Static, backward-looking plans miss the feedback loops that drive overruns and panic buying. By the time a shortfall shows in a report, the cheap decision window has closed.
Capacity and demand forecasting for your compute estate, plus scenario simulation reconciled to your real numbers. Pure and deterministic: every date is an input, no clock inside. Forecasts you can defend to a board or an auditor, because the packet states the method, the history it ran on, and its error.
FORECASTING
Forecasts you can defend to a board.
Seasonal by design: next Tuesday looks like the last Tuesday, with no parameters to overfit three weeks of history
Cost basis: the bench figure first, the canary-measured figure once it exists
Pure and deterministic: every date is an input
BACKTEST
The number arrives with its error.
Each trailing week predicted from the week before it and scored against what happened
The packet states the method, the history and the error; the reviewer never takes the number on trust
A model we could not backtest on the history we hold is a model we do not ship
30D
Backtest
Backtest
Week
Predicted
Actual
Error
w-3
41.2 CPU-h
39.8
+3.5%
w-2
40.1
43.0
−6.7%
w-1
42.9
42.1
+1.9%
next
43.4
—
backtest ± 4.0%
SCENARIOS
Scenarios reconciled to your bill.
Campaign, launch, new customers: the team's assumptions next to the measured demand
See which hypotheses the data supports and which still need testing
Buy nothing until the reclaimed idle is spent
OUTCOME MONITOR
Healthy, flagged, or rolled back.
The serving kernel's telemetry is judged against SLO rules every cycle
A shadow divergence flags without touching the tenant: the original still serves and the golden is kept for the rebuild
A breach unpins, records the rollback and notifies
30D
Outcome monitor
Outcome monitor
Target
Stage
Fallback
p99
Verdict
billing.proration
canary 10%
0.0%
238 ms
Healthy
etl.reproject
shadow
0.4%
—
Flagged
media.transcode
primary
2.1%
1.4 s
Breach → rolled back
AI & GPU
Cost per token. Cost per GPU.
Inference cost attributed per model and per endpoint: cost per token, per request, per tenant
Fleet-wide GPU capacity and headroom, so you know what you can serve before you buy more
Route inference by performance per dollar and see what each decision saves
EXECUTIVE REPORT
One report, four desks.
Ranked actions per decision-maker, with the source window and the method stated
Financial figures labeled as scenarios or attributed costs, never added into a fake total
PDF on demand, with its sources
30D
Backtest
Backtest
Week
Predicted
Actual
Error
w-3
41.2 CPU-h
39.8
+3.5%
w-2
40.1
43.0
−6.7%
w-1
42.9
42.1
+1.9%
next
43.4
—
backtest ± 4.0%
30D
Outcome monitor
Outcome monitor
Target
Stage
Fallback
p99
Verdict
billing.proration
canary 10%
0.0%
238 ms
Healthy
etl.reproject
shadow
0.4%
—
Flagged
media.transcode
primary
2.1%
1.4 s
Breach → rolled back
BEFORE YOU START
Before you start
What horizon?
Weeks to twelve months, with every assumption stated and the backtest attached.
Which data feeds it?
Billing, kernel telemetry, serving telemetry from deployed kernels, and the projections your team provides.
Is a forecast a guarantee?
No. It is a scenario with its assumptions and its error visible. Results are measured after the change.
What's next
Start with the 48-hour assessment or bring one workload. We agree the scope in the first conversation.
