What would the same AI output cost, optimized?
Two questions, about a minute. You get an estimated range of the cost savings typically available at your spend level, and where they usually come from.
Estimated cost savings, each month
$32,000 to $56,000
That is $384,000 to $672,000 a year, roughly 32% to 56% of your AI spend.
An estimate based on the findings that typically appear in an AI bill, not a measurement of your business and not a guarantee.
How far does a typical AI bill come down?
Most companies find they can get the same AI output for roughly half the cost. It comes from a consistent set of optimizations: frontier models running work an open-weight or small model handles identically, prompts carrying context the task does not use, prompt caching and batch pricing left switched off, agent loops retrying without a ceiling, workloads still running behind features that were removed, seats nobody opens, and committed-use tiers the company already qualifies for. None of it requires reducing AI usage.
The seven places the money usually is.
None of these is a mistake anyone made. Each is a cheaper way to buy output you are already producing, and each carries a dollar figure once there is connected data behind it.
A smaller model does the same job
Classification, routing and extraction running on a frontier model, where an open-weight or mid-tier model scores the same on your own evals. Usually the largest single optimization.
Context the task does not need
Retrieval depth and system prompts that grew during development and were never trimmed. Paid for on every call, changing no output.
Caching and batching unused
Static prompt prefixes sent uncached, and latency-insensitive work running synchronously at full price. Both are configuration.
Agent loops and retries
Retries that eventually succeed and therefore never look like failures, and loops with no terminal state. The fastest growing category.
Workloads nobody revisited
Systems running on deprecated models at legacy pricing, or serving features that were removed from the product.
Seats and duplicate tools
Per-seat AI tools assigned generously and reclaimed never, plus two tools doing substantially the same job.
Discounts already earned
Committed-use pricing and volume tiers your trailing usage already supports, never claimed because nobody was tracking the threshold.
About this estimate
Where does this estimate come from?
From the pattern of optimizations we look for, not from a measurement of your business. The range reflects the cost savings typically available across the categories on this page: model selection, context and prompt size, caching and batching, agent retries, dormant workloads, unused seats and unclaimed discounts.
It widens with provider count, because more billing relationships mean more unclaimed discount tiers, more duplicate tooling and more spend that nobody is watching. It is an estimate, and it is not a promise.
How accurate is it?
Treat it as a way to decide whether this is worth thirty minutes, not as a number to put in a plan. A real figure requires connected data, and you get one in the first week of a pilot: itemized, with a dollar value and an owner against each optimization.
Does this mean using AI less?
No. Every category behind the estimate is about paying the correct price for output you are already producing. Volume is unchanged. See cost optimization for what each finding actually looks like.
We only use one provider. Does the estimate still apply?
Yes, at the lower end of the range. Most of the savings come from how workloads are built and operated rather than from vendor count, and all of that applies to a single provider. See single-provider visibility.