Amazon Elastic Kubernetes Service (EKS) cost optimization is the process of minimizing expenses by configuring pod and node sizing, selecting cost-effective pricing options, and eliminating unnecessary costs while maintaining system reliability and performance.
Modern businesses rely on automated rightsizing as their primary solution to prevent Kubernetes overprovisioning and achieve the highest possible cost reduction.
This blog post explains EKS pricing details, reveals hidden expenses, and provides 10 actionable methods to reduce costs without impacting system performance.
Why EKS Cost Optimization Matters
EKS has become a popular managed Kubernetes choice for AWS-nativeteams. However, without an optimization plan in place, running Kubernetes clusters is not cheap when expanding from a single to multiple EKS clusters across different environments and geographic regions.
A 2023 CNCF microsurvey found that 49% of organizations experienced cost increases from Kubernetes. Not because Kubernetes itself is inherently expensive, but because overprovisioned resources and underutilized capacity create waste that compounds at scale.
This is exactly why EKS cost optimization matters.
Cost optimization isn’t a standalone finance initiative, but rather a byproduct of well-architected systems. When you optimize for performance and reliability through continuous rightsizing and intelligent resource management, cost efficiency follows naturally.
Common barriers to EKS cost optimization include:
- Unexpected high bills when traffic levels increase slightly, new microservices start, or pod/node autoscalers (HPA/VPA/KEDA, Karpenter/CA) create configuration errors
- Extended support fees to maintain outdated Kubernetes versions
- Complicated chargeback/showback reports because AWS billing data does not align with namespace structure, team assignments, and application divisions
- Excessive overprovisioning due to large safety margins that remain unmodified for a long time
EKS Pricing and Key Cost Drivers
There are four main cost areas in EKS: the control plane, worker nodes, storage, and networking. EKS uses a pricing model that charges users for Kubernetes control plane access based on cluster usage and duration.
⚠️ Extended Support Cost Warning
AWS charges an additional $0.60 per cluster per hour for unsupported Kubernetes versions. This is a 600% increase from the standard $0.10/hour control-plane fee (total: $0.70/hour). For a cluster running 24/7, this adds ~$432/month per cluster.
Keeping Kubernetes versions current is a high-leverage cost optimization action.
Here’s a quick overview of the main EKS cost drivers beyond the control plane:
| Cost Driver | What It Includes | Why It Matters |
| Worker Nodes | EC2 instances running your pods (On‑Demand and Spot)Savings Plans(SPs)/Reserved Instances (RIs)Fargate profiles with vCPU/GiB requested by pods | As the raw compute backing your workloads, this is typically the largest portion of your EKS bill. |
| Storage | EBS volumes for stateful workloadsOrphaned volumes left behind when nodes or clusters are destroyed | Orphaned EBS volumes from deleted pods, unused snapshots, and excessive application/infrastructure logs (CloudWatch, S3) accumulate silently, creating recurring costs that teams rarely audit. |
| Networking | Inter‑AZ data transfer for chatty services Cross‑AZ traffic through load balancers (ALB/NLB) | Networking can be a hidden cost driver when services are spread across AZs or load balancers communicate over cross‑AZ traffic. |
Two challenges typically get in the way of optimizing EKS costs:
- Visibility: Shared clusters and poor tagging make it difficult to understand which teams, namespaces, and services are driving costs.
- Operations: Tuning pod or node autoscalers, node groups, and pricing demands ongoing engineering work and can impact reliability and performance, making teams reluctant to make any aggressive changes.
10 Best Practices for Amazon EKS Cost Optimization
Companies can apply the following recommendations immediately to lower their EKS spend.
1. Standardize and Enforce Tagging for Showback
You can’t optimize what you can’t see:
- Define a standardized tagging system that includes team, service, environment, and cost-center categories for all clusters, node groups, and their associated resources.
- Integrate these tags with AWS Cost Explorer and Kubernetes labels/annotations.
- Use a solution like ScaleOps that provides real-time cost visibility per cluster, namespace, and team, while continuously optimizing the underlying resources to reduce that spend automatically.
2. Rightsize Pods and Improve Bin Packing
Most EKS clusters run overprovisioned because most developers aren’t Kubernetes resource management experts. To avoid OOMKills or CPU throttling, they set elevated pod requests “just to be safe,” often adding 2-3x safety buffers. Without working with percentile-based sizing (P95/P99) or understanding the difference between real-time metrics (metrics-server) and historical trends (Prometheus), teams tend to overallocate by default.
The result? A pod might request 4 vCPUs but typically use ~1 vCPU under normal load, meaning ~75% of node capacity is wasted.
To fix this:
- Review your peak CPU and memory consumption levels from busy periods against your current pod request and limit settings.
- Eliminate excessive safety margins from configurations.
Pro Tip
Manual rightsizing is challenging because workload patterns change constantly. New releases alter CPU profiles, traffic seasonality shifts memory needs, and what was “right-sized” last month can be wrong today. Keeping up requires continuous monitoring, analysis, and YAML updates across hundreds of pods, an approach that doesn’t scale.
Even tools like VPA in Off/Recommendation mode often provide static snapshots. They don’t react to real-time cluster conditions or coordinate decisions across pod and node autoscalers.
ScaleOps provides automated pod rightsizing that continuously adapts requests and limits based on observed workload behavior and current cluster conditions. This allows for enhanced efficiency automatically as stability and performance improve.
3. Use Right Autoscaling Tools: CA, Karpenter, and KEDA
There are two layers of autoscaling to consider:
Pod-Level Autoscaling:
- VPA (Vertical Pod Autoscaler): Adjusts CPU/memory requests for individual pods. It’s often used in Recommendation mode first to gain visibility before applying changes automatically
- HPA (Horizontal Pod Autoscaler): Scales pod replicas based on CPU, memory, or custom metrics
- KEDA (Kubernetes-based Event Driven Autoscaler): Event-driven scaling using leading indicators (queue depth, lag, latency) vs. lagging indicators (CPU/memory)
Node-Level Autoscaling:
- Cluster Autoscaler (CA): Traditional node scaling approach using Auto Scaling Groups
- Karpenter: Modern EKS standard; provisions nodes directly based on pending pod requirements, enabling faster scaling (often under ~60 seconds vs. 3-4 minutes for CA) and better bin-packing
Pro Tip
Karpenter is only as good as your pod requests. It will create an expensive 4-vCPU node if requested, even though a pod only uses 1 vCPU. ScaleOps solves this problem by optimizing pod sizes before sending precise resource requests to Karpenter. ScaleOps’ Karpenter Optimization enables Karpenter to select the cheapest possible mix of instances, thereby maximizing savings and system utilization.
4. Choose Wisely Between EC2 and Fargate
Amazon EKS provides two options for worker nodes: EC2 instances that you manage or AWS Fargate, where AWS manages the servers for you.
- Determine which applications require continuous operation and stable performance, and which need short-lived operation periods.
- EC2 provides optimal performance for applications that require stable, high-volume operations; you can further reduce the cost of that capacity with SPs and RIs.
- Fargate is suitable for short-term jobs and small applications that need serverless compute. Each pod runs in an isolated microVM defined by Fargate profiles, and you pay strictly for the vCPU and memory your pods request.
Pro Tip
With Fargate, you only pay for the CPU and memory your pods request, while with EC2, you pay for the whole node regardless of usage. ScaleOps analyzes real workload behavior and automatically places the right pods on EC2 or Fargate according to policies you define. It continuously rightsizes pods to maximize EC2 node utilization for steady, long‑running workloads, while routing short‑lived and spiky jobs to Fargate, where “pay per pod” is more cost‑effective.
5. Increase Spot Instance Adoption Safely
Spot instances offer prices up to 90% lower than on-demand instances; however, with spot, users must accept the possibility that instances may be preempted:
- Start by selecting stateless services, batch jobs, and development testing environments for spot instance deployment.
- Spread resources across multiple availability zones and instance types; also, use PodDisruptionBudgets to ensure the minimum number of replicas stay running during node replacements or Spot interruptions, enabling applications to recover gracefully.
Pro Tip
ScaleOps Spot optimization identifies workloads that can run on spot instances and moves them automatically to maximize spot instance usage without any system interruption.
6. Prefer Savings Plans Over Traditional RIs
For baseline capacity that runs every day, commitment‑based discounts are one of the most effective ways to lower your compute bill:
- Determine your baseline EKS compute usage that should be covered by commitment pricing rather than on-demand pricing.
- Evaluate Savings Plans and EC2 Reserved Instances. Compute Savings Plans offer maximum flexibility (covering EC2, Fargate, and Lambda across any region), while EC2 Instance Savings Plans can provide higher discounts but are scoped to specific instance families. For most teams, Savings Plans are the modern default due to their flexibility.
- Calculate your permanent node requirements, buy SPs that match your baseline usage, and then monitor for changes in your clusters.
7. Eliminate Underutilized and Zombie Resources
Your EKS expenses from idle workloads accumulate steadily without delivering any value:
- Use per-namespace and per-workload scaling policies of ScaleOps to be more aggressive in dev/test environments. Automatically scale to zero during off-hours (evenings/weekends) and progressively scale back up during business hours without manual cron jobs or intervention.
- Remove or archive unused environments via automated processes if they’ve been inactive for a specified period.
- Perform periodic removal of abandoned EBS volumes, load balancers, and Elastic IPs.
8. Optimize EBS Volume Types (gp3 over gp2)
For most general‑purpose workloads, gp3 is cheaper and more flexible than gp2. In practice, this means you can usually get the same or better performance at a lower monthly cost by standardizing on gp3 and gradually phasing out older gp2 volumes:
- Set gp3 as your default volume type for new workloads to achieve better performance. In most cases, you can migrate from gp2 to gp3 with zero downtime via the AWS Console or CLI, removing the fear of disruption that prevents teams from making this cost-saving change.
- Build a simple migration plan to move high-usage or long-lived gp2 volumes to gp3 over time, focusing first on the largest cost drivers.
9. Minimize Inter‑AZ Data Transfer
A multi-AZ topology, which spreads your cluster across different availability zones, provides excellent resilience, but it will increase your AWS expenses when you enable it for all environments:
- Place chatty and tightly coupled services within the same availability zone whenever possible.
- Apply topologySpreadConstraints with caution and only if required.
- Use single‑AZ clusters for non‑critical workloads when the risk assessment indicates it is acceptable.
10. Codify Cost Optimization with IaC
Manual operations don’t scale; implement cost‑aware configuration as part of your infrastructure as code (IaC) so every new cluster inherits it. For example, the following simplified Karpenter NodePool definition prefers spot capacity:
These 10 practices form a complete EKS optimization strategy. However, implementing them manually across production clusters is where most teams hit a wall.
Autonomous EKS Optimization with ScaleOps
All of the best practices we covered work in theory, but only if teams are willing to constantly monitor usage and manually tune resources across clusters, nodes, and workloads. In real world production environments, that level of manual effort simply does not scale.
ScaleOps is an autonomous cloud resource management platform that removes this operational overhead. Instead of relying on static configurations or periodic tuning, ScaleOps continuously manages cloud resources in real time, making context aware decisions based on actual workload behavior and live cluster conditions.
With ScaleOps:
- Continuously rightsize pods and optimize replica counts based on real usage, not assumptions or oversized safety margins
- Improve Karpenter and Cluster Autoscaler outcomes without replacing or migrating anything by working natively with your existing autoscaling configuration and supplying real time, context aware intelligence that drive more efficient instance decisions
- Get unified, granular cost visibility by cluster, namespace, team, and application across cloud, hybrid, and air gapped environments through a self hosted architecture that integrates cleanly with existing GitOps and CI/CD workflows
If you are ready to move from best practices to autonomous cloud resource management with ScaleOps and reduce EKS costs by up to 80% without sacrificing reliability, book a demo or get started with a free trial.
