Attributed Intelligence.
Every dollar of AI spend, matched to the team, workflow and customer that caused it. Then the optimizations that buy you the same output for a lot less.
5 minute setup. Read-only. No engineering time needed.
Illustrative product view. Figures are examples.
What is Camaze?
An AI cost management platform built for finance teams. It connects read-only to every AI provider, tool and self-hosted model you pay for, reports the total, attributes it to teams and customers, forecasts it, alerts on movements the day they happen, and lists the optimizations that lower the bill without lowering how much AI you use.
One invoice is not an answer.
What the bill tells you
- A total. Bigger than last month.
- Spend by API key. Which maps to nothing.
- Last month's number. Three weeks late.
- No cause. Launch, model change or runaway agent, unknown.
- No return figure. So the line cannot be defended.
What Camaze tells you
- Every source. Including the ones nobody counts.
- By workflow, team, product and customer.
- Yesterday. Spikes flagged the next morning.
- Named cause and owner on every movement.
- Cost per run, per ticket, per customer.
One provider. Still no idea where it went.
Sprawl makes it louder. It does not create it. 38% of that invoice is one workflow. Nothing on the bill says so.
- Which workflows drove the number
- Production or evals, separated
- Why it jumped in week three
- What it returned, per unit of work
| Workflow | Environment | Cost | Share | Change |
|---|---|---|---|---|
| Ticket triage | Production | $34,800 | 38% | +22% |
| Document summarization | Production | $19,100 | 21% | +4% |
| Internal agents | Production | $14,600 | 16% | +64% |
| Sales research | Production | $8,900 | 10% | -3% |
| Evals and test suites | Non-production | $7,200 | 8% | +112% |
| Prototypes | Non-production | $4,300 | 5% | +9% |
| Not yet attributed | Unknown | $1,900 | 2% | -41% |
Illustrative product view. Figures are examples.
It started with one API key.
A year later there are eleven places money leaves the building, each billing differently, in a different place, on a different cycle.
- Credits, invoices, seats and tokens, same month
- Self-hosted models buried in the cloud bill
- Model switching that breaks every spreadsheet
- Card purchases that never reach procurement
Illustrative product view. Figures are examples.
The same output, for about half the price.
Nobody overpaid on purpose. The cheaper route either did not exist when the choice was made, or nobody had the data to see it. An open-weight model that scores the same as a frontier one is not a mistake corrected. It is an optimization taken.
Illustrative product view. Figures are examples.
Agents are where the new savings are.
One user action becomes a chain of calls, tools and retries. A retry that succeeds on the fourth attempt is a success in every log except the bill.
- Cost per run, tracked against a target
- Retry share of run cost, isolated
- Runs that never terminate, surfaced
- Frontier models on routing steps, flagged
- What we found
- 68% of your support classifier calls are simple yes or no answers running on a frontier model. An open-weight model scores the same on your own eval set.
- What it costs you
- $19,400 a month, about 13% of total AI spend.
- What to do
- Route classification to the open-weight model. Keep the frontier model for escalations only.
Illustrative product view. Figures are examples.
The model market moves faster than your budget cycle.
A cheaper model ships. Pricing drops.
More detail
Caching becomes available. Your volume crosses a discount tier. Every one of those is money, and none of it reaches finance in time to plan.
- New models scored against your own workloads
- Price drops applied to your forecast automatically
- Caching and batch pricing, matched to eligible workflows
- Discount tiers as your volume reaches them
-
A cheaper model now covers your largest workload
Ticket triage runs 41M tokens a month on a frontier model. A newly released mid-tier model matches it on your own eval set.
$21,300a month -
Input pricing dropped on a model you already run
No change needed. The saving lands automatically. The forecast has been updated and plan variance is now favorable.
$4,900a month -
Prompt caching is now available on the document workflow
78% of that workflow's input tokens are an unchanged system prompt. Caching them is a configuration change, not a rewrite.
$8,700a month -
Volume now qualifies for committed-use pricing
Twelve months of usage supports a commitment at the next tier. Draft terms and a break-even are attached.
$14,200a month
Illustrative product view. Figures are examples.
Four capabilities, built around the bill.
Spend visibility
One number, then split by team, project, workflow, product and customer. Includes models on your own GPUs.
Spend visibilityCost allocation
Showback and chargeback that survive a review. Plus what AI adds to the cost of serving one account.
Cost allocationBudgets and alerts
Notice on day 5, routed to the owner. Automatic spike detection, no configuration required.
Budgets and alertsForecasting
Month, quarter and year, with ranges. Model a rollout or a model switch before you commit to it.
ForecastingRead-only, out of band, connected in 5 minutes.
Find out what you could save.
Monthly spend and provider count. An estimated savings range in about a minute.
Every other AI cost tool is an engineering tool with a finance tab.
This one is the reverse.
| Engineering AI tools | Cloud cost tools | Camaze | |
|---|---|---|---|
| Built for | Developers debugging prompts | Infrastructure teams | Finance and budget owners |
| Starts from | Traces and logs | Cloud invoices | The whole AI bill |
| AI tools and seats | No | No | Yes |
| Self-hosted and open weight | Rarely | Raw GPU time | Attributed to models |
| Answers “who spent this” | By API key | By cloud tag | Team, workflow, customer |
| Forecasting | None | Cloud only | Month, quarter, year |
| Tracks the model market | No | No | Continuously |
| Output lands in | A dashboard | A dashboard | Budget, board pack, ERP |
28 connections, including the ones you host yourself.
Self-hosted spend arrives as ordinary GPU time inside a cloud invoice. Camaze pulls it out.
For whoever has to answer for the number.
Three steps. About 5 minutes.
Connect, read-only
5 minutes, no engineering time. Read-only billing credentials per provider. Nothing that can spend money or read your prompts.
See the whole picture
Twelve months backfills automatically. One total, then split by team, project, workflow, model and customer.
Act
Work the savings ledger, largest first. Set budgets and alerts. Then reinvest the difference in more AI.
Questions people ask
What does Camaze do?
It reports every dollar your company spends on AI in one place, splits it by team, project, workflow and customer, flags movements the day they happen, forecasts where the year lands, and keeps a standing list of what can be cut.
Built for the people who have to explain the number, not the people who wrote the code.
How is this different from the AI cost tools engineers use?
Those are engineering tools with a finance tab. They organize around traces, latency and prompts.
Camaze starts from the bill and reports in budgets, owners, variance and forecasts.
We only use one provider. Is this still useful?
Yes, and it is a common place to start. One invoice gives you a total. It does not tell you which workflows drove it, why it jumped, what it returned, or what next quarter costs.
Does it cover models we run ourselves?
Yes. Self-hosted spend arrives as ordinary GPU time inside a cloud invoice. Camaze separates it and prices it per million tokens served, so it is directly comparable to an API.
How much access does it need?
Read-only billing and usage credentials. Nothing that can spend money, change a deployment, or read your prompts. Most companies are connected in about 5 minutes. See security.
How much do companies typically save?
Most companies find they can get the same AI output for roughly half the cost. You see your own figure in the first week, itemized, with a dollar value against each optimization.
None of it means using AI less. It means buying the same output at the price it is actually worth.
Will this be used to cut our AI budget?
The opposite. Budgets get frozen when nobody can explain the number. The point of the savings is to fund more AI, not less.
How long does setup take?
About 5 minutes, and no engineering time. You paste read-only billing credentials for each provider. Twelve months of history backfills automatically.