Karpenter now exposes enough pricing data to estimate Kubernetes node costs directly from Prometheus metrics. The existing Karpenter dashboards in the kubernetes-autoscaling-mixin already cover node pools, instance types, and scaling behavior. This post focuses on the new cost dashboard: estimated monthly cost, spot instance savings, and node pool cost breakdown in Grafana.

This builds on the earlier posts on Karpenter monitoring with Prometheus and Grafana, comprehensive Kubernetes autoscaling monitoring, and Kubernetes cost tracking with OpenCost. If you want actual cost allocation, shared resource accounting, and historical cost analysis, use OpenCost and the opencost-mixin. This Karpenter dashboard answers a narrower question: what does the current Karpenter-managed node fleet look like in dollars?

With the release of Karpenter v1 we have stable Prometheus metrics, but the Grafana dashboards are not that great and there are no open source alerts. Therefore, I decided to create a monitoring-mixin that provides a set of Prometheus rules and Grafana dashboards for Karpenter. This blog post will introduce the kubernetes-autoscaling-mixin - a set of Prometheus rules and Grafana dashboards for Kubernetes autoscaling, but we will only write about Karpenter monitoring in this blog post.

The kubernetes-mixin is a popular resource for providing excellent dashboards and alerts for monitoring Kubernetes clusters. However, there’s still not much in place yet for autoscaling tools like PDBs, HPAs/VPAs, KEDA, Karpenter, or the Cluster Autoscaler. These essential components are commonly deployed in Kubernetes environments but don't have standardized, open source monitoring solutions. This blog post aims to solve that by introducing the kubernetes-autoscaling-mixin - a set of Prometheus rules and Grafana dashboards for Kubernetes autoscaling. This mixin includes coverage for Kubernetes core components such as HPA and PDB, as well as additional components like the KEDA, VPA, Karpenter, and the Cluster Autoscaler, ensuring you have visibility into the autoscaling mechanisms that keep your cluster running efficiently.