Running Kubernetes on Amazon Web Services has become the default choice for modern cloud-native teams. And Amazon Elastic Kubernetes Service (EKS) makes operating Kubernetes easier—but not cheaper by default.

Without continuous optimization, EKS costs grow silently due to overprovisioned pods, inefficient autoscaling, idle environments, and hidden infrastructure waste.

At Apton Works, we help teams solve this problem using ACORN, an AI-powered cloud operations platform that continuously optimizes cost, reliability, compliance, and performance together.

This article explains:

• Why EKS cost optimization matters

• The real cost drivers behind EKS bills

• Practical, production-safe optimization techniques

• How ACORN approaches Amazon EKS cost optimization differently
  
  

Why Amazon EKS Cost Optimization Matters

EKS adoption has exploded across startups and enterprises—but Kubernetes cost efficiency has not kept pace.

The problem isn’t Kubernetes itself. The problem is how Kubernetes resources are configured and operated at scale.

Common symptoms we see across EKS environments:

• Cloud bills spike even with small traffic increases

• Pods request far more CPU/memory than they actually use

• Autoscalers add capacity but rarely remove it efficiently

• Dev/test clusters run 24×7 with no business value

• Teams hesitate to optimize aggressively due to reliability risks

EKS cost optimization is not a finance-only exercise.
It’s a result of well-architected, continuously optimized cloud operations.

Understanding EKS Pricing and Cost Drivers

EKS costs are distributed across four major areas:

1. Control Plane

AWS charges per EKS cluster, per hour.
Running unsupported Kubernetes versions incurs significantly higher fees, making version upgrades a high-impact cost-saving action.

2. Worker Nodes (Largest Cost Component)

This includes:

• EC2 On-Demand and Spot instances

• Savings Plans / Reserved capacity

• Fargate compute (vCPU + memory requested per pod)

Most EKS spend lives here.

3. Storage

Hidden costs often come from:

• Orphaned EBS volumes

• Unused snapshots

• Excessive application and infrastructure logs

These costs accumulate quietly unless audited regularly.

4. Networking

Inter-AZ traffic, load balancers, and chatty microservices can create unexpected recurring charges, especially in multi-AZ clusters.

The Two Core Challenges of EKS Cost Optimization

1. Lack of Visibility

Shared clusters, inconsistent tagging, and poor namespace alignment make it difficult to answer:

Which team or service is actually driving this cost?

2. Operational Risk

Manual tuning of pod sizes, autoscalers, and node groups can easily:

• Break performance

• Reduce reliability

• Create production incidents

This risk makes teams reluctant to optimize aggressively.

10 Practical Amazon EKS Cost Optimization Best Practices

1. Standardize Cost Visibility Across Clusters

Cost optimization starts with clarity.

Best practices:

• Enforce consistent tags (team, service, environment, cost center)

• Align AWS tags with Kubernetes labels and namespaces

• Track cost trends continuously—not monthly

ACORN provides real-time EKS cost visibility by cluster, namespace, and workload, not delayed billing reports.

2. Continuously Rightsize Pods (Not One-Time Tuning)

Most EKS clusters are heavily overprovisioned.

Why?

• Developers add large safety buffers to avoid throttling

• Requests rarely reflect real usage

• Workload behavior changes constantly

The result:
Pods request 3–4× more resources than they use, wasting node capacity.

ACORN continuously analyzes workload behavior and automatically adjusts pod requests and limits—without manual YAML changes.

3. Use Autoscaling—But Avoid Policy Conflicts

Autoscaling is powerful, but dangerous when misconfigured.

Common tools:

• HPA (Horizontal Pod Autoscaler)

• VPA (Vertical Pod Autoscaler)

• KEDA (event-driven scaling)

• Cluster Autoscaler or Karpenter (node scaling)

The mistake:
Running multiple autoscalers without coordination, causing them to fight each other.

ACORN orchestrates autoscaling decisions holistically—ensuring pod sizing, replica counts, and node provisioning work together, not against each other.

4. Choose the Right Compute Model: EC2 vs Fargate

EKS offers flexibility—but not every workload belongs everywhere.

General guidance:

• EC2 → steady, long-running, high-throughput services

• Fargate → bursty, short-lived, or low-ops workloads

ACORN dynamically places workloads on the most cost-effective compute model based on actual behavior, not static rules.

5. Increase Spot Instance Usage—Safely

Spot instances offer massive savings, but require careful handling.

Best practices:

• Use spot for stateless and fault-tolerant workloads

• Spread across AZs and instance families

• Enforce PodDisruptionBudgets

ACORN identifies spot-compatible workloads automatically and manages transitions without service disruption.

6. Schedule and Decommission What You Don’t Need

Idle infrastructure is one of the biggest hidden cost drains.

Examples:

• Dev/test clusters running overnight

• Forgotten environments

• Temporary workloads never removed

ACORN enables:

• Scheduled scale-down during off-hours

• Automatic scale-up during business hours

• Safe decommissioning of inactive environments
  
  

7. Commit Baseline Usage with Savings Plans

For workloads that run every day:

• Use Savings Plans for predictable baseline capacity

• Keep burst capacity flexible with on-demand or spot

ACORN helps identify true baseline usage, reducing the risk of overcommitting.

8. Optimize Storage Defaults (gp3 over gp2)

Many teams still run legacy gp2 volumes.

Best practice:

• Default to gp3 for better performance at lower cost

• Migrate high-cost volumes gradually with zero downtime

  
  

9. Reduce Unnecessary Inter-AZ Traffic

Multi-AZ improves resilience—but increases network costs.

Optimization strategies:

• Co-locate tightly coupled services

• Use topology-aware scheduling carefully

• Use single-AZ clusters for non-critical workloads
  
  

10. Codify Cost Optimization via Infrastructure as Code

Manual fixes don’t scale.

Cost optimization should be:

• Built into Terraform, Helm, and cluster templates

• Enforced automatically for every new cluster

ACORN integrates directly with existing EKS and IaC workflows—no replatforming required.

How ACORN Approaches Amazon EKS Cost Optimization

ACORN is not a cost-reporting tool.
It is an AI-driven cloud operations platform.

With ACORN:

• Pods are continuously rightsized based on real usage

• Autoscalers receive intelligent, context-aware signals

• Node provisioning adapts dynamically to workload needs

• Cost optimization never compromises reliability or compliance

Instead of reacting to cloud bills, teams prevent waste by design.

Final Thoughts

Amazon EKS cost optimization is not about cutting corners—it’s about operating Kubernetes intelligently.

The most successful teams:

• Treat cost as a runtime metric, not a monthly report

• Optimize continuously, not periodically

• Use automation to remove human error and hesitation

At Apton Works, ACORN represents this next generation of EKS operations—where cost efficiency, reliability, compliance, and performance are engineered together.