Your engineering team just spun up another EKS cluster for a new microservices rollout, and within weeks, your AWS bill climbed 30%. More than 40% of EC2 instances run under 10% CPU utilization, and in containerized environments, the waste compounds fast—idle nodes, oversized pods, forgotten test namespaces, and load balancers left running after demos all drive costs higher while delivering zero value.
Kubernetes on AWS gives you elastic scale and resilience, but without deliberate optimization, it also hands you a mounting cloud bill and little visibility into what’s driving spend. This guide walks through practical, automated strategies to reduce your Amazon EKS costs—covering right-sizing, cluster autoscaling, spot instances, storage and networking optimizations, commitment mechanisms, and continuous monitoring—so you can maintain performance while cutting costs by 30–60%.
Understanding where EKS costs hide
EKS billing isn’t just compute. Your total spend breaks into several layers, and each presents optimization opportunities that most teams overlook until the bill arrives.
The EKS control plane charges $0.10 per cluster per hour for standard Kubernetes version support. For very small clusters, this fixed fee can dominate your bill; consolidating multiple small clusters into fewer, larger ones reduces that overhead. Extended support beyond the first 14 months costs $0.60 per cluster per hour, making timely Kubernetes upgrades a cost optimization lever in addition to a security best practice.
Your data plane—the EC2 instances hosting your pods—is typically the largest cost component. EKS bills you for each instance’s type, size, and uptime, whether those instances are running productive workloads or sitting idle waiting for the next pod. When nodes run at 30% utilization because pod requests reserve capacity that actual workloads never touch, you’re paying full price for two-thirds wasted capacity.
Storage costs accumulate through persistent volumes backed by EBS, unattached volumes left behind when pods are deleted, and redundant snapshots. EBS volume types have distinct performance characteristics—gp3 offers baseline 3,000 IOPS and 125 MiB/s regardless of size, while io2 Block Express supports up to 256,000 IOPS. Defaulting to over-provisioned IOPS or keeping volumes attached after workloads terminate wastes money with no performance benefit.
Networking charges can rival compute costs. Data transfer across availability zones, inter-region traffic, and NAT gateway charges add up quickly. Application Load Balancers and Network Load Balancers each carry hourly fees plus per-gigabyte data processing charges that persist even when no traffic flows. An unused ALB left running in a forgotten demo namespace can cost hundreds monthly.
Commitment discounts and Spot pricing represent your largest potential savings levers. On-Demand EC2 pricing is simple but expensive. Reserved Instances and Savings Plans can deliver up to 72% discounts, while Spot instances offer up to 90% savings for interruption-tolerant workloads. The challenge is matching these pricing mechanisms to your actual usage patterns without over-committing on capacity you might not need six months from now.
Without pod-level cost attribution, you see a single AWS bill with EC2 line items, but you don’t know which namespace, service, or team is driving spend. As workloads scale, this opacity makes it nearly impossible to enforce budgets or right-size proactively. A software firm discovered orphaned test clusters costing over $5,000 monthly only after implementing detailed cost monitoring.
Right-sizing pods and nodes for efficiency
Over-provisioning is the silent budget killer in Kubernetes. Developers set conservative CPU and memory requests to avoid out-of-memory errors or throttling, then those requests multiply across dozens of replicas. Before long, you’re running nodes at 30% utilization because pod requests reserve capacity that actual workloads never touch.
Start by auditing your current resource requests and limits against real usage. Tools like Kubecost or OpenCost surface pod-level CPU and memory consumption over time, showing you which workloads are over-requesting resources. Compare requested versus actual metrics: if a pod requests 2 CPU cores but consistently uses 200 millicores, you’re paying for 1.8 cores of idle capacity every hour that pod runs.
Adjust pod resource requests to align with observed usage patterns, leaving a buffer for spikes but eliminating egregious over-provisioning. This frees node capacity, allowing Kubernetes to schedule more pods per node and reducing the total number of EC2 instances you need to run. Automated right-sizing can achieve 30-50% reduction in cluster costs, and the savings compound as you scale.
On the node side, match instance families to workload profiles. CPU-bound microservices benefit from compute-optimized C-series instances, memory-heavy caching layers fit better on R-series, and general-purpose M-series instances work well for balanced workloads. Don’t default everything to the same instance type—EKS supports heterogeneous node groups, and mixing instance families based on actual workload needs improves both cost efficiency and performance.
Graviton instances cost approximately 18-20% less per hour than comparable Intel Xeon instances, with organizations typically achieving 20-40% cost reductions when migrating compatible workloads. A compute workload costing $91,000 annually on Graviton would cost approximately $182,000 on Intel—a potential 50% reduction in compute costs. Most container images compile seamlessly for Arm64, and EKS provides native support across managed node groups and Fargate, making Graviton adoption straightforward for net-new workloads. M7g instances achieve 20% performance gains and 17% price-performance improvements over comparable x86 instances, while C7g offers 2x faster cryptographic operations for security-intensive workloads.
Intelligent autoscaling: pods and nodes
Manual capacity planning doesn’t scale in a dynamic Kubernetes environment. Static node groups sit idle during off-hours and become bottlenecks during traffic spikes. Autoscaling—both at the pod and node level—ensures you’re only paying for what you actually need, moment by moment.
Horizontal Pod Autoscaler (HPA) adjusts the number of pod replicas based on observed CPU utilization, memory, or custom metrics. Tune HPA check intervals to detect load spikes more rapidly, and set higher initial replica counts for critical applications to handle sudden traffic without immediate scaling lag. The goal is responsive scaling that matches pod count to real-time demand without over-provisioning a large static replica count that idles during normal traffic.
Vertical Pod Autoscaler (VPA) recommends and optionally applies adjustments to pod CPU and memory requests based on historical usage. VPA helps you continuously right-size individual pods as workload patterns evolve, preventing drift back into over-provisioning after an initial optimization pass. This automation is critical as applications mature and usage patterns shift.
On the node side, Cluster Autoscaler provisions or terminates EC2 instances based on pending pods and node utilization. When pods can’t be scheduled due to insufficient capacity, Cluster Autoscaler launches new nodes; when nodes sit idle, it scales them down. This dynamic approach eliminates the need to maintain a large static node pool “just in case” while ensuring capacity is available when workloads need it.
Karpenter takes this further. Karpenter is an AWS-native Kubernetes node provisioner that dynamically selects optimal instance types and sizes in real time, improving bin-packing and reducing over-provisioning through intelligent workload placement. Unlike Cluster Autoscaler, which works within predefined Auto Scaling Groups, Karpenter provisions nodes directly from the full catalog of EC2 instance types, choosing the best fit for pending pods’ resource requests and constraints. A media streaming company achieved a 15% reduction in overall node count by leveraging Karpenter’s smarter scheduling, and another organization saw a 35% cost reduction combining node auto-scaling with Spot instances for non-critical tasks.
For maximum responsiveness, implement node pre-warming: maintain a small buffer of ready nodes so incoming pods can be scheduled immediately, then let autoscaling catch up. Use placeholder pods on extra nodes to keep them ready—these lightweight pods can be replaced with actual workloads when needed, ensuring scaling delays are minimized during sudden traffic surges.
Leveraging Spot instances for non-critical workloads
Spot instances can cut compute costs by up to 90% compared to On-Demand pricing by using AWS’s spare capacity. The trade-off: AWS can reclaim Spot instances with two minutes’ notice. For workloads that tolerate interruptions—batch processing, CI/CD pipelines, stateless microservices, data processing jobs, and development environments—Spot delivers dramatic savings with manageable risk.
Run non-critical or fault-tolerant workloads on Spot node groups. Configure Kubernetes node selectors, taints, and tolerations to route appropriate pods to Spot nodes while keeping latency-sensitive or stateful applications on On-Demand or Reserved Instance–backed nodes. A nightly batch processing job that moved to Spot achieved 65% compute cost reduction, and an e-commerce retailer uses Spot for flash-sale traffic spikes while maintaining stable production traffic on Reserved capacity.
Mitigate interruption risk by spreading Spot requests across multiple instance types and availability zones. Use Spot instance fleets or Karpenter’s diversification features to increase the likelihood that at least some capacity remains available when AWS reclaims instances. Implement checkpointing in long-running jobs so work can resume from the last saved state if a Spot instance is interrupted mid-task. For distributed systems like Apache Cassandra or stateless microservices, design architectures that naturally tolerate node failures.
For EKS clusters, combine Spot with auto-scaling to automatically shift workloads back to On-Demand capacity during Spot interruptions. Set up fallback Auto Scaling groups or let Karpenter dynamically switch instance purchasing options based on availability and cost, ensuring workloads continue running even when Spot capacity becomes scarce.
Intel instances can offer superior value in Spot markets, with some configurations providing 65% savings compared to Graviton2 and 27% savings over AMD in Spot pricing scenarios. Evaluate Spot pricing across architectures before defaulting to a single instance family—the optimal choice depends on current market conditions and workload characteristics. The optimal pricing strategy covers 60-80% of baseline capacity with Savings Plans or Reserved Instances, while handling variable load with On-Demand and Spot instances to maximize savings without compromising availability.
Storage optimization: EBS volumes and persistent data
Kubernetes persistent volumes backed by EBS are essential for stateful workloads, but they’re also a common source of waste. Volumes often outlive the pods they serve, and teams forget to delete them after experiments or decommissioned services. Unattached volumes and stale snapshots continue accruing charges month after month.
Standardize on gp3 volumes for general-purpose workloads. gp3 offers baseline 3,000 IOPS and 125 MiB/s throughput regardless of volume size, and you can provision additional IOPS or throughput independently of capacity. This decoupling lets you right-size both capacity and performance, avoiding the over-provisioning inherent in gp2 volumes, where IOPS scale with size. Migrating from gp2 to gp3 is straightforward and proper EBS optimization commonly saves 30–50%.
Audit unattached EBS volumes regularly. When pods are deleted, their persistent volumes may remain attached or detached but not deleted, continuing to accrue charges. Use AWS Cost Explorer or third-party tools to identify volumes that haven’t been attached to an instance in weeks, then clean them up. Similarly, review EBS snapshot retention policies—old snapshots from long-deleted volumes can add up over time, accounting for thousands of dollars in unnecessary storage costs.
For logs, metrics, and backups, push data to S3 with lifecycle policies that transition older objects to cheaper storage classes or delete them after retention periods. S3 is far less expensive than EBS for long-term storage, and integrating S3 lifecycle rules into your logging and backup workflows prevents unnecessary EBS volume growth. This is particularly effective for application logs that need to be retained for compliance but are rarely accessed after a few weeks.
If your workload requires high IOPS, evaluate whether you truly need io2 Block Express (supporting up to 256,000 IOPS) or if gp3 with provisioned IOPS meets your needs at a fraction of the cost. Over-provisioning IOPS “just in case” is a common pattern that inflates EBS bills without measurable performance benefits. Benchmark your actual workload requirements before selecting premium storage tiers.
Networking and load balancer cost control
Data transfer and load balancer fees can rival your compute spend if left unchecked. Cross-availability-zone data transfer charges apply when pods communicate across AZs, and each Application Load Balancer or Network Load Balancer carries an hourly fee plus per-gigabyte data processing charges. These costs are easy to overlook during development but become material at scale.
Co-locate chatty microservices in the same availability zone using Kubernetes topology spread constraints or pod affinity rules. If your services exchange large volumes of data, keeping them on the same node or within the same AZ eliminates cross-AZ transfer costs. Balance this against resilience requirements—don’t sacrifice high availability to save on data transfer—but for internal, non-critical communication, colocation is effective and can reduce networking costs by 20-40%.
Consolidate ingress with a shared Application Load Balancer where possible. Running a separate ALB for each service or namespace is convenient but expensive. Use Ingress controllers that support host-based or path-based routing to serve multiple services through a single ALB, reducing both hourly fees and data processing charges. AWS Load Balancer Controller for Kubernetes automates ALB provisioning and can share ALBs across Ingress resources, dramatically reducing load balancer sprawl.
Remove idle load balancers flagged by AWS Trusted Advisor. Development and demo environments often spin up ALBs or NLBs that remain active long after the project ends. Regularly audit your load balancers and delete those not actively serving traffic. Each idle ALB costs approximately $20-30 monthly, and clusters with dozens of forgotten load balancers can waste thousands annually.
For east-west traffic—service-to-service communication inside the cluster—prefer internal load balancers or direct pod-to-pod communication via Kubernetes Services rather than routing through an internet-facing ALB. This keeps traffic on AWS’s internal network, reducing both latency and cost. Choose Network Load Balancers for high-throughput, low-latency scenarios where per-connection processing overhead matters.
Use CloudFront for egress-heavy applications. If your EKS workloads serve static content or APIs to external users, placing CloudFront in front of your services reduces data transfer costs by caching responses at edge locations and leveraging CloudFront’s lower egress pricing. For workloads serving global audiences, CloudFront can cut egress costs by 50% or more while improving user experience through lower latency.
Applying AWS pricing mechanisms: Savings Plans, Reserved Instances, and EDP
Spot and right-sizing address usage efficiency, but AWS’s commitment-based pricing mechanisms—Savings Plans and Reserved Instances—deliver deeper discounts when you commit to baseline capacity. These instruments are essential for optimizing predictable workloads, but they require careful planning to avoid over-committing on capacity you won’t use.
Savings Plans offer up to 72% discounts compared to On-Demand pricing in exchange for a one- or three-year commitment to a consistent amount of compute usage measured in dollars per hour. Compute Savings Plans apply across EC2 instance families, regions, and even Fargate and Lambda, providing maximum flexibility as your architecture evolves. EC2 Instance Savings Plans deliver slightly higher discounts—up to 72% versus 66%—but lock you into a specific instance family within a region, reducing flexibility if you later need to change instance types.
The optimal pricing strategy covers 60-80% of baseline capacity with Savings Plans or Reserved Instances, while handling variable load with On-Demand and Spot instances. This balance captures commitment discounts on predictable workloads without over-committing on capacity you might not need as architectures evolve. Start conservatively: use AWS Cost Explorer to analyze your EKS cluster’s EC2 usage over the past three to six months, identify stable baseline consumption, and purchase Savings Plans or Reserved Instances to cover 70–80% of that baseline.
Layer On-Demand capacity on top for occasional spikes and Spot for batch or fault-tolerant tasks. A manufacturing company reduced forecasted AWS spend by 43% using a three-year Compute Savings Plan combined with right-sizing. One e-commerce cluster reduced monthly costs from $414 to $138—a 66% reduction—by consolidating node groups, migrating interruptible workloads to Spot, and covering baseline capacity with Savings Plans.
For larger organizations spending over $1 million annually on AWS, the AWS Enterprise Discount Program (EDP) can include additional discounts in exchange for multi-year spend commitments. EDP negotiations are complex and typically require dedicated FinOps resources, but the savings compound with Savings Plans and Spot strategies. EDP typically starts at $1M annual commitments and can include up to 25% Marketplace purchases, making it valuable for organizations with significant AWS footprints.
As of June 1, 2025, AWS no longer allows Reserved Instances and Savings Plans to be shared across customers within an organization, so factor account-level planning into your commitment strategy. However, Convertible Reserved Instances and Savings Plans remain flexible—you can adjust instance families, regions, or even trade them as workloads evolve, reducing lock-in risk. AWS automatically applies your Savings Plans to the highest discount eligible usage first, optimizing across your entire portfolio.
Hykell’s AWS rate optimization continuously manages commitment portfolios using AI-driven planning, automatically purchasing, converting, or trading Reserved Instances and Savings Plans as usage patterns shift. This active portfolio management can boost your effective savings rate to 50–70%+ on compute without manual intervention, and organizations with robust forecasting achieve 20-30% more accurate financial planning.
Monitoring and cost attribution with Kubecost, OpenCost, and AWS tools
You can’t optimize what you can’t measure. Native AWS billing provides service-level breakdowns but lacks the granularity to attribute costs to specific Kubernetes namespaces, pods, or teams. Specialized cost monitoring tools close that gap and surface optimization opportunities that would otherwise remain invisible.
Kubecost integrates directly with EKS to break down expenses at the pod, namespace, and node levels. It shows CPU, memory, storage, and network costs for each workload, identifies idle or underutilized resources, and integrates with AWS Cost and Usage Reports for accurate pricing that includes Reserved Instances and Savings Plans. Deploying Kubecost on EKS is straightforward via Helm using an AWS-optimized bundle, and the dashboard provides cluster overview, namespace-level allocation, and pod-level cost analysis. Kubecost enables chargeback models that hold teams accountable for their infrastructure spending, making cost visibility a cultural lever for optimization.
OpenCost delivers granular cost insights for Kubernetes on AWS EKS, helping identify optimization opportunities across containerized workloads. OpenCost is open-source and integrates with Prometheus, making it a natural fit if you’re already using Prometheus for metrics collection. Export OpenCost metrics to Grafana or CloudWatch for centralized dashboards that combine cost data with performance metrics, enabling you to correlate spend with application behavior and user traffic patterns.
AWS Cost and Usage Reports (CUR) provide the most detailed AWS billing data, including discounts from Savings Plans and Reserved Instances. Integrate CUR with Kubecost or OpenCost for the highest accuracy, ensuring cost allocation reflects actual AWS pricing rather than list rates. AWS introduced split cost allocation support for accelerated workloads in EKS—Trainium, Inferentia, NVIDIA, and AMD GPUs—in September 2025, and now supports importing up to 50 Kubernetes custom labels per pod as cost allocation tags. This enhancement is available in AWS Cost and Usage Report at no additional cost, making GPU cost tracking far more granular than before.
Tag Kubernetes resources consistently—labels for team, project, environment, and cost center—to enable chargeback and showback. Implement a tiered tagging strategy with business, technical, and operational tags, then configure AWS Cost Allocation Tags to surface those labels in Cost Explorer and CUR. A development team that discovered its environments were consuming 40% of total AWS spend used tag-based attribution to implement tighter budgets and automated shutdown policies, reducing development infrastructure costs by 60%.
Set up cost anomaly alerts using AWS Budgets or Cost Anomaly Detection, and route alerts to Slack or email so teams can act immediately when spend spikes unexpectedly. Combine these alerts with Kubernetes-native dashboards in Grafana or Kubecost to drill down from a cost spike to the specific pod or namespace driving the increase. Real-time visibility turns cost management from a monthly accounting exercise into an operational discipline.
Hykell’s observability platform provides role-specific dashboards that combine cost, utilization, and performance metrics across your EKS clusters. CFOs see high-level KPIs and trend lines, FinOps teams track discount coverage and effective savings rates, and DevOps engineers drill into instance-level usage and anomalies—all from a single pane of glass with no clunky integrations or long setup times.
Eliminating waste: idle resources, zombie pods, and forgotten environments
Even with autoscaling and right-sizing, waste accumulates. Test clusters spun up for a proof-of-concept and never deleted, pods left running in a demo namespace after the presentation, and EBS volumes detached but not removed all contribute to unnecessary cloud spend. These “zombie resources” can account for 15-30% of total cloud costs if left unchecked.
A software firm found orphaned test clusters costing over $5,000 monthly and cut their AWS bill by 22% almost immediately after automated termination. Schedule regular audits using tools like Kubecost, Komiser, or AWS Cost Explorer to identify resources that haven’t been accessed in weeks. Establish policies that automatically shut down non-production environments during off-hours—downscaling development and demo namespaces nights and weekends can save 60–75% of weekly runtime costs in those environments.
Implement Kubernetes resource quotas and limit ranges to prevent individual namespaces from consuming unbounded resources. Set namespace-level CPU and memory caps, and configure alerts when teams approach their quotas. This governance prevents runaway costs from a single misconfigured deployment and encourages developers to think about resource efficiency from the start. Resource quotas also improve cluster stability by preventing a single team from monopolizing shared infrastructure.
Use pod disruption budgets and lifecycle policies to gracefully terminate idle workloads. For batch jobs, configure TTL settings so completed pods and their associated resources are automatically cleaned up after a defined period. This prevents clusters from filling with finished jobs that continue to occupy storage and API server resources, degrading performance while adding cost.
Review load balancers and elastic IPs monthly. AWS charges for idle load balancers and unassociated elastic IPs, and these often outlive the services they supported. Delete unused resources as part of your regular cost hygiene routine. One organization identified 47 idle ALBs across development accounts, eliminating over $1,200 in monthly waste with a single cleanup pass.
Implementation roadmap: getting started with EKS cost optimization
Optimizing EKS costs doesn’t require a six-month project. Start with quick wins, layer in automation, and iterate as you learn. This phased approach delivers measurable savings within weeks while building the foundation for continuous optimization.
Week one: establish visibility Enable AWS Cost and Usage Reports and deploy Kubecost on your EKS clusters. Tag resources consistently and configure Cost Allocation Tags in AWS. Identify your top five cost drivers—typically large node groups, high-IOPS EBS volumes, or cross-AZ data transfer—and establish baseline metrics for cost per pod, cost per namespace, and overall cluster efficiency. Use a 90-day lookback period to identify meaningful spending patterns without getting lost in historical data.
Week two: quick wins Delete idle load balancers, unattached EBS volumes, and orphaned snapshots. Migrate gp2 volumes to gp3, right-sizing IOPS and throughput. Implement off-hours schedules for non-production namespaces. These changes require minimal engineering effort and can yield 10–20% savings immediately. Codify these cleanup tasks as part of your regular operational cadence to prevent waste from accumulating.
Week three: right-size and autoscale Deploy Karpenter or tune Cluster Autoscaler for dynamic node provisioning. Adjust pod resource requests based on observed usage from Kubecost. Identify workloads suitable for Spot instances and create mixed node groups with appropriate taints and tolerations. A well-executed right-sizing pass can reduce cluster costs by 30–50%, and combining right-sizing with intelligent autoscaling compounds the savings.
Week four: commitments and monitoring Analyze stable baseline usage and purchase Savings Plans or Reserved Instances to cover 70–80% of that capacity. Set up cost anomaly alerts and integrate Kubecost metrics into your existing observability stack—Grafana, CloudWatch, or Hykell’s unified dashboard. Establish a monthly cost review cadence with engineering and finance stakeholders to track savings, identify new optimization opportunities, and adjust strategies as workloads evolve.
Beyond the first month, optimization is continuous. Workloads evolve, AWS introduces new instance types and pricing mechanisms, and your application architecture changes. Most companies can reduce their EC2 costs by 30-40% through proper instance sizing and commitment strategies, and regular reviews ensure those savings persist as your infrastructure scales.
Real-world results: case studies in EKS cost optimization
Organizations across industries have achieved substantial savings by implementing these strategies. A SaaS company reduced overall Kubernetes costs by 42% by combining pod right-sizing, intelligent node scaling with Karpenter, automated Reserved Instance management, Spot integration for batch workloads, and continuous cleanup of unused resources. The optimization took four weeks to implement and now runs automatically, requiring minimal ongoing maintenance.
An e-commerce retailer cut monthly EKS costs from $414 to $138—a 66% reduction—by consolidating node groups, migrating interruptible workloads to Spot, and right-sizing pod requests. GOV.UK achieved 15% per-instance savings migrating from m6i (x86) to m7g (Graviton), with total savings reaching 55% when combined with right-sizing and Savings Plans. This phased migration took three months and improved both cost and environmental impact.
A financial services firm reduced forecasted AWS spend by 43% using a three-year Compute Savings Plan layered on top of rightsized instances and Spot for non-critical analytics workloads. These examples share common patterns: visibility into pod-level costs, continuous right-sizing, strategic use of Spot and commitments, and automated enforcement of cost policies. None of these organizations achieved these results through one-time efforts—they built continuous optimization into their operational culture.
Automating optimization with Hykell
Manual optimization works, but it doesn’t scale as clusters grow and workloads multiply. Continuous cost management requires constant analysis of usage patterns, real-time adjustments to pod resource requests, dynamic commitment purchasing, and vigilance against configuration drift. Most engineering teams lack the bandwidth to maintain this discipline while also shipping features.
Hykell automates Kubernetes cost optimization on AWS, continuously fine-tuning pod resource requests and limits during runtime with zero downtime, managing Savings Plans and Reserved Instance portfolios based on evolving workload patterns, integrating with Karpenter for intelligent node provisioning, eliminating idle resources and orphaned volumes automatically, and providing real-time cost visibility across namespaces, teams, and services.
Hykell customers typically reduce AWS cloud costs by up to 40% without compromising performance, and the pricing model aligns incentives—you only pay a share of the savings achieved. If you don’t save, you don’t pay. Implementation takes days, not months, and the platform runs on autopilot once deployed, freeing your engineering team to focus on building features rather than hunting for cost inefficiencies. Hykell combines detailed cost audits to identify underutilized resources with automated EBS and EC2 optimization and Kubernetes-specific tuning for comprehensive coverage.
Transform Kubernetes from cost center to competitive advantage
Kubernetes on AWS delivers the scalability, resilience, and developer velocity your business needs, but unchecked EKS spending drains budgets and distracts engineering teams from higher-value work. By implementing right-sizing, intelligent autoscaling, Spot instances, storage optimization, strategic commitments, and continuous monitoring, you can cut your Kubernetes costs by 30–60% while maintaining—or even improving—performance and reliability.
Start with visibility through Kubecost or OpenCost, tackle quick wins like idle resource cleanup and EBS migration, layer in automated scaling strategies and Spot for appropriate workloads, and treat cost optimization as an ongoing practice rather than a one-time project. The strategies outlined here are proven across hundreds of organizations, and the tooling required—from open-source projects like OpenCost and Karpenter to AWS-native features like Savings Plans—is accessible to any engineering team.
Ready to stop overpaying for Kubernetes? Contact Hykell for a free cost assessment and see how automated optimization can reduce your AWS bill by up to 40% without the engineering effort or the commitment risk.