Your AI bill, taken apart line by line.

The AI Cost Audit is a scoped review of your AI usage, model choices, and architecture. You get a written report that names the waste, the risk, and the fixes. Each finding comes with a dollar figure. Fixed prices for bounded scopes; larger estates are quoted before any data moves.

Book a free 30-minute call →

In 30 minutes we'll look at how you're using AI and identify where the biggest waste likely sits. No pressure — you leave with something useful either way.

Why It Pays For Itself

Most AI bills carry the same problems.

Expensive models doing work a cheaper model handles fine.
No caching, so you pay full price for repeated work.
Real-time API calls for jobs that could run as batch at half the price.
Commitments and reservations that don't match how you actually use them.

If your team spends $10,000 a month or more on AI, one good finding can usually cover a starter audit. Larger estates need a larger scope, because the work grows with the number of workloads, providers, accounts, and stakeholders involved.

What You Get

A report your team can act on the same day.

A spend map. Where the money goes, by model, feature, and workload.
Findings with dollar figures. Every problem priced: what it costs you today, what it costs after the fix.
A ranked fix list. Ordered by savings against effort, so your team knows what to do first.
Risk flags. Commitments, rate limits, lock-in, use-it-or-lose-it capacity.
A 60-minute readout call with your team to walk through all of it.
The report ships as a clean markdown doc. Your engineers can hand it straight to their own AI tools and start fixing.

What It Costs

Starts at $2,500

Scoped before inputs

The $2,500 starter audit covers one AI product or workload, one team, up to two providers or accounts, a written report, and one readout call.

Findings Sprint

$1,000 · fixed · 1 week

Send billing exports and a 30-minute walkthrough. You get a memo with your top 3 findings, each with a dollar figure. Upgrade to a full audit within 30 days and the $1,000 is credited.

Starter Audit

$2,500 · fixed scope

One bounded workload or product area. Good when the spend is real, but the surface area is still narrow.

Multi-Workload Audit

Scoped after intro

Multiple workloads, teams, providers, accounts, or commitment decisions. We set the boundary before any data moves.

Enterprise Audit

Custom scope

Large estates, business units, procurement questions, exec readouts, or phased delivery. The work is priced to the estate, not squeezed into the starter box.

I price the audit by surface area, not as a percentage of your bill: workloads, providers, accounts, stakeholders, commitment math, and how many readouts or report versions your team needs. You know the price before you send data.

Every tier includes: the written report, one readout call, and one round of follow-up questions or report revisions within two weeks of delivery. Not included: implementation, ongoing monitoring, vendor negotiation on your behalf, or new workloads added mid-audit — those are scoped separately, so the price you agreed to never quietly grows.

Who This Is For

The person who owns the AI bill.

Founders, CTOs, platform leads, FinOps leads. Teams spending roughly $10,000 a month or more on AI APIs and infrastructure, or larger organizations trying to understand which AI workloads are worth more spend.

Not for you if: you're still exploring AI and the spend is a couple of subscriptions, or you want someone to rebuild your stack for you. I advise. Your team owns everything.

Who I Am

Cloud cost discipline, applied to AI.

Saurav Sharma. I’m a cloud and AI cost optimization consultant. I help companies understand where their AI spend is going, what business value it is creating, and which parts are worth optimizing.

My background sits across both sides of this problem. I spent 6 years at Amazon, first working with large enterprise customers as a Senior Technical Account Manager on cloud architecture, cost optimization, reliability, and scaling, then building LLM and GenAI platforms as an engineer.

I also spend a lot of time teaching this space. I hold 12 AWS certifications, teach 33,000+ students on Udemy, and run the CloudYeti YouTube channel.

What Happens After

The audit comes first. Staying covered comes after.

Model prices, commitment options, and the cheapest way to run a workload change every few months. After the audit, most teams keep me on as their AI advisor so someone is watching this for them.

See the Advisory Retainer →

FAQ

Common questions.

Do I need to give you access to our systems?

No. Read-only billing and usage exports, a model list, and an architecture sketch. That's it.

Will you sign an NDA?

Yes.

What if you don't find anything?

Then you get written confirmation that your setup is sound, plus the specific things to watch as pricing changes. Teams that pass clean are rare, but that answer has value too.

Do you implement the fixes?

Rarely, and only when a fix needs a real build. The report is written so your team can act on it. If you want ongoing help, that's the advisory retainer, and we talk about it on the readout call.

How do you decide the audit price?

By scope. A single bounded workload can fit the starter audit. Multiple workloads, providers, accounts, business units, executive readouts, or procurement decisions become a larger scoped audit. I quote that on the intro call before you send data.

We spend less than $10K/month. Can we still work together?

Probably the better fit is a paid 1-on-1 hour where we go through your setup live, or the $1,000 Findings Sprint if you want the top problems in writing.