$2.4M in CUD commitments, 31% net savings

Context: A Fortune 500 data analytics company was operating a massive, GCP-primary infrastructure utilizing BigQuery, Dataflow, and GKE. Their annual on-demand compute baseline had reached $7.8M. Despite the volume of spend, the organization had avoided long-term commitments due to internal friction between engineering, which demanded total flexibility, and procurement, which needed predictability.

Constraint: The procurement team required a rigorous, defensible Total Cost of Ownership (TCO) model to present to the board. The model had to guarantee financial returns without risking underutilization penalties if engineering decided to refactor major components or shift workloads between regions over the next two years.

What we changed:

• Performed a deep workload baseline analysis, isolating the highly variable transient workloads from the underlying stable compute floor that ran 24/7.
• Developed a comprehensive Committed Use Discount (CUD) portfolio strategy, blending one-year and three-year commitments to balance discount depth with architectural agility.
• Executed a $2.4M CUD purchase structured precisely at a 60/40 split between one-year and three-year terms, providing maximum coverage on the stable floor while leaving enough on-demand margin for architectural shifts.
• Established a quarterly FinOps review cadence to evaluate commitment coverage and plan incremental purchases as baseline usage evolved.

Measured result: The executed CUD portfolio delivered an immediate 31% net savings rate on covered compute. The blended commitment structure yielded a highly favorable break-even point of just 8 months. The board approved the TCO model, and engineering maintained the flexibility they required without financial penalty.