Your AI costs are growing.
The visibility is not.
I kept seeing the same pattern across scale up software companies. The CFO spots an inflating API line item. The engineering team claims they need it for product features. Neither side has the data to prove if those features actually generate more money than they consume.
AI cost attribution fails because the architecture obscures it. We optimize for speed of deployment, but the resulting system lacks economic boundaries. The $5,000 AI Cost Governance Review fixes this structural defect.
Who This Is For
Chief Financial Officers
Turn black box engineering expenses into predictable unit economics.
VPs of Finance
Establish accurate margin reporting across disparate AI models.
PE Operating Partners
Identify structural margin collapse before acquiring a portfolio company.
What You Get
The review installs clear visibility into your AI infrastructure. We do not provide opinions. We provide math and architectural governance.
Unit Economics Model
Exact calculation of cost per inference and cost per useful outcome.
Collapse Point Calculation
Mathematical threshold where AI maintenance costs exceed innovation budget.
Margin Protection Plan
Strategic architecture adjustments to cap expenses and protect gross margins.
Research & Methodology Foundations
AI Volatility Tax
AI Volatility Tax is the margin reduction incurred when variable LLM inference query costs scale faster than subscription ARR, transforming hosting into variable COGS.
Inference Economics
Inference economics is the practice of tracking and optimizing the financial costs of running AI models.
Semantic Caching
Semantic Caching stores similar LLM prompt responses in a vector database to serve future requests locally, eliminating redundant API costs.
The AI Margin Squeeze
The AI Margin Squeeze is the systemic erosion of SaaS gross margins caused by variable AI inference costs scaling faster than flat subscription revenues.