Kubernetes Cost Optimization Tools

Managing costs in Kubernetes clusters is a significant challenge due to their dynamic nature and the need for granular visibility, especially as clusters expand in size and complexity. While containers promise better performance, scalability, and resource efficiency, many organizations grapple with escalating expenses and underwhelming results.

This article highlights what you should look for in a cost optimization tool and how it aligns with the financial operations (FinOps) framework's three phases: inform, optimize, and operate. By reading this article, you will learn how to:

• Gain valuable insights: Understand resource utilization at a granular level
• Implement effective optimization strategies: Reduce costs without sacrificing performance
• Maintain continuous cost efficiency: Foster collaboration and accountability across teams

This article will equip you with the knowledge and tools to effectively manage and reduce costs in your Kubernetes environments.

When selecting cost optimization tools for Kubernetes, it's essential to look for features that provide deep visibility and actionable insights. The table below outlines the most important features and describes their importance in terms of cost optimization.

Cost allocation directly links Kubernetes expenses for resources like pods, services, and deployments to the teams or projects that use them. This connection is the foundation of effective resource management and accurate budgeting.

Tools for cost allocation

Effective cost allocation in Kubernetes requires tools that provide detailed insights while simplifying the process. Kubecost is a dedicated cost allocation tool that streamlines this process, enabling teams to gain deep insights with minimal effort. Having deep insights into individual Kubernetes objects and associating them with owners builds the habits that strengthen a FinOps culture across the company.

Kubecost allocates costs using Kubernetes' native labeling feature. Adding labels to resources lets you track costs associated with teams, projects, or environments.

labels:
  app: my-app
  team: alpha-team
  project: customer-portal
  environment: production

Once your resources are labeled, you can view your cost allocation reports within the Kubecost dashboard.

Other tools, such as AWS Cost Explorer / Azure Cost Management, provide overall cloud cost insight but lack Kubernetes-specific insights.

Kubecost’s deep integration with Kubernetes provides the best base for cost allocation and the FinOps framework.

FinOps framework and accountability

Another key to the FinOps framework is ensuring accountability across engineering teams. Each team member must understand that infrastructure spending is vital to project success and take pride in managing it well.

Technically minded people often focus on the engineering aspects of implementation, so it may require conscious effort and persistence to focus everybody on the FinOps principles. Usually, once they see the direct impact of their actions on the billing and the long-lasting effects on projects led by them and their peers, they will become incentivized to optimize recurring costs.

Your team can drive accountability by setting clear ownership guidelines and key performance indicators (KPIs) for spending. For example, you could ensure that your teams label resources appropriately and are educated on resource optimization best practices.

Chargeback vs. showback

Implementing showback and chargeback practices is the next step in enhancing organizational accountability. These practices help teams understand resource usage and its financial impact, promoting more responsible and cost-effective behaviors.

Showback is a reporting practice in which costs are calculated and presented to teams or departments without charging them. It is an informational tool for teams to understand their spending patterns and resource utilization.

Chargeback goes a step further by billing the teams or departments for the actual costs based on usage. This practice enforces accountability by making teams financially responsible for their consumption.

Showback can serve as a starting point to inform teams of spending and prepare them to take a more accountable role. It is vital to educate teams during this process. Building documentation and hosting workshops or Q&A sessions improve understanding of ongoing changes and set the right mindset as you transition to chargeback.

Kubernetes clusters are often used in multi-tenant setups, where multiple teams or projects share the same infrastructure. To implement accurate showback and chargeback, you need more than just cluster-level insights—you need visibility down to the granular level of individual resources.

Traditional tools have challenges, such as limited granularity and the inability to correctly attribute resources directly to costs. Kubecost addresses these challenges by being able to attribute costs to specific teams by using labels and by providing real-time cost visibility. To learn more, visit https://blog.kubecost.com/blog/kubernetes-chargeback.

Extracting relevant data is necessary to optimize spending on Kubernetes clusters. The optimization should start with a detailed analysis of current and historical spending patterns to identify potential issues at the cluster level and down to individual Kubernetes objects.

In conjunction with cost allocation, the cost analysis process should identify patterns and inefficiencies that ultimately lead to informed decisions about cost-cutting measures.

Cost analysis is not a one-off process; it should be performed regularly, ideally with support from tools that can automate the reporting processes, such as KubeCost.

KubeCost integrates tightly with Kubernetes, offering granular insights into the cost and usage of resources such as CPU, memory, and storage across different namespaces, nodes, and workloads. Organizations can pinpoint precisely where their money is being spent and identify inefficiencies or areas for potential savings.

One common scenario is that after conducting a cost analysis using a tool like Kubecost, an organization can look at the built-in dashboards and discover that a significant portion of its Kubernetes budget is spent on idle resources during non-peak times. The analysis might show that some pods were over-provisioned, with requests far exceeding actual usage. With this knowledge, deployments can be tweaked to require fewer resources during non-peak times.

Here are some examples of optimizations based on insights performed by a typical organization:

• Right-sizing resources: By adjusting CPU and memory requests to match actual usage patterns, the organization reduced overall resource consumption, lowering its cloud bill.
• Automating scaling: Implementing the Horizontal Pod Autoscaler (HPA) ensured that microservices scaled up only during high-demand periods and scaled-down during low-demand times, eliminating idle resource costs.
• Cleaning up orphaned resources: Identifying and removing unused persistent volumes freed up storage, reducing costs without affecting any active workloads.

  As a result, the organization saw immediate cost savings by pinpointing specific inefficiencies and addressing them with targeted optimization strategies. These changes not only reduced unnecessary spending but also improved overall resource utilization and system performance.

  This is what differentiates KubeCost from other tools available on the market. Kubecost provides detailed insights that can serve as a base for further analysis. Other tools utilize predefined optimization strategies, so their purpose is limited; they are also focused more on cloud optimization, with a limited capacity for analyzing Kubernetes clusters. This may not necessarily be a bad thing, but it is essential to set clear expectations before using these tools. A less advanced team or a team at the beginning of FinOps methodology adoption may benefit from the simplicity provided by other cost-optimization tools. In contrast, Kubecost can serve as a long-term tool throughout all levels of FinOps maturity and Kubernetes adoption.

A proactive approach prevents spending surprises. With an alerting and monitoring solution, engineering teams can identify and resolve issues that would otherwise escalate into larger ones.

Budget management and alerting are vital components of the financial management of Kubernetes clusters. The practice involves planning, tracking, and controlling financial resources to ensure that they align with planned budgets and making necessary adjustments when thresholds are reached. Using automated alerting, organizations can ensure timely reactions to unexpected cost deviations and prevent increased billings. The added benefit is reducing the cognitive overhead of system administrators, who would need to monitor such metrics regularly through manual actions in case automation is unavailable.

Budget management and alerting utilize data provided through cost allocation and analysis, so any advantages and limitations of tools available on the market also apply to this section.

If a tool can not provide granular information on individual resources, it can not alert teams when thresholds are exceeded. Kubecost can effectively manage budgets and provide robust alerting systems for Kubernetes clusters down to the container level. Other tools with management capabilities exist, but they tend to be more focused on multi-cloud environments and non-Kubernetes resources.

Businesses need to understand and prepare for future spending. With all the complexities of today’s technology and modern software development processes, estimating the expected cost of the underlying infrastructure, including Kubernetes clusters, is becoming increasingly difficult.

Historical patterns are not always clear and easy to extract to support forecasting. Machine learning capabilities and predictive analytics are necessary to increase forecast accuracy. External tools such as Kubecost can help with the process by providing forecasts through visualizations and comprehensive tables across various areas, such as cost allocation, resource utilization, and cloud expenditure trends. Such a varied view enables forecasting across departments or infrastructure units, such as individual clusters.

Kubecost’s integration with cloud billing APIs and its ability to consolidate data from different sources ensure that forecasts are comprehensive and up to date.

Kubernetes provides unrivaled performance and flexibility, but that tends to go hand in hand with an increase in cost. It is commonly believed that containers can bring savings compared to the traditional approach of virtual machines, but in practice, that is rarely the case. Optimizing the underlying infrastructure and applications hosted on the clusters requires a conscious effort to ensure a cost-effective approach.

Typically, with limited insight into utilization patterns at the time of resource provisioning, application owners tend to over allocate resources. That is not an inherently wrong approach, but it should always be continually reviewed and optimization improved once more utilization data is available. A reliable monitoring solution, such as Prometheus, and tools to analyze utilization patterns, such as Kubecost, make the entire process much more accessible.

Optimization can take many forms. Monitoring and reviewing historical utilization data make it possible to determine accurate CPU and memory allocation for Kubernetes and maintain a balance between guaranteed resources (resource requests) and maximum usage (resource limit). The outcome of this exercise frequently leads to eliminating orphaned resources or changing the configuration of the underlying infrastructure. Determining key metrics and performance bottlenecks—such as CPU, memory, GPU, or other resources—is essential for establishing optimal node configurations. Cloud providers offer a wide range of VMs, each with different specifications and advantages, which must be considered when optimizing Kubernetes clusters.

Modern architectures on cloud providers make resource optimization more accessible. Autoscaling can provide additional resources on demand. A serverless approach abstracts the concept of virtual machines completely and allows administrators to focus on Kubernetes management. Cloud-managed Kubernetes services reduce the overhead of dealing with the Kubernetes control plane. Native integration with monitoring and logging services increases visibility without added complexity.

There is a series of articles diving deep into Kubernetes auto-scaling that expands on this topic: https://www.kubecost.com/kubernetes-autoscaling/

Here are some components of a resource optimization strategy that are worth keeping in mind when working with Kubernetes clusters:

• Monitor and adjust CPU and memory requests and limits to align with usage.
• Analyze historical usage data to set optimal resource allocations and avoid overprovisioning.
• Use the Kubernetes Cluster Autoscaler (or alternative solutions, such as Karpenter) to adjust the number of nodes based on cluster load, adding nodes when needed and removing them when underutilized.
• Consolidate workloads onto fewer nodes during low-demand periods to maximize resource utilization.
• Set resource quotas and limits for namespaces to control and manage resource consumption effectively.
• Implement the Horizontal Pod Autoscaler (HPA) to scale the number of pods based on workload demands automatically.
• Use the Vertical Pod Autoscaler (VPA) to adjust pod resource requests and limits. This method requires the pods to be restarted automatically to take advantage of the new settings.

You can learn more about implementing the HPA and VPA in this article on the Kubernetes Metrics Server.

External tools certainly make the optimization process much more straightforward. If Kubernetes clusters are hosted on cloud providers such as AWS, some services are available out of the box. AWS users can utilize AWS Cost Optimizer and AWS Trust Advisor to gain insights into potential resource right-sizing.

External tools can serve a similar purpose through a much more granular and user-friendly approach. They excel at analyzing cloud metrics and providing accurate optimization recommendations. Such optimization is based on cloud metrics, though, so it may not be sufficient for Kubernetes clusters. Adding Kubecost would provide additional insights into Kubernetes resources and serve as a robust stack to optimize a Kubernetes-based infrastructure.

The general rule of effective optimization is that it should lead to reduced costs and improved performance rather than sacrificing one for the other.

Keeping a Kubernetes cluster cost-optimized over time is challenging. This article looked at the challenges of getting it right. The FinOps organization outlines a framework of continuous steps: inform, optimize, and operate. These steps are meant to feed into each other, with the outcome being more optimizations and improvements over time.

The three steps translate to key practices. Cost allocation, cost analysis, budget management, and spending forecasting are all practices that, when done right, can improve performance while reducing costs. By implementing the right tools and following the FinOps methodology, organizations can achieve a balanced approach to managing their Kubernetes infrastructure.

Tools like Kubecost make following these practices easier. They provide detailed cost analysis, budget management, and alerting, making them indispensable for managing and optimizing Kubernetes clusters.

In the long run, everyone wins when optimization is done well. The budgets saved on optimizing unused resources can be reallocated to engineering departments for innovation rather than operations. Leadership can correlate actions taken to savings realized. The next step is taking action and optimizing your Kubernetes cluster while realizing savings over time by using Kubecost.