Environmental impact
We know what our AI costs.
Most AI products can’t tell you how much water or power they use. We measured ours, and we publish it.
Last updated
July 2026
Figures
Estimates · production logging live
Scope
Water consumed by inference compute
Next revision
Quarterly
01 · The problem
Start with what the problem actually is.
AI runs on data centers. Data centers run hot, and cooling them evaporates fresh water out of the watershed they sit in.
The water isn’t contaminated. It’s removed. In a region that already has enough, the effect is small. In a region under drought stress, it isn’t.
So the question worth asking isn’t how much water the whole industry uses. It’s how much yours uses, where it goes, and whether you’ve done anything about it.
Here’s ours.
02 · The number
One marketing plan uses less water than a toilet flush.
Every Storia plan is a measured build. We know the exact token count of every step in the pipeline, so we can estimate the compute behind it and the water that cooling that compute consumes.
1–3 liters
Estimated water per marketing plan
~1.8 million
Tokens processed per plan
12 minutes
Time to a finished plan
A token is the unit of work an AI model does. Yours is a big number because your plan is long and specific. The water behind it is still smaller than one flush.
Flush figure is the 1.6-gallon US standard, for scale
Scale check · one football field, watered once = 15,000 gal
A full year of Storia uses less water than a single sprinkler cycle on one field. Not once a week. Once.
03 · Efficiency
The efficiency came first.
We ran an independent audit of every API call in the plan engine and asked one question: where are we spending compute we don’t need to spend?
The answer was nowhere. Every step already runs the smallest model that can do the job well. Heavy reasoning goes to the model that writes your plan. Formatting and structure go to lighter ones. Everything that repeats between steps is cached instead of recomputed, which cuts the work by more than an order of magnitude.
The audit recommended zero changes to reduce compute, because there was nothing left to cut without making your plan worse.
Model mix, per plan
“Under your constraints, the model mix is already right.”
Storia plan engine efficiency audit · July 2026
04 · Measurement
Every plan logs its own usage.
Estimates are a starting point. Our engine now records real token usage on every call it makes, which means the numbers on this page come from production, not from a spreadsheet.
We update this page quarterly with actual figures. If the number goes up, it goes up here too.
Revision log
05 · Next
Where we’re pushing.
01
Regional inference data
We’ve asked our infrastructure provider for regional data on where inference runs and when workload-level environmental reporting will be available. Most of the industry doesn’t publish this yet. Customers asking is how that changes.
02
Region-pinned deployment
We’re also evaluating region-pinned deployment, which would let us choose the exact grid and watershed our compute runs on and match restoration to it precisely.
This page will keep getting more specific. That’s the point of it.
Questions about any number on this page? impact@bystoria.com