What a decision review actually produces.

Memorandum · Prepared for the CEO and the board

The Question

Should the company sign a three-year, $4.2M committed contract for dedicated GPU infrastructure to move its AI workloads off per-request API pricing, as recommended by the infrastructure vendor and supported by the VP of Engineering?

Evidence Reviewed

• The vendor's proposal, pricing model, and contract terms, including termination and capacity-transfer clauses.
• Twelve months of actual API usage and spend data, per product feature.
• The internal cost model supporting the recommendation, and the assumptions embedded in it.
• Current architecture and deployment documentation for the three workloads proposed to move.
• Interviews with the VP of Engineering, the two engineers who operate inference today, and the CFO.
• Reference pricing for equivalent committed capacity from two alternative providers.

Material Assumptions

The recommendation depends on three assumptions doing most of the work:

• A1. Inference volume grows at or above 8% per month for three years.
• A2. The team can operate dedicated infrastructure at the utilization the cost model assumes (70%+).
• A3. Model and hardware requirements remain stable enough that committed capacity stays the right capacity.

Findings

• F1. The usage data supports A1 for one product feature, which produces 81% of current volume. The other two features are flat. The cost model applies the growth rate to all three.
• F2. The cost model assumes 70% utilization from month one. The team has never operated dedicated GPU infrastructure. Comparable teams reach that utilization in the second year, if at all. At 40% utilization the committed contract is more expensive than current API spend until month 29.
• F3. The break-even claim in the proposal excludes the two operations hires the vendor's own reference architecture requires.
• F4. The termination clause prices early exit at 65% of remaining committed value, which makes A3 the assumption carrying the most financial risk.
• F5. A one-year commitment covering only the growing workload, with the flat workloads remaining on per-request pricing, was not evaluated. Reference pricing indicates it captures most of the projected savings at roughly one quarter of the committed exposure.

Unknowns

• Whether the growing feature's volume is durable or promotional: the growth window coincides with a pricing experiment that ends next quarter.
• The vendor's actual delivery timeline for the committed capacity; two references report delays of 6 to 10 weeks.

Economic and Operational Implications

If A1 and A2 hold, the three-year commitment saves approximately $1.1M against projected API spend. If either fails, the company is carrying up to $2.7M of committed cost above the alternative, plus two hires and an operational capability it does not currently have. The downside is roughly 2.5 times the upside, and the two assumptions that create the downside are the two with the weakest evidence.

Conclusion