AI inference cost audit

Find the money
hiding in your AI bill

Your model spend is probably the fastest-growing line in your P&L, and almost nobody owns it. We audit the whole stack — prompt structure, caching, model fit, retries, output length — then reconcile every saving against your own provider invoices.

Free, no signup. An anonymised usage export is enough to start.

Modelled recoverable, per month
$10,964 – $15,581
$13,507 expected · 52% of the current bill
Current$25.9k
New bill$12.4k
Cache hit7.6%
Estimate — not a verified saving
69%of input tokens are system prompts Datadog, 1,000+ production environments
28%of LLM calls use caching at all So most repeated context bills in full
90 daysbefore a saving becomes a fee Measured on your invoices, not ours
How it works

Diagnose, fix, then prove it on the bill

Most tools stop at telling you what is theoretically possible. The hard part is the middle — changing the system without breaking it — and the harder part is proving what happened afterwards.

01

Audit

We read the prompt structure and the usage export. Where does the money actually go?

02

Optimize

Largest-dollar levers first, each quality-checked before the next one starts.

03

Reduce

Cheaper rate, fewer tokens, or both. There is no third lever.

04

Grow

Spend the saving on the roadmap instead of the invoice. Margin you keep.

Run the free scan

Drop in a usage export from OpenAI, Anthropic, OpenRouter or Bedrock and get your monthly run rate, your cache hit rate, and the range of what is recoverable. About a minute. Nothing is stored.

Want the full report? Add a prompt-structure description — a short block list, not a codebase.

⇩
Drop your usage export CSV or JSON · up to 50 MB
Scan it
What we find

The levers that are not a config flag

Gateways now turn on caching and routing for a flat fee. What they cannot do is read your system and tell you why the cache never hits.

Cache-defeating prompts

A timestamp at token one means nothing behind it is ever reused. Invisible on the bill.

History resent in full

Cost grows quadratically with turns. Summarise or window instead of replaying.

Reasoning on trivial work

Thinking tokens bill at the output rate — the most expensive tokens you buy.

Frontier models on simple tasks

Shadow-tested downshifts, verified against real traffic before anything merges.

Uncapped replies

Output length is the lever nobody sets, and it hits cost per call directly.

Retry and fallback burden

Failed calls still bill. Retry storms hide inside request volume.

Estimates, not promises

No percentage guaranteed before anything is measured

Plenty of vendors will promise you 40–60% off on the first call. We will not, because we would not know yet. Here is what we commit to instead.

  • A report with dollar figures either way — including if the answer is "nothing here".
  • Every saving reconciled to your provider invoices, not our dashboard.
  • The baseline signed before any change is made, so a delta can be attributed.
  • Any lever that does not hold gets reported as not held.
Pricing

One engagement fee. No retainer.

A share of the savings we can verify, charged once. No recurring share of spend, no renewal, no seat count. If there is nothing material to recover, we say so and you keep the report.

Two audits in flight at any time, ever. That is a capacity limit, not a marketing line.

Fee
25% of verified annual savings

At go-live50%
At day 90, once measured50%
RetainerNone

Quoted on the conservative case,
never the optimistic one.