Kubernetes has long established itself as the de facto standard for container orchestration engines. However, as the complexity of Kubernetes environments continues to grow, so does the associated cost.
According to the CNCF "FinOps for Kubernetes" survey report, 68% of respondents indicated an increase in Kubernetes expenses, with half of the organizations experiencing annual cost growth exceeding 20%.
Therefore, effective management and optimization of Kubernetes costs demand careful attention from system administrators. In this blog, we explore six strategies and methods for optimizing Kubernetes costs.
1. Fine-Tune Pods and Nodes
One of the simplest ways to reduce costs is by managing the resources used by Pods and nodes. While it's common advice to leave sufficient headroom, over-configuring or allowing applications unrestricted resource usage can lead to disastrous consequences. Setting Kubernetes resource quotas and limit ranges at the namespace level can prevent resource abuse. Additionally, specifying resource requests and limits at the container level enforces how much resources a container can request and the maximum limit.
Node size should align with the resources consumed by Pods. If your workload utilizes only 50% of the node resources, and resource usage is not expected to spike in the short term, consider scaling down the node size to cut costs. Adjusting the number of Pods that can run on a single node is also crucial, as running many Pods on a single node, even without hard limits, can result in inefficient resource utilization.
2. Monitor Cluster and Infrastructure
Effective monitoring of the cluster environment, including underlying or dependent resources, contributes to cost management. Whether using managed Kubernetes clusters or self-hosted clusters, monitoring resource utilization and overall costs is the first step in cost reduction. This provides insights into compute, storage, network utilization, and how costs are distributed among them.
Cloud providers typically offer built-in tools and basic monitoring features. Utilizing tools like Prometheus, Kubecost, and Walrus, which includes built-in cost management views, allows users to gain comprehensive insights into Kubernetes resource expenses, shared costs (such as idle and management costs), and multidimensional cost analysis.
3. Configure Elastic Scaling
Kubernetes supports three types of elastic scaling:
HPA (Horizontal Pod Autoscaler): Automatic horizontal scaling.
VPA (Vertical Pod Autoscaler): Vertical automatic scaling.
Cluster Autoscaler: Cluster automatic scaling.
Fully leveraging Kubernetes elastic scaling features helps users efficiently reduce overall Kubernetes costs. HPA monitors Pod usage and automatically adjusts sizes to maintain the desired usage level. VPA adjusts resource requests and container limits in the cluster. Autoscaling adds or removes nodes from the Kubernetes cluster based on demand. This ensures that workloads always have enough infrastructure resources to complete their tasks without paying for idle infrastructure.
At present, not all Kubernetes services or distributions support automatic scaling. However, if the service you use supports it, it can significantly reduce Kubernetes costs.
4. Choose Different Purchasing Strategies for K8s Workloads
For AWS or GCP, on-demand instances are the most expensive option. Therefore, it's advisable to make full use of reserved instances or even Spot instances (bid-based instances). Compared to on-demand prices, Spot instances can offer up to a 90% discount. They are an excellent choice for short-term work or stateless services that can be quickly rescheduled without data loss. To avoid interruptions, users can use workload management tools to reserve Spot instances for a fixed period.
Plan the purchasing strategy for each node, prioritizing the use of Spot instances where possible to maximize purchasing discounts. If Spot instances are not suitable for your workload, such as running databases in containers, consider purchasing nodes with stable availability. In any case, try to minimize the use of on-demand resources.
5. Kubernetes Scheduling
After adjusting Pod and node sizes and scales, ensure that Pods are scheduled on the correct nodes. Kubernetes scheduling matches Pods with nodes, and the scheduler's default behavior is customizable. If, for example, you want to place containers with critical business functions on a high-performance node and less critical components on a relatively low-performance node, default behavior cannot achieve this.
If a non-critical Pod is scheduled on a high-performance node, it leads to performance waste and ultimately increases costs. Kubernetes provides features such as nodeSelector, affinity, taints, and tolerations to address such issues and optimize scheduling. These features fully configure Kubernetes' scheduling process to meet user requirements, enabling efficient use of available resources across nodes.
6. Simplify Development
While the wave of containerization is strong, not everything needs to be containerized. Some development teams attempt to containerize applications or workloads for the sake of containerization, leading to unnecessary workloads running on Kubernetes clusters. These workloads can run easily on other technologies at a lower cost. For instance, serverless technology can be used for event-based features, while Kubernetes is reserved for high availability and critical task functionalities.
Conclusion
Cost management may not always be the top priority for developers, but it is undoubtedly a crucial aspect to consider. The right solutions can make Kubernetes cost management timely, economical, and effortless, allowing businesses to achieve a perfect balance between cost and performance.
Establishing a multi-cloud environment using different cloud providers can benefit users from discounts offered by each platform. It also allows workload migration between platforms to choose the most cost-effective option without service interruptions or compromised service quality. Additionally, selecting different technologies to offload functionality to the most suitable technology or service can provide deeper cost management while effectively managing the entire application.
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