Maximizing reliability, minimizing costs: Right-sizing Kubernetes workloads

Do you know how much money you could save by adjusting workload requests to better represent their actual usage? If you’re not rightsizing your workloads, you might be overpaying for resources that your workloads aren’t even using or worse, putting your workloads at risk for reliability issues due to under provisioning.

As we’ve previously discussed, setting the resources is the most important thing you can do to increase the reliability of your Kubernetes workloads. In this blog we will help you with the second key finding from the State of Kubernetes Cost Optimization report!

According to our research findings, workload rightsizing is the most important golden signal. Workload rightsizing measures the capacity of developers to properly use the CPU and memory they have requested for their applications. 

Rightsizing is challenging

It can be quite difficult to predict the resource needs of your applications, which historically has not been a concern for developers in traditional data center environments.In traditional data center environments, resources were typically over-provisioned upfront to ensure capacity for peak demand and future growth, so developers didn’t need to focus on accurately predicting resource needs as they were covered by the excess capacity, whereas in cloud environments, resources are consumed on-demand. Finding a balance between efficiency and reliability can often feel like a delicate balancing act.

Tools for workload rightsizing

There are native tools in Cloud Monitoring and the GKE UI you can use to rightsize your workloads running on GKE.