Kubernetes has become the de-facto choice for most users when it comes to container orchestration. The popularity of cloud-native developments has brought technologies like containerization and microservices to the forefront.
Tools like Kubernetes play a key role by providing a feature-rich, robust and secure orchestration platform for containerization. One aspect that any Kubernetes administrator must look into is managing Kubernetes costs. So, in this article, let’s look into some ways to reduce overall Kubernetes costs.
The primary way to manage costs is to properly monitor the cluster environment, including underlying or dependent resources. Monitoring resource utilization and overall costs are the first steps towards reducing costs regardless of whether you use a managed K8s cluster or a self-hosted one. It allows users to gain a better understanding of computing, storage, network utilization, etc., and how costs are distributed between them.
In-built tools and basic monitoring functionality offered by cloud providers can help in this aspect. However, it’s always advisable to utilize tools such as Prometheus, Kubecost, and Replex for a comprehensive understanding. It enables K8s admins to gain a more comprehensive view of the environment and optimize the costs.
One of the easiest ways to reduce costs is to manage the resources used by Pods and Nodes. While it is always advisable to have enough headroom, overprovisioning or allowing applications to use unlimited resources can lead to disastrous consequences. For example, let’s assume that a Pod consumes all the available memory from the node due to an application error unnecessarily utilizing resources and making other pods starve for resources. Users can prevent such situations by limiting resource utilization with Kubernetes Resource Quotas and Limits Ranges at a namespace level. Additionally, they can specify resource requests and limits at a container level that enforce how many resources a container can request and the maximum limit.
Rightsizing nodes is up to the resources used by Pods. You can safely downsize the nodes reducing costs if you have a workload that utilizes only 50% of the resources available in the node, and you do not see a resource usage increase in the near future. Another consideration is the number of Pods that can be run on a single node. Even though there is no hard-set limit, running a large number of Pods can lead to inefficient resource utilization. Due to this issue, managed K8s service providers like EKS and AKS have limited the number of Pods that can be run on a node.
After rightsizing pods and nodes, the next step is to ensure that the correct pods get scheduled on the correct nodes. The K8s scheduling process matches pods with nodes, and the default behavior of the scheduler can be customized to benefit users. Assume that you want to put containers with business-critical functionality on a high-performance node and other less critical components on relatively lower-performance nodes. By default, K8s does not have a way to match the correct nodes even if you provision nodes on different performance tiers.
This problem can lead to wasted performance and ultimately increased costs if a non-critical pod gets scheduled on a high-performance node. Kubernetes provides features such as nodeSelector, Affinity, and Taints and Tolerations to mitigate this issue and optimize the scheduling. They can be used to fully configure the Kubernetes scheduling process to match user needs, thus allowing users to efficiently use the resources available throughout the nodes.
Every aspect of the development need not be containerized. Some development teams try to containerize applications or workloads for the sake of containerization, which can lead to an unnecessary amount of workloads running on a Kubernetes cluster. This workload can be easily offloaded to other technologies, and more often, it will be comparatively cheaper. A good example is to use serverless functions for event-based functionality and mainly Kubernetes for high availability and mission-critical functionality.
Implementing cloud-native best practices across the delivery pipeline can be a tedious and time-consuming task. DevOps has greatly reduced the gap between Dev and Ops aspects of the delivery pipeline and allowed users to create robust and flexible delivery pipelines. The best way to reduce the time and effort to deploy containers to the Kubernetes cluster is to integrate K8s deployments as an automated part of the delivery pipeline. Practices like GitOps are tailor-made to facilitate continuous delivery at an infrastructure level, greatly reducing the deployment workloads of the team.
An upfront investment will be needed to properly set up everything from the continuous integration to build, test, and publish containers and continuous delivery to deploy these containers on the cluster. Tools like Jenkins for CI, ArgoCD, and Flux for CD are ideal for creating such delivery pipelines. A properly integrated delivery pipeline can have far-reaching benefits in the long term to reduce deployment workload and organically introduce best practices and standardization across the development environment. Additionally, it will significantly reduce the chance of misconfigurations or human errors that cause errors within the cluster and disruptions in the application leading to less troubleshooting. This reduced workload will allow teams to focus on more valuable tasks such as functionality development, bug fixes, and improving the security posture of the environment.
One good way to reduce costs is to utilize different cloud providers and set up a multi-cloud environment. This multi-cloud environment enables users to benefit from cost-savings offered by each platform and even move workloads between platforms to the most affordable option without service disruptions or decreases in service quality. Kubernetes will manage all these things. Another option is to use different technologies and offload functionality to the technology or service that best matches the requirement. This option allows greater control over costs while efficiently managing the entire application.
CloudPlex is one of the best options for all these needs, allowing users to manage and run workloads across multiple cloud platforms and different technologies using a single platform. CloudPlex offers features such as a drag and drop tool to create K8s manifests or helm charts and comprehensive debugging functionality directly through your IDE. It even provides a complete monitoring environment to monitor your containers throughout their lifecycle. Why not give CloudPlex a try for your next Kubernetes development needs.
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