A Practical Guide to Kubernetes Performance and Cost Optimisation

Table of Contents

1. Introduction: Why Kubernetes Performance and Cost Matter

2. Understanding the Performance–Cost Tradeoff in Kubernetes

3. Core Kubernetes Architecture and Cost Drivers

4. Resource Requests and Limits: The Foundation of Optimisation

5. Right-Sizing Pods and Workloads

6. CPU Performance Optimisation Techniques

7. Memory Management and Avoiding OOM Killers

8. Autoscaling Strategies: HPA, VPA, and Cluster Autoscaler

9. Node Optimisation and Instance Selection

10. Networking Performance and Cost Considerations

11. Storage Optimisation in Kubernetes

12. Monitoring, Observability, and Cost Visibility

13. FinOps Best Practices for Kubernetes

14. Common Kubernetes Cost Optimisation Mistakes

15. A Practical Optimisation Checklist

16. FAQs (10 Questions & Answers)

17. Final Thoughts

1. Introduction: Why Kubernetes Performance and Cost Matter

Kubernetes has become the default orchestration platform for modern cloud-native applications. It offers flexibility, scalability, and resilience that traditional infrastructure simply cannot match. But this power comes with a hidden cost both literal and operational.

Many teams move to Kubernetes expecting automatic efficiency, only to discover ballooning cloud bills and unpredictable performance. Clusters grow faster than workloads. Nodes sit idle. Pods request more resources than they use. Performance issues appear during peak traffic, even though plenty of capacity exists.

This is why Kubernetes performance and cost optimisation is no longer optional. It is a core operational skill.

Optimising Kubernetes is not about cutting corners. It is about using exactly what you need, no more and no less, while maintaining reliability and speed. This guide is designed to be practical, battle-tested, and grounded in real-world engineering practices.

2. Understanding the Performance Cost Tradeoff in Kubernetes

In Kubernetes, performance and cost are deeply connected. Over-provisioning resources may improve performance in the short term but will destroy cost efficiency. Under-provisioning saves money initially but leads to throttling, crashes, and poor user experience.

Every optimisation decision lives on a spectrum:

• More CPU means faster execution but higher cost

• More memory reduces garbage collection pressure but increases spend

• More replicas improve availability but multiply infrastructure usage

The goal of Kubernetes cost optimization is balance, not minimisation.

High-performing clusters are not the largest clusters. They are the best-aligned clusters, where resource supply matches real workload demand.

Understanding this tradeoff is the foundation of every optimisation strategy discussed in this guide.

3. Core Kubernetes Architecture and Cost Drivers

Before tuning anything, you need to understand where Kubernetes actually spends money.

Primary Cost Drivers:

• Worker Nodes: Compute instances are the largest cost contributor

• Idle Capacity: Unused CPU and memory still cost money

• Over-provisioned Pods: Inflated requests block efficient scheduling

• Storage Volumes: Persistent volumes often grow unchecked

• Network Egress: Cross-zone and outbound traffic add up quickly

Kubernetes itself does not cost money. Your infrastructure choices do.

Performance issues often arise not from lack of resources, but from poor scheduling caused by incorrect resource declarations.

4. Resource Requests and Limits: The Foundation of Optimisation

Resource requests and limits are the most important and most misconfigured part of Kubernetes.

Requests

Requests define what a pod needs to be scheduled. The scheduler uses these values to place pods on nodes.

Limits

Limits define the maximum resources a container can consume.

When requests are too high:

• Pods get stuck pending

• Nodes appear “full” while being mostly idle

• Costs increase unnecessarily

When limits are too low:

• CPU throttling occurs

• Containers get OOMKilled

• Performance becomes unpredictable

Best Practice

• Set requests close to average usage

• Set limits slightly above peak usage

• Never leave requests empty in production

This single practice can reduce Kubernetes cloud costs by 30–50%.

5. Right-Sizing Pods and Workloads

Right-sizing is the process of aligning declared resources with actual usage.

How Right-Sizing Improves Performance

• Reduces CPU throttling

• Improves scheduling efficiency

• Prevents noisy neighbor issues

How Right-Sizing Reduces Cost

• Frees unused capacity

• Enables bin-packing on fewer nodes

• Reduces autoscaler churn

Practical Steps

1. Monitor real CPU and memory usage over time

2. Identify consistently underutilized pods

3. Adjust requests downward gradually

4. Validate performance under load

Right-sizing should be continuous, not a one-time exercise.

6. CPU Performance Optimisation Techniques

CPU is a compressible resource in Kubernetes, which means performance degrades gracefully but silently.

Common CPU Issues

• CPU throttling due to low limits

• Burstable pods competing for cycles

• Uneven core distribution across nodes

Optimisation Techniques

• Use Guaranteed QoS for latency-sensitive workloads

• Avoid setting CPU limits for batch jobs

• Prefer multiple smaller pods over one large pod

• Align pod CPU requests with application concurrency

High CPU performance does not require high CPU allocation—just correct allocation.

7. Memory Management and Avoiding OOM Killers

Memory is not compressible. When it’s gone, your pod is gone.

Common Memory Mistakes

• Setting memory limits too close to peak usage

• Ignoring application-level memory leaks

• Running JVMs without container-aware settings

Best Practices

• Leave headroom between request and limit

• Use memory profiling tools

• Enable container-aware JVM flags

• Monitor RSS, not just heap usage

Memory optimisation is often the fastest way to improve both stability and cost efficiency.

8. Autoscaling Strategies: HPA, VPA, and Cluster Autoscaler

Autoscaling is where performance and cost optimisation truly meet.

Horizontal Pod Autoscaler (HPA)

• Scales pods based on CPU, memory, or custom metrics

• Best for stateless workloads

Vertical Pod Autoscaler (VPA)

• Adjusts requests automatically

• Excellent for batch and backend services

• Use in recommendation mode first

Cluster Autoscaler

• Adds or removes nodes based on scheduling demand

• Prevents over-provisioned clusters

Golden Rule

Scale pods first, nodes second.

Autoscaling without right-sizing simply scales waste faster.

9. Node Optimisation and Instance Selection

Choosing the wrong instance type can double your costs overnight.

Node Optimisation Strategies

• Use multiple node pools for different workloads

• Separate memory-heavy and CPU-heavy applications

• Prefer newer generation instances

• Use spot or preemptible nodes for fault-tolerant workloads

Well-designed node pools dramatically improve Kubernetes performance tuning outcomes.

10. Networking Performance and Cost Considerations

Networking is often ignored until bills spike.

Cost Traps

• Cross-zone traffic

• Unnecessary service mesh overhead

• Chatty microservices

Performance Improvements

• Co-locate services where possible

• Reduce network hops

• Avoid overusing ingress controllers

Optimising network paths improves latency and reduces egress costs.

11. Storage Optimisation in Kubernetes

Persistent storage quietly drains budgets.

Common Problems

• Oversized PVCs

• Unused volumes

• Expensive default storage classes

Best Practices

• Right-size volumes

• Use dynamic provisioning carefully

• Monitor IOPS usage

• Archive or delete unused data

Storage optimisation is slow, but the savings are long-term and stable.

12. Monitoring, Observability, and Cost Visibility

You cannot optimise what you cannot see.

Key Metrics to Track

• CPU throttling

• Memory usage vs limits

• Pod restart counts

• Node utilisation

• Cost per namespace

Combine performance monitoring with cost dashboards for real insight.

13. FinOps Best Practices for Kubernetes

FinOps brings financial accountability to engineering teams.

Core FinOps Principles

• Shared ownership of costs

• Visibility by team and service

• Continuous optimisation

Tag resources, allocate costs by namespace, and review spend regularly.

Kubernetes cost optimisation is as much cultural as it is technical.

14. Common Kubernetes Cost Optimisation Mistakes

• Relying solely on autoscaling

• Ignoring idle resources

• Treating dev and prod clusters the same

• Overusing managed add-ons

• Never revisiting resource configurations

Avoiding these mistakes often delivers instant savings.

15. A Practical Optimisation Checklist

• ✅ Set requests and limits for every pod

• ✅ Right-size workloads quarterly

• ✅ Use HPA with meaningful metrics

• ✅ Separate node pools by workload type

• ✅ Monitor cost per namespace

• ✅ Remove unused volumes and services

16. FAQs: Kubernetes Performance and Cost Optimisation

1. What is Kubernetes performance and cost optimisation?

It is the practice of improving application performance while minimizing infrastructure waste by aligning resources with actual usage.

2. Why are Kubernetes costs often higher than expected?

Because of over-provisioned resources, idle nodes, and incorrect autoscaling configurations.

3. How do resource requests affect cost?

Requests reserve capacity. Inflated requests lead to unused but paid-for resources.

4. Is autoscaling enough to control costs?

No. Autoscaling without right-sizing often increases waste.

5. What is the biggest Kubernetes cost-saving opportunity?

Right-sizing CPU and memory requests.

6. Should I always set resource limits?

Yes for memory, selectively for CPU depending on workload type.

7. How often should I review resource configurations?

At least quarterly, or after major traffic changes.

8. Are spot instances safe for Kubernetes?

Yes, for stateless and fault-tolerant workloads.

9. How does monitoring help cost optimisation?

It reveals unused capacity and performance bottlenecks.

10. Can Kubernetes be cheaper than traditional VMs?

Absolutely when optimised correctly.

17. Final Thoughts

Kubernetes does not automatically optimise itself. It provides the tools, but the responsibility lies with engineers.

True Kubernetes performance and cost optimisation is not about aggressive cost cutting. It is about precision engineering, continuous learning, and disciplined operations.

When done right, Kubernetes delivers what it promises: scalability, reliability, and efficiency without surprise bills.