Optimizing Resource Utilization for Committed Cloud Spend: A Real-Life GCP Case Study

ShareChat, one of India's largest Google Cloud users, faced challenges with efficient resource utilization due to its large cloud compute consumption. The company experimented with native GKE autoscaler but found it left significant headroom for underutilized resources, and purchasing Committed Use Discounts (CUD) still resulted in a gap in resource utilization, with some clusters reaching up to 50% of unused capacity. To address this issue, ShareChat implemented Cast AI, a Kubernetes automation solution that helped reduce overprovisioning through automation mechanisms like autoscaling, bin-packing, and workload rightsizing, eliminating the need for manual node pool configuration and resulting in significant time and cost savings. The company then worked with Cast AI to develop a specialized rebalancer for optimizing resource utilization across CUDs, which enabled easier configuration, prioritized utilization, and increased commitment utilization to around 98%, reducing the need for manual capacity planning and resulting in substantial cost savings.

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