Cloud Cost Monitoring is Key To Effective Kubernetes Resource Management

If you are running Kubernetes clusters, then you need to have cloud cost monitoring set up to fully maximize your Kubernetes resources, optimize your platform, and drive down expenses.

To truly appreciate the complexity of managing Kubernetes resources and costs, you just have to take a look at the different variables that impact the cost. This list includes recovering abandoned resources, optimizing instance sizes, choosing the right platform (EKS vs GKE vs others), and more.

Visibility with Opsani

Before you can initiate any optimization effort, your first priority is to gain better visibility into the prevailing usage and costs of resources. Opsani has the technology to automatically help you achieve visibility into your Kubernetes resource usage and spending.

With Opsani, you can get a dashboard that allows for real-time tracking and reporting. This makes it easy for your organization to closely follow the costs.

Opsani Cloud Cost Monitoring Dashboard Overview

Opsani’s cloud cost monitoring dashboard gives you a clear and comprehensive picture of your application performance and spending on Kubernetes resources. Opsani’s custom cloud cost monitoring dashboards are designed and built to seamlessly compatible with both Google Kubernetes Engine (GKE) and Amazon Elastic Container Service for Kubernetes (EKS) clusters. 

Cluster-level metrics enable users to pinpoint high-level cost trends and follow spend across production versus development clusters. Metrics at the node level help you see and compare hardware costs, which is quite useful if you are running node pools using various instance types. Lastly, namespace metrics aid with the comparison and allocation of costs across disparate departments and/or applications.


The screenshot above shows metrics that DevOps and SRE (Site Reliability Engineering) teams deal with on a regular basis. The dashboard provides you with not just the visibility, but also the staging ground where you can find opportunities for cost optimization. Every piece of information delivers insights that you and your DevOps/infrastructure teams can use to delve deep into workloads, traffic patterns, resource constraints, and other factors that impact your cluster costs. Optimization options in this situation range from vertical pod autoscaling to moving a section of compute to preemptible spot instances.

All metrics and graphs in your dashboard are crucial to managing cluster resources and optimizing cloud costs. With our guidance and technology, you have everything you need to cost dashboards configured and running.


Prior to any cloud cost monitoring and cost optimization endeavor, there are three things you need to take care of first. One, you will require a Kubernetes cluster. Two, you need to configure the kubectl command-line tool so it can communicate with your cluster. And three, you need to install Git.

Know Kubernetes First and Foremost

Opsani can help you automatically unleash the full potential of your Kubernetes platform while keeping your costs to a manageable level. But before you perform any cloud cost monitoring and optimization, it is essential that you have a deep and solid understanding of what Kubernetes is all about.

There are many optimization actions that you can perform to reduce Kubernetes costs. But if you don’t have sufficient Kubernetes knowledge, discovering the best way to optimize your Kubernetes clusters manually to bring down spend can be an extensive exercise.  

For example, knowing when and if to tune your infrastructure with the cluster autoscaler or focus on tuning the application with the Horizontal Pod Autoscaler (HPA) or the Vertical Pod Autoscaler (VPA) already gives a complexity of options to choose from.  And, as there are others, the number of possible parameters to consider quickly grows to a point where improving efficiency and decreasing cost is quite possible, but knowing that your configuration is truly optimal is hard. The figure below shows various combinations of CPU and memory settings for a two-tier application tested over a five hour period as Opsani’s AI continues to hone in on an optimal configuration. 

Opsani leverages ML algorithms to provide continuous optimization for Kubernetes. What is challenging or impossible for a human, the Opsani AI handily finds and applies optimal configurations to the environment.  Further, Opsani continually refines its understanding of the optimum across time and through load variations.

Contact Opsani to know more about how they can help you optimize your infrastructure and cut your costs with the power of AI and ML. You can also sign up for a free trial and experience how Opsani can take your business to higher heights.