AI Spend Reduction Audit
You audit a company's Anthropic/OpenAI billing dashboards and application logs the way a utility-bill auditor audits an electric bill: pull 60-90 days of usage, map which endpoints hit flagship models for tasks a cheaper model could handle just as well, identify repeated system prompts eligible for prompt caching (a roughly 90% discount on cached input tokens on both Anthropic and OpenAI as of 2026), and flag batch-eligible workloads like nightly summarization jobs for the Batch API's 50% discount. On a $20,000/month AI bill, model routing alone typically recovers 40-70% of spend, and layering in caching and batching can push combined savings toward the 70-85% range for workloads with repeated context. You implement the routing/caching/batching changes yourself, then bill a contingency fee - 20-30% of the verified monthly savings - for an agreed 6-12 month share period, exactly like a utility-bill auditor taking a cut of the refund they found. After the share period ends, the client keeps 100% of the savings and you move to the next account.
Real, fast-growing pain (AI spend expanding faster than engineering discipline) and a proven adjacent business model (contingency-fee utility auditing), but you're selling a percentage of savings a CFO has never budgeted for and can't easily verify without your baseline - expect a slow, education-heavy first sale.
Structure every engagement as audit-then-implement, not audit-only: a savings estimate you hand over in a sales call is trivial for an internal engineer to act on without paying you. Requiring you to implement (or co-implement) the routing/caching changes before the contingency clock starts protects your fee and mirrors standard practice among utility-bill auditors, who are typically paid only once savings are realized on an actual bill, not merely identified on paper.
Suits you if
- ✓You have hands-on production experience with LLM APIs (routing, caching, evals, batch jobs)
- ✓You're comfortable with a sales cycle built on trust and a documented baseline, not a fixed price list
- ✓You can read a usage dashboard and translate it into a specific, defensible savings number
- ✓You want a business where your upside scales with the size of the client's problem
Skip it if
- ✕You need predictable flat-fee income rather than variable contingency payouts
- ✕You don't have the engineering depth to evaluate whether a cheaper model silently degrades output quality
- ✕You're targeting companies spending under ~$10k/month on LLM APIs - the dollar savings won't justify the effort
- ✕You're not willing to put real verification/baseline methodology in writing before work starts
Skills: LLM API cost engineering (model routing, prompt caching, batch processing, prompt/context compression), basic evals/regression testing to catch quality degradation, usage-normalized cost analysis to prove savings weren't just a slow month, and consultative B2B sales to technical and finance stakeholders.
Unlock "AI Spend Reduction Audit"
Get the full step-by-step plan, tools list, and experience breakdown with lifetime access to the whole database.
Get full access