Know where your AI spend is heading
SAVI projects your AI spend to the end of the month by team and workflow, with a confidence range, so budget conversations start from where you are heading and not only where you have been.
Budget surprises come from not seeing the trend
Provider charges build up token by token, so by the time a month closes the number has already moved. A projection shows where spend is heading while there is still time to act.
Unplanned AI spend is easy to underestimate
Providers charge for every token processed, not only the useful ones. Forecasting at team and workflow level shows where growth comes from.
Over-budgeting and under-budgeting both cost you
Too much budget leaves capacity unused. Too little means emergency approvals and friction with engineering.
Recommendations need context
Seeing the likely saving of a change helps your team decide what to act on and what to leave.
See where spend is heading, by team and workflow
SAVI blends month-to-date spend with the recent daily rate to project spend to the end of the month, with a confidence range. The range narrows as more history builds up. Forecast snapshots are kept, so you can compare them with what actually happened.
- End-of-month projection by team and workflow
- A confidence range that narrows with more history
- Forecast snapshots to compare with actuals
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Example data. End-of-month forecast by team, total $56,000, with a confidence range
Example data. Growth of 25% on one workload, with caching and routing savings applied
Plan with scenarios
Test the cost of growth before you commit. SAVI models a base, a high and a stress case for each workflow, and estimates savings from caching and model routing, so finance can see the range of outcomes.
- Base, high and stress cases per workflow
- Estimated savings from caching and model routing
- Growth shown per team and workflow
Act on the right savings, not every one
Treat each saving as the start of a conversation with engineering, not an instruction. SAVI shows the estimated saving from caching and model routing, so you can weigh it against the effort and decide what fits your workload.
- Savings from caching and model routing, with an estimate for each
- Weigh the estimated saving against the effort before you act
- Check the forecast after a change to see what moved
Example data. Estimated savings, total $14,480 a month
Do you know what your AI spend will be next month?
6 questions. Answer honestly.
Can you project your AI spend at team and workflow level, with a stated range?
Can you track AI spend over time and see whether it follows business growth?
How do you decide which cost-saving recommendations to act on?
Are cost recommendations reviewed with engineering, or passed over the fence?
Can you see the cost impact of a change after you make it?
Do you check how new model releases affect your cost structure?
This checks one slice of your AI estate. Take the full AI Cost Ownership Assessment →
Your AI gap score
Answer to begin
Complete all questions to see your result
See where your AI spend is heading.
Book a discovery call to see an end-of-month projection for your own teams, with a confidence range your CFO can use.