Underutilized VM Commitments Due to Architectural Drift

When organizations evolve their architecture - such as moving to GKE (Kubernetes), Cloud Run, or autoscaling - usage patterns often shift away from the original commitments. Resource-based CUDs cannot be cancelled, so when architectural shifts strand them, underutilized commitments lead to sustained, silent waste; Compute flexible (spend-based) CUDs offer more flexibility across regions, machine series, GKE, and Cloud Run, but only for eligible spend. This is especially common when workload changes go uncoordinated with finance or centralized planning.

CUDs are billed based on committed resource quantities (e.g., vCPUs, memory) for specific VM families and regions, regardless of actual usage. If consumption falls below the commitment level, the unused portion is still billed, generating cost with no value returned.

• Review actual usage against committed VM types and regions across a representative time period
• Identify consistent underutilization of CUDs based on usage-to-commit ratio
• Confirm whether architectural shifts (e.g., to GKE, Cloud Run, or autoscaling groups) have occurred
• Check for commitments applied to workloads that have been decommissioned or scaled down
• Evaluate whether commitments are isolated to specific projects, regions, or teams without visibility across the org

• Consolidate workloads onto committed VM types where feasible
• Avoid renewing commitments for workloads that are scaling down or migrating
• Use Compute flexible (spend-based) CUDs when architectural flexibility is needed, and confirm resource-based CUD sharing across projects in the billing account is enabled
• Implement cross-functional governance for forecasting and commitment approvals
• Align future commitments with current and expected usage patterns, not historical ones

• Committed use discounts (CUDs) for Compute Enginedocs.cloud.google.com
• Resource-based committed use discountsdocs.cloud.google.com
• Spend-based committed use discountsdocs.cloud.google.com