Make your AI infrastructure legible — and its costs attributable.
Productized engagements for teams whose AI and cloud bills are growing faster than their usage. Clear deliverables, a number you can defend to finance.
AI Cost & Unit Economics Audit
Flagship · 1–2 WeeksThe full teardown: where your AI and cloud spend actually goes, what each inference/request/customer really costs, and the top levers to fix it. Cost data turned into an engineering artifact.
Deliverables:
- • Cost-attribution map (who owns what)
- • Unit-economics model: cost per inference / request / customer
- • Waste & rightsizing findings, ranked by $ impact
- • Showback / chargeback model finance can run
- • Executive-ready teardown deck
Perfect For:
- • Teams whose AI bill grows faster than usage
- • LLM/inference spend nobody can attribute
- • Pre-fundraise or pre-scale margin clarity
- • FinOps programs that need real per-unit numbers
Unit Economics Sprint
1 DayA focused, one-day teardown of a single AI system or workload. Fast answer to "what does this actually cost us, and where's the waste?"
What You Get:
- • Fully-loaded per-unit cost for one system
- • Spend breakdown (compute, inference, egress, storage)
- • Top 3 cost levers, quantified
- • Instrumentation gaps to close
- • One-page findings summary
Perfect For:
- • Validating economics before scaling
- • A single runaway workload or feature
- • Founders pricing an AI product
- • A quick, defensible number for the board
ML Cost & Reliability Evals
2 WeeksA production eval harness that treats cost as a first-class metric alongside quality and latency — so you catch cost regressions the way you catch accuracy regressions.
Implementation:
- • Cost-per-inference baseline & budgets
- • Evaluation harness with cost gates
- • Data quality contracts
- • Drift & spend monitoring
- • Documentation & team handoff
Metrics Tracked:
- • Cost per inference / per request
- • Quality & accuracy
- • Latency & throughput
- • Model & cost drift
- • Safety & bias checks
Executive Cost Briefing
90 MinA board-ready story on your AI infrastructure economics: what it costs, who owns it, and where the margin risk is — with the Q&A prep to defend it.
Session Includes:
- • Cost & attribution landscape review
- • Unit-economics narrative for the board
- • Margin & spend-risk assessment
- • Investment / rightsizing recommendations
- • Board Q&A preparation
Ideal For:
- • CEO/CTO board prep
- • Investor / diligence conversations
- • FinOps & finance alignment
- • Strategy validation
- • Team alignment on cost ownership
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Not sure where your spend is going?
Book a free 30-minute review. Bring your AI/cloud cost puzzle and we'll find the first thread to pull — no obligation.
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