I turned yesterday’s presentation from SAP into a new AI benchmark for FinOps professionals. It took one night. What it found should worry anyone budgeting for AI right now. At the FinOps X keynote this week, SAP’s Frederik Pohl and Maida Nazifi showed how they run FinOps for AI at global scale: an AI cost control plane managed by cost per OUTCOME — “because GPUs and LLMs don’t behave quite like VMs.” It was the best moment of the keynote, and honestly, the most needed one. The FinOps Foundation recently declared that FinOps now covers ALL technology spend — yet before defining data center unit economics or naming authoritative sources for those metrics, it has pivoted again, to token economics. An arena J.R. Storment’s own keynote called a “Wild West.” Scope is expanding faster than definitions. SAP’s segment was the part you could actually build on. I was curious what an A.I. benchmark, driven by SAP’s cost-per-outcome idea would look like (rather than just quantifying problem solving, long running context, or reading comprehension)… so I ran a series of tests towards a working benchmark: 14 models: closed frontier and open weights, 420 graded document-extraction runs, deterministic grading, no LLM judges, run overnight unattended. One metric: Cost Per Successful Outcome = total dollars spent ÷ answers that actually passed. Failures stay in the bill, because that’s how your invoice works. SAP is right. They don’t behave like VMs. At all: Cost per success ranged $0.0002 to $0.59 on IDENTICAL work — 3.5 orders of magnitude. The token price sheet shows only ~70x. Rate cards understate the real economics by 35x. An open-weight model won outright: best pass rate (70%) and lowest cost per success, confidence intervals clear of every frontier model. No model at any price beat 70% on this task set. Every dollar above the cheapest model at the ceiling bought nothing. The priciest model scored 7 points BELOW the winner. Price and quality were uncorrelated across all 14. Practical payoff: routing this workload to the value leader instead of a frontier model cuts cost per successful document ~99.9% with zero quality loss — a governable decision, IF someone in the room can read cost-per-outcome data. That someone is FinOps. You can’t make a defensible AI value statement to the business from a price sheet and a leaderboard — the real economics live in the gap between them, and reading that gap is the new core skill. One keynote slide became a working benchmark in a night; the measurement discipline is buildable NOW, by practitioners, without waiting for a standards body to finish the vocabulary. Full analysis, ranking table, confidence intervals, and the honest caveats: https://www.realtimecost.com/benchmark submitted by /u/Artistic_Lock_6483
Originally posted by u/Artistic_Lock_6483 on r/ArtificialInteligence