Kubernetes Cost Optimization - What Could Go Wrong???

Kubernetes cost optimization is challenging and can result in trade-offs between system performance and costs.

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In this blog post, we'll discuss what is Kubernetes cost optimization, 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 2024.

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Kubernetes Cost Optimization - What Could Go Wrong?

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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.

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

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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.

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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.

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Stage 1: Gaining Visibility

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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

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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.

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Take a look at the Top Automated Kubernetes Cost Optimization Tools

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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.

Start your free, 30-day trial of PerfectScale today.

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