Start an AI Cost-Per-Token Audit Service

1. 1

   Build a defensible measurement method

   Define exactly how you compute cost-per-token and intelligence-per-dollar across a client's workloads: which costs count (compute, serving, retries, idle), how you normalize across models and tasks, and how you attribute spend to useful output. A method you can defend in front of a skeptical CFO or head of engineering is the foundation of the whole service.

2. 2

   Prove it by cutting one real bill

   Apply your method to a real AI workload (yours or a friendly team's) and produce a benchmarked before-and-after with the reallocation that lowered the cost per unit of output. Your first case study, with numbers, is your entire sales pitch in this results-driven market.

3. 3

   Position against generalist IT audits

   This is not a cloud-bill review or an IT cost audit. Position on the specific, current metric enterprises now care about because NVIDIA made it the buying criterion, and make clear you benchmark AI output economics, not just server invoices. That specificity is why they hire you over their existing consultants.

4. 4

   Package as a fixed-scope audit with a plan

   Sell a defined engagement: instrument and measure the current spend, benchmark it against cost-per-token targets, and deliver a prioritized reallocation plan (which workloads to move, which hardware or model to switch, what to cut). One clean, priced deliverable gives the buyer an easy first yes.

5. 5

   Anchor pricing to the savings

   An enterprise with a large AI infrastructure bill will pay well for a credible plan to reduce it. Price against the defensible savings rather than your hours, and where you are confident, offer a follow-on implementation or a shared-savings component tied to the measured reduction.

6. 6

   Turn audits into ongoing benchmarking

   Spend drifts as models, traffic, and prices change, so a one-time audit becomes stale. Offer a recurring benchmarking retainer that re-measures cost-per-token each quarter and keeps the reallocation current, converting the audit into steady revenue.

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Best for: An analyst or engineer who can turn AI spend into a benchmarked, defensible savings plan

Not for: Not for anyone without the literacy to both measure AI spend and recommend real reallocations; a benchmark you cannot defend or act on is worthless here. Not for generalist IT consultants hoping to relabel a cloud-bill review, and not for people who want a one-off transaction instead of building the recurring benchmarking relationship.