Kubernetes Optimization on AWS: Challenges, Strategies, Tools
BT
Benjamin Thomas
CTO
February 12, 2026
Featured
10 min read
Optimizing Kubernetes on AWS requires a deep understanding of cost drivers like EC2 compute, load balancers, and storage. Mismanaged resources, such as idle instances and orphaned volumes, can quickly escalate costs. By right-sizing nodes, using Spot Instances, and automating scaling policies, engineers can effectively control expenses. Tools like Sedai automate the process, continuously adjusting resources based on real-time usage patterns, ensuring both performance and cost-efficiency without manual intervention.
Managing Kubernetes on AWS can quickly become complex as workloads scale and cloud resources fluctuate. Without proper optimization, teams often face unexpected costs, over-provisioned instances, and inefficient resource utilization.
The problem is pervasive: without a clear strategy, EC2 instances, EBS volumes, and network traffic can quickly spiral out of control, leading to wasted spend.
It’s no surprise that68% of companies now use automation for cost optimization and rightsizing, showing how essential automated approaches have become in managing dynamic cloud environments. That’s where optimization comes in.
In this blog, you’ll explore the key challenges of Kubernetes optimization on AWS, explore strategies to reduce costs, and find out the best tools to keep your environment efficient and cost-effective.
What Drives Cost in AWS EKS?
Managing costs in Amazon EKS takes a clear understanding of how cloud resources interact and scale. You need to focus on optimizing key components to avoid unnecessary overhead and maintain cost efficiency as you grow. Below are the cost drivers in AWS EKS.
1. EC2 Compute Costs
This is usually the biggest contributor to your EKS bill. Costs depend on instance types, pricing models (On-Demand, Spot Instances, Savings Plans), and the number of nodes running.
Overprovisioned nodes, underutilized Auto Scaling groups, and heavy dependence on costly On-Demand instances are often the main drivers of excess spend.
2. Elastic Load Balancers (ELBs)
Application Load Balancers and Network Load Balancers are created by Kubernetes Service or Ingress objects to expose applications. They incur hourly charges and data processing fees, and are frequently left running long after the related service has been removed.
3. EBS Volumes
Stateful workloads use Persistent Volumes (PVs) backed by Amazon EBS. These volumes remain even after the pods that used them are terminated. Without proper cleanup, orphaned volumes can build up over time, quietly increasing storage costs.
4. Network and Data Transfer Costs
Data transfer charges are often overlooked. But they can add up quickly, especially for traffic moving between Availability Zones (AZs), across regions, or to the internet via services like NAT gateways.
5. Idle Resources
This represents pure cloud waste, including forgotten namespaces, zombie pods, or even entire clusters sitting unused. Typically, they result from forgotten resources that continue to consume cloud resources without serving any purpose.
6. Container Image Storage and Registry Costs
Private registries like Amazon ECR charge for storing image layers and for data transferred every time nodes pull images. Stale, untagged, or duplicate images slowly pile up, increasing storage fees and inflating costs if they are not actively managed.
Seeing what drives costs in AWS EKS makes it easier to understand why optimizing Kubernetes on AWS is so important.
Why Optimizing Kubernetes on AWS Matters?
Optimizing Kubernetes on AWS matters for you because it directly affects both cost efficiency and system performance. As workloads scale, poorly managed Kubernetes clusters can quickly lead to wasted resources, increased operational complexity, and higher cloud spend.
Here’s why Kubernetes optimization on AWS deserves priority:
1. Cost Efficiency at Scale
Kubernetes environments are naturally dynamic, with resources constantly being provisioned and removed based on demand. Without proper optimization, this often leads to overprovisioned compute, idle instances, and unnecessary cloud charges.
Managing EC2 instances, EBS volumes, and networking efficiently can unfold meaningful cost savings, especially when using autoscaling and spot instances.
2. Enhanced System Reliability
Optimization helps your Kubernetes cluster run more reliably. Over-provisioned or misconfigured clusters often create bottlenecks or trigger service disruptions. With the right tuning, such as autoscaling and resource limits, workloads are evenly distributed across nodes, reducing the risk of failures due to resource exhaustion.
3. Simplified Operations
Unoptimized Kubernetes clusters increase operational complexity. You must juggle node management, scaling, and system health, which quickly adds overhead. Optimizing Kubernetes on AWS cuts down manual effort while maintaining performance and reliability.
4. Faster Deployment and Scaling
Optimization prepares workloads to scale quickly while maintaining low latency and high throughput. When Kubernetes runs efficiently on AWS, teams gain agility, shorten time-to-market, and respond faster to customer demands.
A well-optimized Kubernetes architecture ensures that scaling policies work smoothly and that resources are provisioned when needed.
5. Compliance and Security
Kubernetes optimization goes beyond cost and performance. It also plays a key role in security and compliance. Strong resource management, combined with network policies, RBAC (Role-Based Access Control), and pod security policies, ensures workloads operate securely and in accordance with organizational guidelines.
Understanding why Kubernetes optimization on AWS matters also highlights the common challenges teams face with EKS cost optimization.
Optimizing costs in AWS EKS comes with its own set of challenges. You need to navigate complex environments and manage resources carefully to prevent unnecessary spending.
These are some of the most common hurdles teams face with AWS EKS cost optimization and how they affect your overall efforts:
1. Setting High CPU/Memory Requests to Avoid Issues
Challenge: Assigning excessive CPU or memory requests to prevent throttling often results in overprovisioning and unnecessary spending.
Solution: Review actual resource usage with tools like Prometheus and KubeResourceReport, then set realistic requests based on consumption to avoid waste.
2. Always-on Dev/Test Clusters
Challenge: Keeping development or testing clusters running around the clock increases costs, even when they’re not actively in use.
Solution: Set up automated downtime scheduling to shut down dev/test clusters during off-hours using Kubernetes CronJobs or AWS Lambda, so they run only when required.
3. Running Non-Production Environments 24/7
Challenge: Non-production environments running continuously without usage awareness drive unnecessary resource consumption and costs.
Solution: Automatically scale down idle clusters during non-working hours by applying auto-scaling policies to reduce non-production spend.
4. Receiving a Single Bill with No Workload Breakdown
Challenge: An aggregated bill without team or service-level visibility makes cost control difficult.
Solution: Apply labels and use tools to track spending by team or service, improving accountability and optimization.
Challenge: When finance manages costs and engineering manages infrastructure, a disconnect arises that slows proactive optimization.
Solution: Encourage shared ownership by giving engineering teams access to cost data and insights, enabling smarter resource decisions.
6. Siloed Responsibilities That Create a Disconnect and Blame
Challenge: Siloed teams often lead to misalignment and finger-pointing when cost issues surface.
Solution: Provide engineers with cost visibility and promote cross-functional collaboration by sharing tools and dashboards, giving everyone a clear view of spending and supporting better decision-making.
Seeing these common AWS EKS cost challenges makes it easier to apply the right strategies to optimize spending.
Optimizing AWS EKS costs requires a strategic approach to resource management that avoids common pitfalls and waste. You need to focus on practical strategies and use the right tools to improve cost visibility and simplify resources.
1. Right-size Nodes and Workloads
Over-provisioned nodes and workloads drive unnecessary costs. Right-sizing helps ensure resources closely align with actual workload demand.
How to optimize:
Use the Vertical Pod Autoscaler (VPA): Automatically adjust CPU and memory requests based on actual consumption. Start in recommendation mode to gather insights before enabling full automation.
Audit pod resource usage: Use tools such as Metrics Server, kubectl top, or Prometheus to compare actual usage with requests and fine-tune accordingly.
Select appropriate instance types: For variable workloads, use T-series burstable instances; for compute-intensive jobs, choose C-series compute-optimized instances.
Group similar workloads: Apply node selectors and taints/tolerations to improve resource placement by scheduling similar workloads on the right nodes.
2. Use Spot Instances for Flexible Workloads
Spot Instances can deliver savings of up to 90% compared to On-Demand instances, though they carry interruption risk.
How to optimize:
Identify Spot-friendly workloads: CI/CD pipelines, batch processing, and data analytics are good candidates since they can tolerate interruptions.
Use an intelligent autoscaler: Tools like Karpenter enable dynamic Spot provisioning, offering better reliability and faster scaling than Cluster Autoscaler.
Reduce interruption impact: Build resilient applications using pod anti-affinity and topology spread constraints to minimize disruption from Spot terminations.
3. Clean Up Idle or Unused Resources
Idle assets such as orphaned EBS volumes and ELBs contribute directly to cloud waste. Regular cleanup helps avoid unnecessary charges.
How to optimize:
Audit service traffic: Routinely review services and ingresses, and remove those with zero traffic over extended periods.
Garbage collect orphans: Use scripts or tools to locate and delete unused EBS volumes or ELBs not tied to active services.
Apply TTL labels: For temporary resources, add delete-after labels to automate cleanup once they expire.
4. Schedule Non-Production Cluster Downtime
Development and staging clusters running 24/7 consume resources even when unused.
How to optimize:
Automate shutdowns: Use AWS Instance Scheduler or Lambda scripts to scale down or stop non-production clusters during off-hours and weekends.
Adopt infrastructure as code (IaC): Use Terraform or CloudFormation to tear down and rebuild non-production environments easily, lowering ongoing costs.
5. Decommission Outdated EKS Clusters
Unused clusters incur hidden costs, block modernization, and introduce security risks from outdated Kubernetes versions.
How to optimize:
Apply cluster lifecycle management: Assign clear ownership and define an end-of-life (EOL) date for every cluster.
Identify forgotten clusters: Tools like Wiz can automatically discover unused or unsupported clusters and provide a prioritized list for cleanup.
6. Embed Cost Optimization in Engineering Workflows
Building cost awareness into daily workflows helps engineers make better decisions early, preventing budget overruns.
How to optimize:
Surface cost impact in CI/CD: Use tools that estimate cost changes in pull requests, giving engineers immediate feedback on infrastructure updates.
Define policy as code (PaC): Enforce cost-saving rules, such as restricting expensive instance types in development, through automated policies.
Enable real-time alerts: Configure cost and usage notifications via tools like Slack to flag anomalies as they occur.
Integrate cost visibility: Show cost data directly in developer portals or dashboards, allowing engineers to monitor service impact in real time.
7. Use Amazon EKS Managed Node Groups
EKS Managed Node Groups automate node provisioning, updates, and scaling, removing the need for manual node management. Without proper control, this can introduce unnecessary overhead, especially across diverse workloads.
How to optimize:
Automated Node Pool Scaling: Use EKS Managed Node Groups to automatically scale node pools based on application needs. This minimizes manual intervention and simplifies overall cluster operations.
Instance Type Review and Optimization: Regularly review the instance types configured in your node groups to ensure they align with workload requirements and help optimize costs.
8. Optimize Network Traffic with VPC Peering
Cross-AZ or inter-region traffic can significantly increase data transfer costs. When network paths aren’t optimized, you end up paying more for outbound traffic than necessary.
How to optimize:
VPC Peering for Cost-Efficient Networking: Implement VPC Peering to avoid data transfer charges between VPCs. This keeps private traffic within the same region, helping reduce external transfer costs.
Network-Aware Cluster Design: Design clusters with network efficiency in mind by minimizing data movement between Availability Zones and regions.
9. Enable Cost Allocation Tags
Without proper tagging, tracking, and managing, cloud spend becomes difficult. Tags provide clear visibility into resources used by specific teams, services, or environments.
How to optimize:
Resource Cost Attribution with Tags: Apply cost allocation tags to track resources by department, team, or project. This breaks AWS costs into manageable segments, making optimization easier.
Cost Visibility and Budget Tracking: Configure AWS Cost Explorer and AWS Budgets to review detailed cost breakdowns of tagged resources and analyze usage patterns.
10. Implement Cost Optimization in Kubernetes Workflows
Cost optimization works best when it’s embedded into development and operational workflows. Building cost-aware practices into Kubernetes ensures the entire team stays aligned on reducing spend.
How to optimize:
Shift-Left Cost Awareness: Shift cost awareness left by adding cost checks to CI/CD pipelines so developers receive immediate feedback on infrastructure costs as part of their normal workflow.
Policy-Driven Cost Controls: Apply cost-saving policies through automated tools like Policy-as-Code (PaC) to limit the use of expensive resources, such as large EC2 instances, in development environments.
Applying these cost optimization strategies works even better when paired with the right tools to monitor and manage AWS EKS spending.
To manage AWS EKS costs effectively, you need the right tools to gain clear visibility into resource usage and optimize spending.
Below are the top tools that offer detailed insights and automation capabilities to track, control, and reduce cloud costs across your EKS environment.
1. Sedai
Sedai is an autonomous cloud optimization platform that continuously learns application and workload behavior to optimize Kubernetes clusters, including AWS EKS, for cost, performance, and reliability without manual intervention.
The platform applies machine learning models to adapt to traffic and resource usage patterns in real time, executing safe changes that reduce waste and improve efficiency across compute, storage, and data.
Key Features:
Autonomous Workload and Node Rightsizing: Continuously adjusts pod CPU and memory requests along with node allocations based on real utilization patterns to remove over-provisioned resources.
Predictive Autoscaling: Learns workload demand and proactively scales pods and clusters ahead of spikes, improving resource efficiency while avoiding wasteful over-provisioning.
Cost-Aware Purchasing Optimization: Evaluates actual usage patterns to recommend optimal mixes of On-Demand, Savings Plans, and Spot Instances to reduce overall cloud spend.
Autonomous Anomaly Detection and Remediation: Identifies performance anomalies such as elevated memory usage or pod instability and automatically applies corrective actions to maintain application availability.
Granular Cost Attribution: Delivers detailed cost visibility by workload, namespace, storage, and networking rather than only at the cluster level, helping teams pinpoint precise EKS cost drivers.
Multi-Cloud & Multi-Cluster Support: Operates across AWS EKS, Azure AKS, Google GKE, and other Kubernetes environments, applying consistent optimization and visibility policies.
SLO-Safe Optimization: Validates changes against defined service-level objectives (SLOs) so cost optimization never comes at the expense of performance or availability.
Continuous Behavior Modeling: Continuously refreshes its understanding of workload behavior, enabling optimization strategies to evolve alongside changing usage patterns.
Here’s what Sedai delivers:
Metrics
Key Details
30%+ Reduced Cloud Costs
Sedai uses ML models to find the ideal cloud configuration without compromising performance.
75% Improved App Performance
It optimizes CPU and memory needs, lowering latency and reducing error rates.
70% Fewer Failed Customer Interactions (FCIs)
Sedai proactively detects and remediates issues before impacting end users.
6X Greater Productivity
It automates optimizations, freeing you to focus on high-priority tasks.
$3B+ Cloud Spend Managed
Sedai manages over $3 billion in annual cloud spend for companies like Palo Alto Networks.
Best For: Engineering teams operating large-scale, business-critical Kubernetes workloads on AWS EKS who need continuous cost control, stronger performance, and reduced operational toil without introducing manual optimization workflows.
AWS Cost Explorer gives engineers clear visibility into AWS spending. It supports in-depth analysis of cost and usage patterns across services, helping teams understand where cloud resources are consumed and identify optimization opportunities.
Key Features:
Cost and Usage Reports: Provides detailed breakdowns of AWS service usage and costs, letting you filter and drill down by service, region, or usage type.
Cost Forecasting: Uses historical data to predict future AWS costs, helping teams plan and budget more effectively.
Customizable Dashboards: Build tailored views that monitor cost and usage trends across your AWS environment.
Cost Allocation Tags: Tags resources for detailed tracking and cost allocation, allowing teams to assign expenses to specific projects, departments, or teams.
Best For: Engineering teams and cloud architects who need to track, analyze, and forecast AWS spending, with the flexibility to break down costs by service, team, or project.
AWS Compute Optimizer delivers recommendations for optimizing EC2 instance types and other compute resources based on historical usage patterns. The service helps lower cloud costs by ensuring instances are properly sized, reducing both overprovisioning and underutilization.
Key Features:
EC2 Instance Optimization: Reviews historical usage data to recommend cost-effective EC2 instance types for workloads, ensuring resources align with actual needs.
Auto Scaling Recommendations: Provides guidance on configuring auto scaling policies to scale resources with demand without unnecessary overprovisioning.
EBS Volume Optimization: Provides recommendations to improve EBS volume performance while lowering storage costs based on real usage.
Historical Data Analysis: Uses past resource consumption data to suggest instance size and type changes for better cost efficiency.
Best For: Senior engineers and cloud architects managing large EC2 environments who need data-driven guidance to optimize instance types and configurations for cost efficiency.
Kubecost is a Kubernetes cost-monitoring platform that provides engineers with real-time visibility into Kubernetes resource costs, including those tied to EKS clusters. It enables teams to track cloud costs at the container level, helping allocate expenses accurately across teams and services.
Key Features:
Cost Allocation by Namespace/Label: Supports precise cost tracking and allocation for Kubernetes resources, with breakdowns by namespaces, labels, or individual workloads.
Real-time Cost Monitoring: Tracks cloud costs continuously, delivering up-to-date insight into resource usage and spending across Kubernetes environments.
Resource Optimization Suggestions: Provides actionable recommendations to optimize resource usage, including tuning pod requests and resource limits.
Multi-Cloud Support: Offers visibility into cloud costs across Kubernetes clusters on AWS, Google Cloud, and Azure.
Best For: Kubernetes engineers and cloud teams who need detailed, container-level visibility into EKS costs, along with actionable insights to optimize resource usage and reduce cloud spend.
OpenCost is an open-source tool for monitoring and optimizing Kubernetes cost allocation. It’s designed for teams that want a transparent and customizable solution to track Kubernetes resource usage and related cloud costs.
Key Features:
Granular Cost Tracking: Tracks costs at the container, pod, namespace, and cluster levels, delivering detailed visibility into resource consumption.
Cost Allocation by Label: Enables cost allocation through Kubernetes labels, making it simple to associate spending with specific teams or services.
Integration with Prometheus: Integrates directly with Prometheus to collect metrics and calculate resource costs in real time.
Open-source Flexibility: As an open-source platform, OpenCost allows full customization, giving teams the freedom to adapt the tool to their specific cost-tracking needs.
Best For: Engineering teams managing Kubernetes clusters who need an open-source, flexible cost monitoring solution to optimize resource usage and gain transparency into cloud spending.
Teams managing large-scale, business-critical AWS EKS clusters.
Automates cost optimization and performance tuning for EKS with minimal manual intervention.
AWS Cost Explorer
Teams tracking, analyzing, and forecasting AWS spending.
Provides detailed cost insights and usage patterns to optimize EKS costs.
AWS Compute Optimizer
Engineers optimizing EC2 and EKS instances for efficiency.
Suggests right-sizing to reduce overprovisioning and optimize EKS node costs.
Kubecost
Teams needing container-level visibility for EKS costs.
Provides real-time cost monitoring and resource optimization for EKS workloads.
OpenCost
Teams needing open-source cost monitoring for Kubernetes.
Tracks costs and usage at the pod and namespace level, offering customizable monitoring.
Final Thoughts
Optimizing Kubernetes on AWS goes beyond reducing costs. It’s about keeping your clusters scalable, efficient, and reliable as workloads change over time. From right-sizing EC2 instances to fine-tuning EBS volumes and network traffic, effective cost management depends on continuous improvement and smarter resource allocation.
That’s where autonomous optimization makes a difference. By understanding workload behavior, identifying resource needs, and automatically adjusting configurations, platforms likeSedai help engineering teams simplify Kubernetes operations.
With Sedai, clusters continuously self-optimize, delivering stronger performance and lower costs without ongoing manual effort.
Q1. What role does networking play in optimizing AWS EKS costs?
A1. Networking is a major driver of AWS EKS costs, especially when traffic crosses Availability Zones or regions. To control expenses, use VPC Peering for lower-cost communication within the same region. You can also reduce spending by limiting NAT gateway usage and designing networks to avoid unnecessary cross-region data transfers.
Q2. How can I use cost allocation tags to optimize Kubernetes workloads in AWS?
A2. Cost allocation tags help you track spending at a detailed level, such as by namespace, team, or application. This added visibility makes it easier to spot underutilized or inefficient workloads and adjust resources accordingly. It also supports clearer budgeting and shared accountability across engineering teams.
Q3. What is the impact of AWS EKS cost optimization on security and compliance?
A3. Cost optimization naturally supports better security and compliance. Well-managed resources reduce your attack surface by eliminating unused nodes and misconfigured EBS volumes. AI-driven platforms like Sedai also help apply security policies and compliance standards while optimizing infrastructure and delivering savings.
Q4. How can I ensure that EKS scaling policies align with business priorities?
A4. You can align scaling policies with business goals by setting clear Service Level Objectives (SLOs) and using platforms like Sedai to automate resource changes based on real-time performance data. This keeps scaling decisions practical and outcome-driven, helping your infrastructure respond to business needs without constant manual tuning.
Q5. How do I balance cost efficiency with performance when optimizing AWS EKS?
A5. Finding the right balance comes down to right-sizing resources, choosing suitable instance types, and applying intelligent autoscaling. Modern tools continuously adjust allocations based on live workload demand, so performance stays consistent while avoiding unnecessary cloud spend.
