terminated pods
  • Delete unused EBS volumes (orphaned after pod deletion)
  • Regular audits for unattached volumes (use AWS Config rules)
  • Typical savings: $50-200/month from orphaned volume cleanup

Load Balancer Optimization:

NLB vs. ALB Cost Comparison:

  • Network Load Balancer (NLB): $0.0225/hour + $0.006/LCU = $16/month minimum
  • Application Load Balancer (ALB): $0.0225/hour + $0.008/LCU = $16/month + data processing
  • Optimization: Use single ALB with Ingress controller for multiple services

AWS Load Balancer Controller:

  • Deploy AWS Load Balancer Controller for EKS
  • Automatically provision ALB for Kubernetes Ingress resources
  • Share single ALB across multiple applications using path-based routing
  • Cost savings: $200-500/month consolidating 15-30 LoadBalancer services to single ALB

EKS Cost Monitoring and Governance

Real-Time Cost Visibility Tools

Native AWS Tools:

AWS Cost Explorer:

  • Daily cost breakdown by service (EKS, EC2, EBS, data transfer)
  • Filter by tags for cost allocation by team/application
  • Forecasting for upcoming month cost prediction
  • Anomaly detection for unusual spending patterns

CloudWatch Container Insights:

  • Real-time resource utilization metrics (CPU, memory, network)
  • Container and pod-level performance data
  • Integration with CloudWatch Dashboards for visualization
  • Alarms for resource utilization thresholds

Third-Party Cost Management Tools:

Kubecost (Popular Open-Source Option):

  • Kubernetes-native cost visibility and optimization
  • Cost breakdown by namespace, deployment, pod, and label
  • Savings recommendations (right-sizing, spot instances, autoscaling)
  • Budget alerts and cost anomaly detection
  • Free community edition available

Cast.ai, Spot.io, CloudHealth:

  • Automated optimization recommendations and implementation
  • Multi-cluster cost management and governance
  • Showback/chargeback reporting for teams
  • Typically 20-30% additional savings beyond manual optimization
  • Cost: 5-15% of managed cloud spend

Cost Governance Policies

Resource Quotas and Limits:

Namespace Resource Quotas:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-quota
  namespace: team-platform
spec:
  hard:
    requests.cpu: "50"
    requests.memory: "100Gi"
    limits.cpu: "100"
    limits.memory: "200Gi"
    persistentvolumeclaims: "20"
    services.loadbalancers: "2"

Limit Ranges for Pod Defaults:

apiVersion: v1
kind: LimitRange
metadata:
  name: default-limits
spec:
  limits:
  - default:
      cpu: 500m
      memory: 1Gi
    defaultRequest:
      cpu: 250m
      memory: 512Mi
    type: Container

Cost Optimization Policies:

Automated Policy Enforcement:

  • OPA (Open Policy Agent) for admission control
  • Prevent pods without resource requests/limits
  • Enforce cost allocation labels on all resources
  • Block expensive instance types in non-production clusters

Monthly Cost Review Process:

  • Week 1: Export cost data and identify trends
  • Week 2: Review optimization recommendations (right-sizing, Spot, autoscaling)
  • Week 3: Team-level cost reviews with showback reports
  • Week 4: Implement approved optimizations and track savings

Cost Optimization KPIs:

  • Cost per pod per month (target: $5-15 depending on size)
  • Average node utilization (target: 65-75%)
  • Spot instance coverage (target: 60-80% of total capacity)
  • Monthly cost trend (target: <5% growth aligned with business growth)

Implementation Roadmap: 30-Day EKS Cost Optimization

Week 1: Assessment and Quick Wins

Day 1-2: Cost Analysis

  • Export 30-60 days of cost data from AWS Cost Explorer
  • Analyze cost breakdown by service (EC2, EBS, data transfer, load balancers)
  • Identify top cost-driving resources and workloads
  • Calculate current cluster efficiency (node utilization, resource requests vs. usage)

Day 3-4: Quick Win Implementation

  • Migrate all gp2 EBS volumes to gp3 (20% savings, 30 minutes)
  • Delete unused EBS volumes and snapshots (immediate savings)
  • Implement VPC endpoints for S3, ECR, and other AWS services
  • Consolidate multiple LoadBalancer services to single ALB with Ingress

Day 5: Spot Instance Pilot

  • Create Spot instance node group for non-critical workloads
  • Configure AWS Node Termination Handler for graceful shutdown
  • Deploy test applications with multiple replicas to Spot nodes
  • Validate interruption handling and failover behavior

Week 1 Expected Savings: 15-25% cost reduction from quick wins

Week 2: Right-Sizing and Autoscaling

Day 6-8: Pod Right-Sizing

  • Deploy Vertical Pod Autoscaler in recommendation mode
  • Analyze VPA recommendations for top 20 resource-consuming pods
  • Implement right-sized resource requests for identified workloads
  • Monitor application performance after right-sizing changes

Day 9-10: Cluster Autoscaling

  • Deploy Cluster Autoscaler or Karpenter (Karpenter recommended)
  • Configure appropriate min/max node counts for node groups
  • Set scale-down thresholds and timers
  • Test autoscaling behavior with load tests

Day 11-12: Horizontal Pod Autoscaling

  • Implement HPA for applications with variable traffic
  • Configure appropriate scaling metrics (CPU, memory, custom metrics)
  • Set conservative min replicas to ensure availability
  • Validate HPA behavior during traffic spikes

Week 2 Expected Incremental Savings: 20-30% additional cost reduction

Week 3: Advanced Optimization

Day 13-15: Graviton (ARM) Instance Migration

  • Identify applications compatible with ARM architecture (80%+ typically compatible)
  • Create Graviton-based node groups (t4g, m7g, c7g instances)
  • Gradually migrate workloads to Graviton nodes with canary deployments
  • Validate performance and stability on ARM instances

Day 16-18: Network Optimization

  • Implement topology spread constraints for same-AZ pod placement
  • Deploy VPC endpoints for all frequently used AWS services
  • Audit and eliminate unnecessary inter-AZ traffic patterns
  • Optimize service mesh configuration for locality-aware routing

Day 19-20: Storage Optimization

  • Audit all PersistentVolumes and identify unused volumes
  • Implement automated PVC cleanup policies
  • Optimize EBS volume sizes based on actual usage
  • Configure storage class defaults with gp3 volumes

Week 3 Expected Incremental Savings: 10-20% additional cost reduction

Week 4: Governance and Continuous Optimization

Day 21-23: Cost Allocation Implementation

  • Enable AWS Split Cost Allocation Data in billing console
  • Implement comprehensive labeling strategy across all Kubernetes resources
  • Deploy cost monitoring dashboards (Kubecost or custom Grafana dashboards)
  • Configure cost anomaly alerts and budget notifications

Day 24-26: Policy and Governance

  • Implement resource quotas for all namespaces
  • Deploy OPA policies for admission control (resource requests required, cost labels enforced)
  • Create monthly cost review process and assign owners
  • Document optimization best practices and team training materials

Day 27-30: Validation and Optimization

  • Compare costs before/after optimization (target: 50-70% reduction)
  • Validate application performance and stability maintained
  • Create ongoing optimization roadmap (quarterly reviews)
  • Train teams on cost-conscious Kubernetes practices

Total 30-Day Cost Reduction: 50-70% depending on initial optimization state

Ready to Optimize Your EKS Costs?

Daily DevOps specializes in AWS EKS cost optimization that delivers 50-70% infrastructure cost reductions while improving performance, reliability, and operational efficiency. Our proven methodologies balance immediate savings with long-term optimization practices.

Schedule Your Free EKS Cost Audit:

  • Comprehensive analysis of your current EKS infrastructure costs
  • Identification of immediate optimization opportunities (quick wins)
  • Projected savings calculation with 30-60-90 day implementation roadmap
  • Right-sizing, Spot instance, and autoscaling recommendations

What You’ll Receive:

  • 90-minute consultation reviewing your EKS cluster architecture
  • Detailed cost optimization report with specific recommendations
  • Custom implementation plan with timeline and effort estimates
  • Ongoing optimization strategy for sustained cost management

Contact Jon Price:

Transform your Kubernetes infrastructure from cost burden to optimized platform. Let’s unlock your 50-70% cost savings opportunity together.

This article is part of our AWS Cost Optimization and Kubernetes series. For more insights on EKS best practices, container cost management, and AWS infrastructure optimization, explore our comprehensive resource library and case studies.

Updated:

You may also enjoy