GCP's commitment framework is more rigid than AWS and Azure — no marketplace, no exchange, no refund. This paper delivers the negotiation strategy, discount mapping, and purchasing methodology to maximise CUD savings while protecting against stranded spend.
Resource-based CUDs, spend-based CUDs, and Sustained Use Discounts — mechanics, discount depth, flexibility trade-offs, and how they interact with each other.
Complete discount tables across general-purpose, memory-optimised, compute-optimised, GPU (A100, H100), and TPU resources — 1-year and 3-year terms with stranding risk assessment.
Head-to-head comparison of commitment frameworks — why GCP's rigidity demands more conservative sizing and how to use cross-provider benchmarks as negotiation leverage.
Enterprise-negotiated terms that transform GCP's rigid CUD framework — commitment flexibility, cross-region application, machine series migration, GPU term management, and scope expansion.
From utilisation baselining and SUD interaction modelling through portfolio construction, enterprise term negotiation, competitive benchmarking, and ongoing governance.
Why Google's removal of Sustained Use Discounts on newer machine series changes the CUD calculus — and how to plan your commitment strategy around this structural shift.
"On AWS, an over-commitment is a recoverable error. On GCP, it's a sunk cost. Size your CUD portfolio for the workload you're certain about — not the workload you hope for."Redress Compliance — Cloud & FinOps Practice