The Definitive Guide to Kubernetes Cost Optimization

Kubernetes cost optimization is all about keeping your expenses in check when running apps on K8s clusters. It’s crucial to use resources like CPU, memory, and storage efficiently so you don’t end up overprovisioning and wasting money.

Some solid strategies include right-sizing your workloads, which means adjusting the resources you allocate based on actual needs, and using cheaper options like spot instances for non-critical tasks. Autoscaling is also a game changer; it helps you scale your resources up or down automatically based on demand, so you only pay for what you actually use.

Let’s take a look on what Kubernetes price depends on and learn some ways to control and reduce your Kubernetes cost that can be applied to various Kubernetes use cases, from development and CI/CD to production.

Understanding Kubernetes Cost Structure

The first step towards K8s cost optimization is understanding the cost structure of Kubernetes. Kubernetes operates based on a pay-as-you-go model, where organizations pay for the resources consumed by their applications and infrastructure. To effectively optimize costs, it is crucial to have a clear understanding of how Kubernetes pricing works.

What Makes Up Kubernetes Costs?

Kubernetes pricing depends on different things:

- Infrastructure Type and Size: Whether you run Kubernetes on your own servers, in the cloud, or a mix of both, each option affects costs differently. Pick the setup that fits your needs and budget.
- Nodes: These are the computers that make up the cluster. More or bigger nodes mean higher costs. To save, match the number and size of nodes with your workload’s needs (CPU, memory, storage).
- Storage: Kubernetes offers storage options like local disks, network storage, or cloud storage. Choose the most affordable option that meets your needs for speed, data protection, and cost.
- Extras and Add-ons: Extra services like monitoring tools, security services, and load balancers also add to costs. Make sure these services are worth the price for your needs.

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In conclusion, understanding the cost structure of Kubernetes is vital for effective cost optimization. By comprehending the role of Kubernetes in your IT infrastructure and breaking down the pricing factors, organizations can make informed decisions to optimize costs while leveraging the benefits offered by Kubernetes.

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Kubernetes Cost Optimization - What Could Go Wrong?

Kubernetes cost optimization is challenging and can result in trade-offs between system performance and costs. Now we'll discuss what are some of the common issues that may arise during Kubernetes cost optimization, and how to find a balance between optimal performance and cost considerations.

›› Take a look at the top Kubernetes cost optimization tools to evaluate in 2025.

1. Resource Allocation: A Fine Balance

One of the key aspects of Kubernetes cost optimization is resource allocation. If resources are over-provisioned, the cost of running the application will increase, potentially outweighing any benefits in performance.

By reducing the amount of resources allocated to each pod, organizations can lower their overall Kubernetes costs. However, reducing the resources too much can lead to decreased performance and increased latency. To avoid this issue, organizations should carefully consider the resource requirements of their applications and ensure that each pod is allocated enough resources to meet the performance needs of the application.

Under-provisioning can cause Out of Memory (OOM), CPU throttling, evictions, or latency which will cause additional fire drills your team will need to handle.

For some actionable tips on responsible Kubernetes resource allocation  -  take a look at the Kubernetes Cluster Size Best Practices For Rightsizing

2. Autoscaling: Cost vs. Performance

Another important factor in Kubernetes cost optimization is leveraging autoscaling capabilities, like Horizontal Pod Autoscaler (HPA), KEDA, and Cluster-Autoscaler or Karpenter.

To remain cost effective, DevOps and Platform Engineering teams must balance the cost of scaled resources against the need for performance. If resources are scaled up too late, or scaled down too early, when demand spikes happen the performance, availability, and reliability of the application can be impacted.

On the other hand, if resources are overprovisioned, autoscaling may lead to an increased cost of running the application, potentially outweighing any benefits in performance.

To find the right balance, organizations should monitor their applications closely and make scaling decisions based on the specific requirements of their applications, and keep in mind that system loads continually change over time depending on your usage behaviors.

3. Networking: The Cost of Connectivity

Networking costs can be a significant portion of the total cost of running a Kubernetes cluster. To optimize networking costs, organizations may consider using different pod tolerations to keep most traffic within a region/zone/node. However, these cost-saving measures can also result in SLA or SLO breaches caused by decreased network performance and increased latency.

To avoid this issue, organizations should carefully consider the network requirements of their applications as a group and choose networking topology that optimally balances cost and performance.

4. Storage: The Cost of Data

Storage costs can also be a significant portion of the overall cost of running a Kubernetes cluster. To optimize storage costs, organizations may consider using lower-cost, lower-performance, or lower capacity storage options. However, this can result in reduced performance and increased downtime, as the storage system may not be able to keep up with the demands of the application.

To balance cost and performance, organizations should carefully consider the storage requirements of their applications and choose storage options that help them avoid these issues.

5. Maintenance: The Cost of Keeping Things Running

The price of maintaining a cost-optimized Kubernetes cluster may be higher, as there may be a need for continuous governance, monitoring, and analysis to ensure that the cluster continues to operate efficiently. To minimize the cost of maintenance, organizations should consider using proper tools, automation, and processes to monitor and manage their cluster.

Stages of Kubernetes Cost Optimization

‍As we’ve seen - Kubernetes cost optimization can be risky and requires a meticulous, scientific approach to get right without causing the issues we’ve outlined in the previous section.

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To effectively and continually optimize you Kubernetes environment, a structured strategy is vital. It comes in three stages: Gaining Visibility, Taking Owner-led Actions, and Allowing Autonomous Rightsizing. By following this method, you'll create and maintain a finely-tuned Kubernetes setups that efficiently uses resources and saves costs.

Stage 1: Gaining Visibility

Getting comprehensive visibility is extremely important, and any gaps could impact the effectiveness of later stages.
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Here are the levels of visibility you need:

• Infrastructure Cost and Utilization Data: Gather information from cloud billing data andinfrastructure monitoring tools.These are foundational data points required for your optimization journey.
• Business Efficiency Metrics: Identify metrics that directly align with your business goals. For instance, if you're running an e-commerce platform, a relevant metric might be "transactions per second" or "revenue per user." This metric will help you gauge the impact of optimization on your business and on your customers.

Take a look at the Top Kubernetes Observability Tools

Stage 2: Taking Owner-lead Actions

Now you have the data point you begin to put tangible values on the Kubernetes cost optimization efforts. You can think of this as the return on investment (ROI) of optimization.  

• Quantify the Impact: In this step, you determine the costs that can be avoided by right-sizing underutilized or inefficient resources. This will help you better understand your potential savings and prioritize the actions you take based on monetary values.
  
  
• Cost of Action: Calculate the cost required to perform optimization actions. Evaluate the time, effort, and resources needed. For example, calculate the hours required to implement a change and the associated hourly rate. You can now subtract your cost of action from your potential savings to get a preliminary ROI on your optimization efforts.
• Manual Review and Action: Review optimization recommendations generated either manually or by  specialized tools. These recommendations could range from resizing resources to adjusting storage classes, HPA or ClusterAutoscaler thresholds. Start taking the appropriate manual actions based on these suggestions, monitor the results, and compare them to your preliminary ROI projections to determine if you are driving the optimal results.

Depending on the scope of the work needed to optimize your Kubernetes environment, manual efforts could drastically reduce your ROI, and impact other projects and initiatives your teams have.

If this is the case, you are ready for stage 3, automating the Kubernetes cost optimization process.

Stage 3: Automated Kubernetes Cost Optimization

In this stage, continuous optimization becomes a well-oiled machine driven by data and automation.

Here's how to achieve this advanced level of K8s cost optimization:

• Automated Processes: Develop scripts or use specialized tools that process cost and utilization  data and trigger actions based on predefined thresholds.
• Automated Alerts: Implement alert mechanisms that notify you when anomalies or suboptimal conditions are detected. These alerts can prompt humans to analyze the situation or trigger automated actions.
• Automated Actions: Set up processes to automatically resize or stop/start compute resources based on real-time data. Continuous Improvement: Continuously monitor and evaluate the efficiency improvements.

As you can see, Kubernetes cost optimization is a journey that progresses from the initial stage of gathering data to the advanced level of automated kubernetes cost optimization efficiency.

By following the above stages, you'll transform your Kubernetes environment into a finely tuned system that maximizes resource utilization, minimizes costs, and aligns with your business objectives without compromising the stability of your environment.

Kubernetes Cost Management: 7 Tips for K8s Cost Optimization

When starting out with Kubernetes cost optimization it's important to understand what to focus on. Redundant costs come from 2 main sources: wasted resources and idle resources. Both of these are usually caused by over-provisioning, intentional or unintentional. On the other hand - thoughtless cost reduction activity can lead to under-provisioning, which causes performance and reliability issues.

When optimizing our cluster costs we want to focus on all of these areas iteratively - in order to keep our clusters as cost-effective and performant as needed.

Now let's explain each of these focus areas in more detail.

Cost Monitoring in Kubernetes

Establishing a robust monitoring strategy is crucial. Kubernetes offers various tools and features to track resource utilization effectively. Utilize monitoring tools such as Prometheus and Grafana to gain insights into your cluster's performance. Monitoring not only helps in understanding resource usage but also aids in recognizing trends that may indicate increasing costs.

Integrating cost monitoring to track expenses on cloud provider invoices allows teams to identify spikes in costs. Establish alerts for when spending exceeds predefined thresholds to react promptly before costs escalate. Furthermore, consider leveraging the use of Tagging Resources effectively to categorize and measure costs against specific business units. This can also facilitate accountability within teams, as departments can see their own usage and costs, fostering a culture of responsible resource management.

Setting Resource Limits for Efficiency

Another significant strategy in cost management is setting resource requests and limits for pods. Defining the appropriate resource requests helps Kubernetes schedule pods efficiently while ensuring that no single pod monopolizes resources. This balance is vital for maintaining overall cluster health and preventing scenario overloads.

Moreover, by setting limits, organizations can prevent unexpected spikes in resource consumption that lead to increased costs. Conducting thorough assessments of application requirements can help pinpoint effective resource limits, optimizing both performance and cost simultaneously. Regularly reviewing these limits as applications evolve is also essential, as workloads can change over time, necessitating adjustments to resource allocations to avoid waste.

Leveraging Autoscaling for Cost Savings

Utilizing Kubernetes' autoscaling features is a powerful way to manage costs dynamically. With Horizontal Pod Autoscalers, clusters automatically scale the number of pods based on current demand. This ensures that resource allocation aligns with workloads, preventing excess spending during off-peak times.

The Cluster Autoscaler works similarly at the node level, allowing your infrastructure to adapt in real-time. By bringing down nodes that are no longer needed, you can save on cloud infrastructure costs without negatively impacting application performance. Additionally, implementing Vertical Pod Autoscalers can further enhance resource efficiency by adjusting the resource requests and limits of running pods based on their actual usage patterns, ensuring that every pod operates within its optimal resource envelope.

›› Learn how PerfectScale plus Karpenter can provide and additional 30 to 50% in cost reductions on top of what can achieved with Karpenter alone.

Utilizing Discounted Computing Resources

Many cloud providers offer discounted computing options, such as Spot Instances or preemptible VMs, which can significantly lower costs. Integrating these resources within your Kubernetes architecture can optimize expenses, especially for non-critical workloads that can handle interruptions.

When implementing this strategy, ensure that your application design is resilient to pod termination, thereby maximizing savings while maintaining service availability. Balancing cost savings with reliability is key when utilizing discounted resources in a production environment. Furthermore, consider using a mix of on-demand and discounted resources, allowing for a flexible approach that can adapt to varying workload demands while keeping costs in check.

›› Learn how preemptible pods can prioritize critical workloads and optimize node utilization.

Implementing Sleep Mode for Idle Clusters

For development clusters or environments that do not require 24/7 availability, consider implementing a 'sleep mode'. This means shutting down idle clusters after hours, thus saving costs during periods of inactivity.

Automation tools and scripts can help facilitate this process, ensuring that clusters automatically power down based on usage patterns and automatically power up when needed. This approach offers a straightforward method to manage costs without sacrificing the availability of resources for urgent tasks. Additionally, establishing clear policies around cluster usage can help teams understand when to utilize resources efficiently, promoting a culture of cost awareness and operational efficiency.

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Regular Cleanup for Cost Efficiency

Regular maintenance is essential for cost efficiency. This includes timely deletion of unused resources such as old deployments, job histories, and lingering persistent volumes. Kubernetes can accumulate resources over time that may not be actively in use, leading to unnecessary costs.

Establishing a cleanup policy can streamline resource management and help identify stale resources before they become a burden on budget. Implementing automated cleanup processes ensures ongoing cost efficiency without requiring constant manual oversight. Moreover, incorporating a tagging system can assist in identifying resources that are no longer needed, allowing for targeted cleanup efforts that minimize the risk of accidentally removing critical components.

Implementing Resource Quotas

Resource quotas are a feature in Kubernetes that allows you to limit the total amount of resources that can be consumed by a namespace. By implementing resource quotas, you can prevent a single application or team from consuming too many resources and driving up your costs.

Resource quotas can be a powerful tool for cost optimization, but they require careful planning and management. You need to ensure that your quotas are set at the right level to prevent resource starvation, while also preventing over-consumption of resources.

Kubernetes Cluster Size Best Practices To Remember

Although it’s tempting to begin rightsizing your most costly applications and environments, the best way to get quick results is to begin with lower systems that can be easily identified as over-provisioned. This type of low-hanging fruit offers a great first testing opportunity. With increased understanding comes the willingness to allocate additional time and resources to rightsizing your workloads.

1. Be cautious by over-provisioning (setting generous limits or requests) the first time you deploy to production. You can always lower them after you understand your true needs.
2. Go small by utilizing numerous small pods instead of a few large ones. This move will provide higher availability for your applications by default.
3. Don’t run too many pods, as it can lead to resource exhaustion and/or overload by creating too many open connections on your server, making troubleshooting difficult. This undermines the debugging process and can slow down application deployment.
4. Review your past resource usage periodically and perform corrective actions where necessary. Measuring and analyzing capacity utilization over time is the best way to avoid consuming too many resources.
5. Test workload performance on rightsized instances to ensure performance does not suffer. If you have high and low environments for the same workload, right-size the lower ones first then use load testing tools to evaluate performance.
6. Prioritize and remediate issues:  (over and under-provisioning)
7. Complete the feedback loop by communicating regularly with developers. This move can help them provide more accurate capacity requirements in the future.
8. Repeat steps 4-7 on a regular basis. Both utilization and demand can change over time, meaning what is rightsized today may not be in three months.

It is good to remember, Kubernetes is ephemeral. Even after your settings are established, you will need to regularly monitor your containerized environment. Do you have all the tools you need to empower DevOps teams with this level of visibility? Configuration and overall optimization are key ways to ensure resource efficiency and security are always present—and that your Kubernetes workloads have just the right amount of resources for success.

Kubernetes Cost Optimization with PerfectScale

Maintaining high performance and availability for applications running on a Kubernetes cluster, while continuously pursuing Kubernetes cost optimization is a critical concern for organizations. Regular monitoring and evaluation of the K8s cluster resources are essential in making the adjustments needed to ensure your cluster continues to operate efficiently, and that applications continue to perform as expected.

PerfectScale makes it simple to continuously optimize your Kubernetes clusters. We provide complete visibility across your multi-cloud, highly-distributed Kubernetes environment and allow you to quickly drill down into individual services, workloads, and containers that need your attention. Our AI-guided intelligence analyzes the dynamic usage patterns of your Kubernetes environment to understand the requirements needed to meet the demand of your application. This allows us to provide precise recommendations on how to optimally configure the size and scale of your environment, allowing you to easily and effortlessly improve system reliability, performance, cost-effectiveness, and environmental sustainability.

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