Kubernetes cost optimization: Challenges and best practices

What Is Kubernetes cost optimization? 

Kubernetes cost optimization involves strategies to manage and reduce the expenses associated with running applications on Kubernetes clusters. As organizations scale their use of Kubernetes, costs can quickly escalate due to resource mismanagement, inefficient scaling, and unnecessary service utilization. Cost optimization aims to balance performance and expenses, ensuring operations remain within budget while maintaining necessary service levels.

Optimization requires understanding Kubernetes’s deployment environment. Organizations need to identify and minimize excess resource usage, configure clusters efficiently, and implement tools to gain visibility into cost drivers. The goal is to achieve optimal resource utilization without sacrificing operational capabilities or performance standards.

This is part of a series of articles about Kubernetes architecture

In this article:

Main factors influencing Kubernetes costs 

Here are a few common reasons for excessive Kubernetes costs:

  • Resource overprovisioning: Organizations often allocate more CPU and memory than necessary to prevent performance issues, but this leads to unused resources and inflated expenses. Understanding application requirements and using monitoring tools to track resource usage can mitigate overprovisioning.
  • Underutilized resources: Underutilized nodes, persistent volumes, or idle workloads can drive up costs unnecessarily. Nodes that are not fully utilized still incur charges, especially in cloud environments. Regular audits to identify and remove or consolidate these underused resources help optimize costs.
  • Scaling inefficiencies: Misconfigured scaling policies can lead to either insufficient scaling, causing performance issues, or excessive scaling, driving up costs. Utilizing Kubernetes’s autoscaling features effectively, such as Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler, ensures scaling matches workload demands.
  • Data transfer costs: In Kubernetes environments, workloads often communicate across clusters or regions, incurring data transfer fees. These costs are sometimes overlooked but can accumulate significantly, especially in cloud deployments. Optimizing network architecture and minimizing unnecessary inter-cluster communication can reduce these expenses.

Related content: Read our guide to Kubernetes monitoring

Cost impact of running K8s in the cloud vs. on-premises

Running Kubernetes in the cloud offers flexibility and scalability but often comes with higher operational expenses. Cloud providers charge for compute, storage, and network usage based on consumption, which can lead to unpredictable costs if not carefully monitored. Managed Kubernetes services, such as Amazon EKS or Google GKE, simplify operations but add service fees. However, cloud environments benefit from elasticity, supporting variable workloads.

Deploying Kubernetes on-premises provides greater control over infrastructure but requires significant upfront investment in hardware and ongoing maintenance costs. This approach is better suited for organizations with consistent workloads and existing infrastructure. While on-premises environments avoid the pay-as-you-go model of cloud, costs related to power, cooling, and personnel can still be substantial. Additionally, scalability is limited by hardware constraints, making capacity planning critical.

Challenges in Kubernetes cost optimization 

Complex and dynamic infrastructure

Kubernetes’s complex and dynamic infrastructure poses challenges for cost optimization. Dynamic scaling and frequent updates to environments result in changing cost patterns that are difficult to track manually. It requires tools capable of providing real-time insights and adjustments.

Lack of visibility into cost drivers

Limited visibility into cost drivers complicates efforts to optimize Kubernetes expenses. Without understanding which resources consume the most, financial management remains difficult. Teams may overlook subtler cost-generating aspects like data transfer fees or underutilized instances.

Difficulty in resource rightsizing

Resource rightsizing remains challenging in Kubernetes due to rapidly changing workload requirements. Over- or under-allocated resources lead to waste or performance issues respectively. Balancing these needs requires planning and the ability to predict resource requirements accurately.

Multi-tenancy and cost allocation issues

Multi-tenancy complicates cost allocation when organizations manage shared environments across multiple teams or projects. Properly distributing costs in such environments is complex and often leads to inefficiencies or disputed expense reports.

7 strategies for Kubernetes cost optimization 

1. Right-size Kubernetes nodes

Right-sizing Kubernetes nodes focuses on aligning node resources (CPU, memory, and storage) with the actual workload requirements to eliminate overprovisioning or underutilization. Regular monitoring of resource utilization helps identify inefficiencies, and tools like the Kubernetes Horizontal Pod Autoscaler or Cluster Autoscaler can dynamically adjust the allocation to match real-time demand.

Additionally, selecting the appropriate node types for workloads can significantly impact cost-efficiency. For example, memory-intensive applications benefit from memory-optimized instances, while compute-heavy workloads should use compute-optimized nodes. Reviewing performance periodically ensures that the chosen node configurations remain optimal.

2. Scale clusters effectively

Effective scaling of Kubernetes clusters is essential for balancing performance and cost. Kubernetes offers Horizontal Pod Autoscaler (HPA) to dynamically increase or decrease the number of pods based on demand, ensuring sufficient resources without unnecessary scaling. Configuring HPA with well-defined CPU or memory thresholds avoids over-allocation and reduces idle resource costs.

Cluster Autoscaler complements HPA by adding or removing nodes as needed. This node-level scaling prevents unused nodes from driving up costs during low demand. Additionally, tools like Karpenter can further enhance scaling precision by optimizing infrastructure at the node level, ensuring that clusters align with workload needs efficiently and economically.

3. Optimize storage use

Optimizing storage in Kubernetes involves selecting the right storage type, size, and lifecycle policies. High-performance SSDs can support I/O-heavy applications, while less critical data should use more affordable storage options like HDDs or archival tiers. Persistent Volume Claims (PVCs) and storage classes in Kubernetes enable dynamic provisioning to ensure applications get precisely the storage they need without over-allocating.

Automating volume resizing and cleaning up unused volumes can further reduce costs. For example, unused Persistent Volumes (PVs) often accumulate if not audited regularly. Implementing deduplication and compression techniques also helps minimize the total storage footprint, optimizing costs while maintaining data availability.

4. Leverage quotas within namespaces

Namespaces in Kubernetes allow administrators to organize resources by teams, projects, or applications, and leveraging resource quotas within namespaces ensures that no single namespace monopolizes resources. Administrators can set limits on CPU, memory, and storage usage per namespace, creating a fair distribution across workloads.

Quotas are especially useful in multi-tenant environments, as they help manage costs by controlling resource consumption at a granular level. Regularly reviewing and adjusting quotas based on workload changes ensures efficient utilization and avoids unnecessary expenses.

5. Use requests and limits

Requests and limits in Kubernetes define the minimum and maximum resources a pod can consume. Setting these values appropriately ensures that workloads receive adequate resources while avoiding overallocation. Requests guarantee the baseline performance, while limits prevent runaway resource usage that could negatively impact other workloads.

Accurate configuration requires analyzing historical usage data to determine typical and peak resource needs. Tools like Kubecost can assist by providing insights into resource utilization patterns, enabling administrators to set data-driven requests and limits. Regularly updating these parameters ensures they remain aligned with evolving application requirements.

6. Utilize discounted computing resources

Cloud providers often offer discounted computing resources, such as Spot Instances or preemptible VMs, which can significantly lower costs for non-critical or flexible workloads. Integrating these discounted resources into Kubernetes clusters requires careful planning, as they may be interrupted with little notice.

To mitigate risks, workloads using these resources should be designed for resilience, such as through retry mechanisms or checkpointing. Combining on-demand instances with discounted options creates a hybrid approach, maintaining reliability while optimizing costs. Regular monitoring ensures the balance remains effective as workload demands change.

7. Implement cost monitoring and reporting

Cost monitoring tools provide visibility into resource usage and associated expenses, enabling teams to identify inefficiencies and optimize spending. Solutions like Prometheus and Grafana can track cluster performance metrics, while specialized tools like Kubecost focus on analyzing cost data and providing actionable insights.

Dashboards and reports should highlight key cost drivers, allowing technical and financial stakeholders to collaborate on optimization efforts. Alerts for exceeding predefined thresholds help teams address issues before costs spiral out of control. Regular reporting aligns resource management with budget goals, fostering accountability across teams.

Automating Kubernetes infrastructure with Spot by NetApp

Spot Ocean from Spot by NetApp frees DevOps teams from the tedious management of their cluster’s worker nodes while helping reduce cost by up to 90%. Spot Ocean’s automated optimization delivers the following benefits:

  • Container-driven autoscaling for the fastest matching of pods with appropriate nodes
  • Easy management of workloads with different resource requirements in a single cluster
  • Intelligent bin-packing for highly utilized nodes and greater cost-efficiency
  • Cost allocation by namespaces, resources, annotation and labels
  • Reliable usage of the optimal blend of spot, reserved and on-demand compute pricing models
  • Automated infrastructure headroom ensuring high availability
  • Right-sizing based on actual pod resource consumption  

Learn more about Spot Ocean today!