Switching to Karpenter with about 70% spot instance usage reduced monthly compute costs by 70% and cut pod scheduling latency from three minutes to 20 seconds. The Cluster Autoscaler was replaced by Karpenter and the cluster moved to a multi-architecture mix of AMD64 and ARM64 instances. Karpenter examines pending pods and provisions cost-effective instance types while performing right-sizing and advanced scheduling. Average CPU utilization rose from about 25% on fixed nodes to roughly 70% after right-sizing. Karpenter v1.0 added improved stability, disruption budgets, and node consolidation features. The cost savings covered migration engineering within the first month.
After switching to Karpenter with about 70% spot instance usage, our monthly compute costs dropped by 70%. That's a significant reduction that freed up substantial budget for new features and infrastructure improvements. Burninova's implementation involved replacing the traditional Kubernetes Cluster Autoscaler with Karpenter, and also moving to multi-architecture setup with both AMD64 and ARM64 instances.
Cloud optimisation platform nOps have also written about the benefits of using Karpenter for autoscaling. In a post on their site, they explain that Karpenter functions as "an open-source, flexible, and high-performance Kubernetes cluster autoscaler, offering advanced scheduling and scaling capabilities". Unlike traditional cluster autoscalers that operate with fixed node groups, Karpenter examines pending pods and provisions the most cost-effective instances to meet the specific resource requirements.
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