AI Cost Engineering Advisory

The problem

Your AI features shipped fast — and now token spend is a line item nobody forecasted. Costs scale with usage in ways the old cloud bill never did: a single verbose prompt, an oversized model used for a trivial task, a retrieval pipeline re-embedding the same documents, or a missing cache can multiply spend with no change in user value. Most teams can't yet answer the basic question: what does one user interaction actually cost us, and why?

What we do

We measure where tokens and inference dollars go, identify structural waste — model selection, prompt and context bloat, missing caching, redundant retrieval, inefficient batching — and design a cost model you can forecast against. The emphasis is on changes that cut cost without degrading output quality.

What you get

• A usage and cost breakdown by feature, model, and call pattern.
• Structural findings: model right-sizing, prompt/context efficiency, caching and deduplication, retrieval and embedding cost, batching, and fallback design.
• A forecastable cost model — cost per request / per user / per feature, so finance and engineering share one number.
• A quality-guardrail note for each recommendation, so a cost change doesn't quietly reduce output quality.
• A prioritized 30/60/90-day action plan.

How it works

Same shape as the database review: scoping call → read-only access to usage data, logs, and configuration → diagnostics and senior review → report + walkthrough.

Who it's for

Engineering and FinOps teams whose LLM/inference spend has become material and unpredictable, and who want an engineering-grade cost model rather than a dashboard.

What this is not

Not model fine-tuning, not prompt-quality consulting in isolation, and not a guarantee of a specific cost reduction. We quantify the opportunity in your environment and show the tradeoffs.

Model-agnostic: when we discuss model selection, current frontier models (including the Claude 4.x family) are among those we benchmark on cost and capability. Recommendations are based on your workload, not vendor preference.