AgentPing for SaaS teams
On a flat plan, inference cost is never evenly distributed. A handful of accounts run ten times the volume of everyone else, and nothing in your billing tells you which ones. AgentPing attributes every run to the customer and feature behind it, so you can name the unprofitable accounts, price the next feature on real unit cost, and see margin move before the invoice does.
One customer, ten times the usage, same monthly price.
→They are enthusiastic, they are a reference customer, and they are the most expensive thing in your infrastructure. Nobody finds out until someone divides the provider bill by account count and the average stops matching anyone.
The number came from a demo, not from production.
→Demo prompts are short and clean. Real ones arrive with pasted documents and long histories. Price against the median and the tail eats the margin, and repricing a live feature is far harder than pricing it once.
A prompt change added context to every call.
→Nothing broke. No alert fired. Cost per request rose by a third, gross margin dropped a few points across the quarter, and the whole thing was attributed to growth because there was no per-feature number to check it against.
One run event carries the customer, the feature and the cost, so all three failures above become a query instead of an investigation.
Group spend by account and see the distribution. The heavy tail is usually a handful of names, and naming them is what makes a plan change or a usage component possible.
Churn from a degraded AI feature does not arrive as a bug report. Sampled scoring on live runs catches the drift while it is still a chart and not a cancellation.
A customer-facing agent that stalls should page you, not generate a support ticket. Freshness alerting closes the gap between failing and finding out.
Instrument one customer-facing workflow and let it run for a week. The distribution usually answers the question on its own.
For product teams → For platform engineering → All features →