Case Study: Bud Financial achieves 90%+ resource utilization and lower cloud costs with CAST AI

Bud achieved 90%+ resource utilization, reduced costs, and increased engineer productivity

Bud Financial, a B2B financial data intelligence platform that enriches and analyzes billions of transactions, needed better visibility into cloud spending and a way to reduce manual, cluster-by-cluster cost management. As its Kubernetes estate grew to around 25 clusters, the team wanted to prevent overprovisioning, improve resource utilization, and automate the checks that were taking significant engineering time.

Bud turned to CAST AI to automate Kubernetes cost optimization with features including Workload Autoscaler, cluster autoscaling, rebalancing/bin packing, and node hibernation. With CAST AI, Bud achieved up to 93% CPU and memory utilization, 100% Committed Use Discount utilization, and 47% cost savings from hibernating clusters on nights and weekends. The company also said the platform helped reclaim engineer time, with workloads automatically right-sized so teams could focus on shipping code instead of tuning infrastructure.

Bud Financial

Dan Udell

Director of Foundations Engineering