Learn how to optimize costs in Kubernetes, from right-sizing nodes to leveraging quotas and usage-based cost allocation reporting and alerting.
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Kubernetes Cost Optimization

Kubernetes dominates container orchestration. It is celebrated for its ability to scale, resilience, and flexibility. It boasts widespread integration across numerous sectors.

Kubernetes cost optimization is essential to ensure efficient resource usage and budget control. Adopting cost-optimization best practices can lead to substantial savings and enhance operational efficiency.

With adoption and growing environments comes the challenge of cost management. As Kubernetes environments expand to meet the growing demands of applications and services, they raise complex resource management issues. This growth often leads to inefficient resource utilization, which can quickly escalate costs and strain budgets.

This article delves deep into Kubernetes cost optimization, shedding light on the challenges and complexities of scaling and managing Kubernetes clusters. We explore the best practices for effective cost management, from implementing auto-scaling to optimizing resource allocation and employing financial governance mechanisms.

Along the way, we’ll see how third-party platforms, like Kubecost, can take cost optimization to the next level. By embracing the strategies described below, organizations can navigate the financial intricacies of Kubernetes, ensuring a balanced approach between performance and cost and paving the way for sustainable growth in the cloud-native landscape.

The goal of cost optimization is to find a balance between performance and spending (source)

Best practices in Kubernetes cost optimization

Best practice Description
Right-size Kubernetes nodes Over-provisioning of nodes is a leading factor in creating unnecessary costs. Ensuring that workloads have sufficient capacity while not leaving idle resources is a key to controlling costs.
Scale clusters effectively Clusters need elasticity to grow and shrink based on demand. However, incorrect configurations within the tools that enable this flexibility can lead to significant cost increases.
Optimize storage use Orphaned volumes within a cluster incur costs. Ensure that you are using all of the capabilities within Kubernetes to understand how your volumes are provisioned and whether they’re being attached correctly.
Employ monitoring and logging Monitoring and logging play a significant role in creating a holistic picture of a cluster's operation. Ensure you select the tool that makes the most sense for your organization.
Leverage quotas within namespaces Use Resource quotas within namespaces to limit resource consumption. Understand the implications of this and whether it can work within your organization.
Use requests and limits Limits and requests allow for the precise allocation of resources to pods. By setting limits, you prevent any single application from consuming too many resources. Requests ensure that each application receives the minimum resources needed to function correctly.
Implement usage-based allocation reports and alerts Alerting and reporting are key to the successful cost optimization. Ensure teams receive clear and concise reports on costs and are alerted when thresholds are breached.

Key factors contributing to Kubernetes costs

Before optimizing, you must first understand how costs arise within clusters. The following elements are the key aspects contributing to the cost incurred:

  • Compute resources: A Kubernetes cluster is built on computing resources like AWS EC2 instances, Azure VMs, GCP instances, or traditional on-premises servers. These resources determine the computational capacity available for applications. Each major platform has its own method of providing computing power.
  • Storage costs: Persistent storage costs depend on the required capacity and access speed. This aspect is vital for applications that require high I/O throughput.
  • Network expenses: While this may not be the first cost factor that comes to mind, the expenses associated with networking—including the costs for data transfer and bandwidth consumption across cloud regions—can be significant. Monitoring these costs is crucial to prevent unexpected increases in spending.

Identifying the factors contributing to Kubernetes’s total cost helps provide useful context for the best practices described below.

Comprehensive Kubernetes cost monitoring & optimization

Right-size Kubernetes nodes

Incorrect sizing, whether caused by selecting the wrong instance type or misusing limits and requests on the container level, is common within Kubernetes clusters. This can significantly influence overall expenses due to either underprovisioning or overprovisioning. To tackle this, consider the following specific approaches for right-sizing.

Monitoring and alerting

  • Utilize tools for insights: Employ tools like Kubecost to provide utilization metrics for nodes and containers, enabling precise adjustments that consider the potential impact on the application hosted on the underlying node.
  • Set up alerts: Configure alerts to notify administrators when usage exceeds thresholds, allowing quick remediation.

Leverage cloud platform discounts

Most cloud platforms offer a discount in exchange for a long-term commitment. This includes instances that aren’t guaranteed uptime or availability (think spot instances) or compute that requires a contractual agreement of one to three years. Ensure you understand what your platform offers you in exchange for staying with them long-term. As long as you are committed to staying within your provider’s ecosystem, this often makes sense and leads to substantial savings.

The savings can vary from provider to provider ranging from 30% up to a 72% discount.

Platform Savings
AWS Up to 72% savings
GCP Up to 30% savings
Azure Up to 65% savings

Right-sizing containers

Optimizing the utilization of our cluster goes beyond just right-sizing nodes—it’s imperative also to optimize containers:

  • Limits and requests: Set appropriate limits and requests to ensure balanced resource distribution without compromising node performance. This is discussed in more detail further below.
  • Leverage third-party platforms: Use a platform like Kubecost to analyze usage and recommend resource configurations that match actual needs, preventing resource wastage.

As you can see, Kubecost can play a pivotal role in right-sizing nodes and containers. By providing critical data and recommendations, engineers and administrators can obtain information they might otherwise miss, leading to cost optimization that shows up on the organization’s bottom line.

Scale clusters effectively

Effective cluster scalability is pivotal to managing Kubernetes costs. Dynamic scaling aligns resources with application demands, ensuring that workloads run smoothly without accruing costs due to misconfigurations.

Key tools include Kubernetes add-ons: the Vertical Pod Autoscaler (VPA) and Horizontal Pod Autoscaler (HPA). The VPA fine-tunes a pod’s CPU and memory limits to match current consumption patterns, while the HPA adjusts pod replica counts in response to CPU usage or other specified metrics. Additionally, solutions like the Cluster Autoscaler and Karpenter offer cloud-based Kubernetes environments the capability to scale at the node level, automatically modifying cluster size to suit workload requirements by either provisioning new nodes or decommissioning unnecessary ones.

As you manage your Kubernetes environment, it’s vital to follow established best practices for scaling to ensure efficiency and cost-effectiveness.

Leverage resource-based scaling policies

Use CPU and memory metrics to inform scaling actions, guaranteeing that resource distribution aligns with real-time demand instead of fixed benchmarks. Scaling can go far beyond the use of basic metrics like CPU and memory, however, and it’s important to have a deep understanding of how to serve the application in question best. Tools like Keda provide event-based scaling options, and Kubernetes supports the ability to scale on various metrics, allowing administrators flexibility in how they design their scaling patterns.

The different flavors of auto-scaling within Kubernetes (source)

K8s clusters handling 10B daily API calls use Kubecost

Determine the right tool or platform

Selecting the right scaling solution is crucial to avoid complexity and enhance operational efficiency. Assess tools for scalability, ease of integration, and their ability to support your specific workloads. After that, consider the aspects of community support and the tool’s maturity.

Platforms like Kubecost fit right into this process. KubeCost offers insights into fine-tuning scaling strategies—whether using HPA and VPA for container scaling or Cluster Autoscaler and Karpenter for node scaling. This harmonized approach means your scaling solutions will meet your immediate needs and align with your organization’s long-term strategic objectives.

Optimize storage use

Kubernetes offers features like storage classes, persistent volume claims (PVCs), and automated volume resizing to manage storage more dynamically and cost-efficiently. Storage classes define the types of storage offered, each with different costs and capabilities, allowing for precise alignment with needs. PVCs enable the allocation and management of storage on demand, ensuring that applications have the storage they need when they need it.

Being able to resize volumes on the fly, even when in use, is particularly important for optimizing infrastructure. It ensures that storage scales with application needs, avoiding both underutilization and overprovisioning.

Best practices for storage optimization include:

  • Audits: Regularly auditing and cleaning up orphaned volumes can substantially reduce costs. Ensure that your team is making time for this task quarterly.
  • Dynamic provisioning: PVCs and storage classes allow for semi-automated dynamic provisioning of storage space. Use this feature to offload some of the work of optimizing storage.
  • Automatic volume resizing: It’s not enough to focus on removing orphaned volumes—organizations also need to utilize the ability to resize a volume dynamically. Far too often, storage remains underutilized, incurring unnecessary expense when it could easily be lowered in size to reduce costs.

Employ monitoring and logging

As with any other tool or platform, monitoring and logging are crucial for overall health. Tools such as Prometheus and Grafana offer a powerful, open-source combination for tracking metrics and visualizing data, allowing teams to pinpoint inefficiencies and adjust resources accordingly. Cloud-specific tools like Azure Monitor and AWS Cloudwatch provide integrated solutions tailored to their respective environments, facilitating detailed insights and streamlining Kubernetes resource metrics directly from the cloud provider.

Prometheus and Grafana

This duo is renowned for its flexibility and broad community support, not to mention its open-source origins and free availability, enabling detailed monitoring that can drive significant cost optimizations.

Grafana allows you to easily see all the pertinent details you need on one dashboard (source)

Learn how to manage K8s costs via the Kubecost APIs

For a more detailed look at Grafana and Prometheus, take a look at our Grafana and Prometheus guides.

Kubecost

While not directly monitoring metrics or utilization, Kubecost focuses on the financial aspect based on usage. By analyzing spending patterns of resource types and suggesting optimization measures, KubeCost offers a unique perspective on cost management.

Through leveraging the reports provided by Kubecost, organizations can better understand the allocated costs of their Kubernetes clusters by project, workspace, team, or pod, ensuring that resources are managed efficiently by the team directly responsible for them.

Kubecost also generates cost alerts when allocated costs exceed preset budgets. It integrates with AWS, Azure, and GCP to offer a comprehensive view of an organization’s infrastructure costs. Kubecost is free forever for individual clusters and is also available as a hosted service.

Kubecost provides users with a clean and concise console outlining all of their cluster costs (source)

Leverage quotas within namespaces

Quotas within namespaces are essential for effective resource management. By establishing quotas, administrators can cap the resources allocated to each namespace, such as CPU, memory, and storage, ensuring no single namespace monopolizes cluster resources excessively.

Quotas are crucial for managing costs, as they limit the spending incurred by the amount of resources deployed in any single segment (or namespace) within the cluster. This is especially critical in cloud environments, where resource consumption directly correlates with expenses.

Resource quotas should be regularly monitored and adjusted to align with evolving workload demand. Like right-sizing containers through limits and requests, quotas can be utilized alongside these settings for a more nuanced and effective approach to resource management within namespaces. This combination facilitates a comprehensive strategy for managing resource allocation and optimizing the operational efficiency of Kubernetes clusters.

Use requests and limits

Requests and limits are Kubernetes constructs designed to control the resources that workloads can consume, such as CPU and memory. This mechanism allows organizations to allocate cluster resources, preventing overallocation and waste.

The 2023 State of Kubernetes Report determined that setting your requests is the most important thing administrators can do to control costs within their clusters. These constructs are complex and easily misconfigured, so it’s important to ensure that you fully understand the implications of workloads when configuring these parameters.

Understanding requests and limits

Knowing the difference between requests and limits is the key to effective resource management. A request specifies the minimum amount of a resource a container needs to run, essentially receiving a portion of the full resource on the underlying node. On the other hand, a limit sets the maximum resource amount that a container can use, preventing it from exceeding a specified threshold and ensuring a fair distribution across all workloads.

Optimizing with Kubecost

Kubecost offers integrations that assist with setting requests based on observed utilization, enabling a more dynamic and data-driven approach to resource allocation. The platform specializes in fine-tuning Kubernetes resource requests based on actual usage data, which is critical for maintaining a cost-efficient cluster. Using real insights data ensures that resources are neither underutilized due to overprovisioning nor strained by underprovisioning, striking a balance that optimizes performance and cost.

The methodology behind the platform emphasizes a proactive stance on resource management, encouraging continuous monitoring and adjustment of requests to reflect changing workload patterns.

Kubecost has partnered with StormForge, a company specializing in automating setting requests and limits based on machine learning algorithms and integrating with Kubecost.

Implement usage-based allocation reports and alerts

By utilizing detailed reports that generate clear and concise alerts, organizations can elevate their ability to fine-tune their resource allocation and spending.

Enhanced optimization with alerting and reporting

Alerting and reporting functions are indispensable for advancing Kubernetes cost optimization. They empower organizations to monitor resource utilization in real-time, ensuring that spending is always aligned with actual usage. This proactive stance enables the identification of inefficiencies and potential savings opportunities, facilitating a more strategic allocation of resources.

Utilizing Kubecost for precision

Kubecost provides native tools designed to harness the power of alerting and on-premises monitoring, making it easier for businesses to implement a usage-based cost allocation model by offering the following:

  • Alerting: Kubecost seamlessly integrates alerting into different platforms, allowing administrators to receive real-time information when thresholds are breached.
  • On-premises monitoring: While most Kubernetes deployments are on the cloud, the need to monitor costs within on-premises deployments is still present. Kubecost offers an elegant means of obtaining the necessary data to make informed decisions on optimizing these clusters.

Conclusion

Organizations can significantly enhance operational efficiency by managing resources, paving the way for advanced technological deployments and more effective project execution. Navigating the complexities of cost optimization requires sophisticated tools capable of providing detailed insights and actionable recommendations. This is where Kubecost comes into play, offering a comprehensive solution to resource management and expenditure control challenges.

The real value of cost optimization transcends mere savings. It frees up funds for reinvestment in the organization. The funds saved can fuel innovation, drive growth, and foster an environment where creativity and technological advancements thrive. It’s a cycle that boosts the bottom line and moves the organization forward in a competitive landscape.

Integrate a platform like Kubecost to take charge of cost optimization. Kubecost not only simplifies the optimization process with its targeted insights and alerts but also ensures that every dollar spent is an investment toward efficiency and innovation. By leveraging Kubecost alongside the best practices outlined in this article, organizations can achieve a balanced approach to performance and cost, setting the stage for sustainable growth and cutting-edge development in the cloud-native ecosystem.

Comprehensive Kubernetes cost monitoring & optimization

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