Treasure Data Matches Every AI Invoice to Token-Level Usage Data with CloudEagle.ai

“AI spend was becoming harder to explain as our usage grew. We could see the invoices, but we didn’t have a clear way to connect those charges to actual usage. Now we can match every invoice to token-level consumption, understand who and what is driving the spend, and give leadership a number we can stand behind.”

~ Aysha khan, CIO and CISO, Treasure data

2.8M tokens

traced to a specific user, project, or API

$20K+

AI token costs verified against usage

34%

of inactive licenses reclaimed

2.8M tokens

traced to a specific user, project, or API

$20K+

AI token costs verified against usage

34%

of inactive licenses reclaimed

Challenge

AI vendor invoices arrived with no data behind them, just a total, at a scale too large to check by hand.
Finance had no way to know if a bill's total matched what was actually consumed that month.
IT and finance each kept their own version of "AI spend," and the two rarely matched.

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Solution

Token consumption gets tracked by user, API, and project, so every token has a source.
Usage breaks down further by prompt intent, by model, and by session, so it's clear not just who used tokens but what for and on what.
Every invoice gets checked against this tracked consumption before finance approves payment.

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Result

2.8M tokens a month now trace to the specific user, project, or model behind them.
$20K+ in monthly AI token costs get verified against real usage before finance pays a vendor.
34% of licenses turned out to be inactive for 90+ days, invisible until usage itself became visible.

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Challenge

As Treasure Data expanded its use of AI tools across engineering, support, and research, the monthly invoice from each AI vendor arrived as a single total.

It said what was owed. It said nothing about who had used the tokens, which projects they were tied to, which models had been called, or how many sessions that total actually represented, and the volume involved made checking it by hand out of the question.

Finance paid the number as given, because there was nothing to check it against. IT kept its own separate view of usage, built from whatever logs it could pull together, and that view rarely matched finance's.

When leadership asked what the company's AI spend was actually producing, the CIO had one figure and no way to break it down, by team, by project, by model, or by anything else that would explain where the money had gone.

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Solution

Token consumption is tracked by user and by API/service account, so every token used has a clear source, human or machine.
Usage breaks down by project, so spend maps to the initiative that drove it.
Each prompt gets classified by intent, content writing, code generation, research, and similar categories, without storing the prompt text itself.
Usage is visible model by model, so a spike in spend can be traced to a specific model choice, not just a vague total
Every AI vendor invoice gets matched against this tracked consumption before finance approves payment.

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Why CloudEagle.ai?

Treasure Data evaluated several options and chose CloudEagle.ai because it could govern AI without adding another tool to the stack.

A natural extension of the SaaS management platform already in place. CloudEagle.ai was already a leader there, so AI usage control mostly meant adding integrations, and tracking and invoice matching were live from day one.
Every AI invoice matched to real token usage, so finance could verify the bill before paying it instead of approving a total nobody could explain.
Nothing hidden. EagleIQ's seven telemetries find AI tools even when they never go through SSO, and the browser extension catches AI used straight from the browser.
Agents that employees build can be governed too, alongside the tools they use.
One product for two pressing problems. AI compliance had become as urgent as AI cost, and CloudEagle.ai solved both without a separate tool for each.

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Impact

Every Token Has a Source

2.8M tokens a month trace to a specific user, project, or model, with no unattributed spend left over.
A spend spike gets explained in minutes, by model, by project, or by user, instead of staying a mystery on the bill.
Model-level and session-level data show not just how much was used, but on what and by whom.

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The Invoice Finally Has Something to Check Against

$20K+ in monthly AI token costs gets verified against tracked usage before finance pays it.
IT and finance work from the same consumption numbers, one reconciled figure instead of two.
Idle licenses reclaimed before renewal, 34% turned out to be inactive for 90+ days, caught the moment usage became visible.

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The CIO Has a Story to Tell

AI spend is broken down by team, project, and model, giving leadership a real basis for evaluating ROI.
Reporting AI spend to the board means pointing to actual consumption data, not defending a single number.
The same visibility scales automatically as new AI tools and teams get added.

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The Transformation

Before CloudEagle

Every AI invoice was a single number nobody could explain, just what was owed, with no way to check it.

Finding out what a spend spike came from meant a manual investigation with no guarantee of an answer.

Idle licenses sat unnoticed until a renewal forced someone to look.

IT and finance each defended their own version of the truth, and meetings started with whose number was right.

After CloudEagle

Every dollar of AI spend has a name attached to it, a person, a project, or a model, before finance ever pays the bill.

A spend spike stops being a mystery. It gets an explanation the same day it shows up.

Idle licenses get caught and dealt with as they happen, not discovered months later at renewal.

IT and finance sit down with the same number, so the conversation is about what to do next, not whose total is right.

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