Every step your AI agent takes costs tokens.
You can see the money leaving.
Nothing tells you why.
Run better. Plan better. Build better.
TokenWake
can show you why.
And what to change.
Across every team, every workload and every model choice.
Worked scenario
An AI budget built to last twelve months.
It lasted four.
That happened in 2026, to a large engineering organization, and it is public record. We modeled the shape — 5,000 engineers, adoption doubling in a month — and let TokenWake price it.
- $2.2M in four months, still accelerating. Draw any budget across that curve and you get the date it breaks.
- A spending cap bought 23 days — and stopped 20,459 sessions mid-work. The budget still ran out.
- Same cap, different models — the spend came inside the budget, with no change to volume or behavior.
That is the answer TokenWake computes.
A modeled scenario, not a prediction.
That’s a view from the top. We see yours team by team.
What TokenWake shows you
Your environment.What it will cost, how it could run better, and what a change would do.
We map where your company uses AI: which teams, doing what kinds
of work, on which models.
Then we simulate that work to get its realized cost, the human
and agent cost of retries and rework included.
But where will your numbers come from?
Our numbers are different
Dashboards show what happened.We show what happens next.
We simulate. We don't estimate.
Tools built on past usage take what you spent before and stretch it forward. We calculate realized cost.
Here’s how ours works:
Your work.
You describe it, or we import it. We never need your prompts or outputs.
The models.
Published industry metrics tell us how each one behaves, across seven providers, refreshed as new models appear.
The simulation.
Plays it out tick by tick, with every model combination in the catalog, not a sample, in minutes. It counts the rework and retries that cascade forward from earlier steps, and the people who review them.
Your usage.
Session-level usage metadata tunes the simulation to your environment. We calibrate on it; we don't extrapolate from it.
We are not limited to what you have already done.
A dashboard can only show what is. We can show what could be.
Leaks and runaway costs?
We find them and tell you how to fix them.
A new model out today?
We price it on your work on day one and tell you how best to use it.
Is it worth building?
Your value proposition needs a sound realized cost figure. We give you that before you build.
What if volume doubles?
Change the assumption and see what happens to cost and quality.
A wind tunnel for your workflows.
Every team, every workload,
every model choice,
visible at any time.
Here are six ways to use it.
The services
Six services. Start where you are.
Run better
AI environment efficiency assessment
“How am I doing?”
How each team is performing, which models it should be using, and where the waste is.
Run better
Work archetype remediation
“This one is costing too much.”
We find the step responsible for the leaking cost, and price the ways to fix it side by side.
Run better
Dynamic optimization
“Keep me current.”
Every month from your usage, and whenever new models or prices appear, we recalibrate against production and keep your model recommendations current.
Plan better
Forecasting
“What will this cost?”
Realized cost for each kind of work, cascading rework included, as P50 and P90 ranges, rolled up by team and across the enterprise, calibrated on your usage.
Plan better
Controlled comparison
“Which one is doing it better, and why?”
Two teams or setups doing like work, compared with everything else held constant, and the difference traced to its cause.
Build better
Design validation
“We’re building something new.”
Structural soundness: diagnose and fix leaks and runaways before you run a design. Then see its realized cost, the full cost side of a cost/benefit analysis.
Start a conversation
Start with the question that sounds like yours.
Pricing depends on your work. Contact us and we'll scope it.
Run better. Plan better. Build better.