I help teams building with AI figure out which use cases deserve more money, which need better architecture, and which are just burning it. Each one gets a dollar figure, and your team gets a clear order of what to fix first.
30 minutes. I tell you where I'd look first, whether or not we work together.
AI spend is the fastest-growing line on your cloud bill, and it's the one nobody owns. Engineers pick models by habit. Nobody checks whether a workload should be batched, cached, or committed. The pricing fine print changes every quarter. So the bill grows, and the link between what you spend and what you ship gets weaker.
When it doesn't, that's waste, and it's almost always fixable. The teams that get this right don't simply spend less. They know which AI bets deserve more money, which need better architecture, and which to stop. That's the work.
30 minutes. You tell me what you're building and roughly what you spend. I tell you where I'd look first, whether or not we go further.
A fixed-scope, two-week review of your AI usage, model choices, and architecture. Every finding comes back with a dollar figure: what it costs you today, what it costs after the fix, and what to do first. If your team spends $10,000 a month or more on AI, finding one inefficiency usually covers the fee.
See what the audit covers →Model prices, commitment options, and the cheapest way to run a workload reset every few months. Most teams keep me on so their spend map stays current instead of aging out.
Some teams bring me into their sprints as an advisor. Some have me train the team on what the audit found, so the waste doesn't come back. Both come later, and only if the audit says you need them.
The audit comes first. Everything after it is a decision you make once you've seen your own numbers.
Saurav Sharma. Six years at Amazon. 12 AWS certifications. I was doing this work on cloud bills before AI bills existed: finding cost and architecture problems in enterprise AWS accounts as a Senior TAM. Now I do it for AI spend every day: model selection, commitment math, caching and batching, the pricing fine print across OpenAI, Anthropic, Azure, and AWS. I teach 30,000+ students on Udemy and run the CloudYeti YouTube channel.
Founders, CTOs, platform leads, engineering leads, FinOps leads. Teams spending real money on AI who want it to convert into output, not just invoices.
Book a free intro call →30 minutes. I tell you where I'd look first, whether or not we work together.