Optimizing Compute Costs in Kubernetes with Karpenter’s Workload Consolidation

Kubernetes has become the leading platform for container orchestration, but the inherent dynamism of the cloud can make cost optimization a challenge. While scalability is a huge advantage, it can also be difficult to fine-tune resource allocation and avoid unnecessary expenses.

To address this challenge, Karpenter emerges as an efficient solution for managing compute resources in Kubernetes. Its “what you need, when you need it” approach simplifies compute capacity provisioning based on the specific requirements of each pod.

The Magic of Workload Consolidation

This feature allows Karpenter to continuously monitor and optimize pod placement, improving instance resource utilization and ultimately reducing compute costs.

In previous versions, Karpenter was limited to deleting worker nodes that had no pods other than daemonsets. This sometimes left nodes underutilized over time as workloads were rescheduled.

Workload consolidation refines Karpenter’s efficient and cost-effective autoscaling model by consolidating workloads onto the smallest possible number of instances at the lowest cost — all while respecting pod resource requirements and scheduling constraints.

A Practical Example

Imagine a scenario where an application with multiple replicas is deployed, each pod requesting one CPU core and having a topology spread constraint across availability zones. Initially, Karpenter will provision a set of nodes to meet the compute requirements and scheduling constraints specified in the application manifest.

If the number of replicas is later reduced, lowering the requested resource utilization, Karpenter steps in. It will proceed to remove the underutilized node — but not before cordoning it and draining the pods it hosts. These pods are then rescheduled across the remaining instances, ensuring that all resource requests and scheduling constraints continue to be met.

As a result, Karpenter optimizes cluster compute costs by eliminating one of the instances, effectively reducing the data plane cost for that workload.

Key Recommendations

To get the most out of Karpenter’s workload consolidation feature, it’s essential to follow some best practices:

• Precisely specify CPU and memory requests for your pods.

• Use a cost-monitoring tool like Kubecost alongside Karpenter, especially in production environments.

By combining Karpenter’s workload consolidation with well-defined resource requests, you can achieve significant savings on the Kubernetes data plane.