Kubernetes deployment in cloud-native environments isn’t just important; it’s essential. Kubernetes simplifies and streamlines the process of deploying, managing and scaling containerized applications. This makes it much easier for major organizations to build and securely run cloud-native applications at the scale they need. Solutions such as TLS Protect for Kubernetes allows you to monitor the health and status of your security infrastructure, monitor health and status of cert-manager across all Kubernetes clusters, enjoy full visibility and consistency of your cloud-native machine identities, and so much more. But despite how important it is, many organizations are struggling to make the move to cloud due to cost hesitancies.
According to Venafi’s Report on the State of Cloud Native Security, which surveyed more than 800 security leaders and IT professionals, 77% revealed they experienced bill shock since moving legacy applications to the cloud. That same 77% are even reconsidering their cloud adoption, and are considering going back to their legacy solutions.
It is abundantly clear that the need for cost optimization in Kubernetes deployment is something that everyone is thinking about industry-wide. Let’s take a look at some of the main cost drivers in Kubernetes environments, strategies to better manage cost and resource allocation, cost optimization tips for the scaling of Kubernetes, and so much more!
Identifying Key Cost Drivers in Kubernetes Environments
There are several factors that will influence the cost of Kubernetes, each of which play a vital role in determining what the overall expenses of managing your Kubernetes environments will be. Properly managing the following cost drivers will help you minimize expenses, while maximizing the benefits of Kubernetes:
- Cluster Size: A Kubernetes cluster size will directly impact the cost. Cluster size is determined by the number of nodes, pods, and services. It will likely come as no surprise that the larger the Kubernetes cluster, the more resources that will be needed to manage it properly, and the more expenses will be incurred. Plus, larger clusters will increase the complexity of managing and securing them (which is something Venafi can help you out with).
- Node types: As explained above, Kubernetes clusters are made up of nodes. A node is an individual instance or virtual machine (VM) that hosts the containers. The type of node used will impact cost just as much as the quantity of nodes. Different node types can offer various levels of memory, storage capacity, and other features, and these will, of course, come with various price differences. It’s important to find the right balance of choosing node types that do have impressive specifications, but that don’t increase your costs unnecessarily.
- Resource utilization: This may be one of the most significant factors, and mistakes within resource utilization can wind up costing you big-time. If you are under-utilizing your resources, this can become a wasted investment. However overutilization of resources can cause you to incorrectly believe you need to upgrade to large or more nodes (which, as discussed above, increases costs even further).
Strategies for Efficient Resource Allocation in Kubernetes
We know that saving money is a major priority for today’s IT professionals and security leaders, but that doesn’t mean they’re willing to sacrifice performance. A careful allocation of resources can help your team strike the right balance of optimizing performance to meet your business needs in a cost-efficient way. Here are some strategies you can try:
- Consider Workload Requirements: One of the most important things you can do before you even begin to consider proper resource allocation is to understand your workload needs and requirements. You can utilize third-party applications to get a full picture of your typical CPU, memory and storage usage behavior. This complete knowledge of your average workloads will help you craft a plan for the best allocation of your resources.
- Utilize Autoscaling: Horizontal pods autoscaling (HPA) is a Kubernetes feature that can dynamically adjust the amount of pod replicas based on your observed CPU or memory utilization metrics. It scales up to accommodate increased traffic, or scales down during times of lower-than-average activity. This helps ensure that your resources are perfectly matched to your real-world workload needs. HPA is a highly valuable resource allocation resource that keeps your cost and cluster resource use as minimal as possible, without sacrificing your needs.
- Managing Storage: Kubernetes features including dynamic provisioning, volume resizing, and storage classes will optimize your storage allocation. Storage quotas and limits can help prevent the excessive use of resources.
Implementing Monitoring and Analytics for Cost Control
Continuously monitoring metrics such as CPU, memory, and storage usaging with Kubernetes dashboards will allow you to more easily identify inefficiencies or other opportunities for improvement. Whether you need to rightsize pods, adjust resource request and limits, or optimize storage usage, these insights are only possible when you are actively monitoring your usage.
Exploring Tools and Solutions for Kubernetes Cost Management
Venafi offers expert Kubernetes Consulting to help you get the most out of your Kubernetes deployment. Venafi can provide you with a detailed analysis of your Kubernetes cluster and auxiliary services, plus give you a personalized action-plan to reduce your costs and improve efficiency. Along with your tailored report and follow-up session, you can expect a deep dive into your cluster architecture and environment, along with deeper insight on how to optimize your cluster configuration.