AI FinOps & Cost Optimization

Overview

Most mid-market AI buyers discovered in 2025 that token-spend scales sideways: a $5K/month workload becomes $50K/month inside two quarters. We instrument the stack, find where the dollars actually go, and cut cost by 30–60% — with every change eval-gated so quality never drops.

Honest about fit

A fit if…

You run at least one production AI system with monthly LLM spend over $10K
Your CFO or CEO has asked "what is this AI costing us, and is it worth it?"
You've learned quality-preserving cost optimization is an engineering discipline, not a model swap

Not a fit if…

Your monthly LLM spend is under $10K — the economics don't work yet
You want a one-time cost report with no implementation — buy the Audit and walk
You believe the answer is "just use the cheapest model for everything" — let the Audit show you the data

What you get

Concrete deliverables. No hand-waving.

Audit: full cost breakdown by model, workload, team, and use case
Token-spend telemetry — where the dollars actually go, not where you think they go
Cache-hit analysis and the top 10 highest-cost queries, broken down
Model-routing recommendation with per-route savings estimate
Retainer: monthly optimization implementations — caching, routing, prompt rewrites, batching
Savings tracked against fee; every change regression-tested before it ships
Optimize tier guarantee: our fee is less than the savings we generate, or we credit the difference