Building AI Chargeback: From Metering to Billing

Your AI platform is running. Teams are using it. Someone from Finance just asked how much each team is actually spending and you don't have a clean answer.

That's the chargeback problem. It sounds like a billing question, but it's really an infrastructure question. Token consumption, model selection, request volume, latency tiers. None of that maps cleanly to a cost center without the right plumbing in place.

In this session, we'll walk through what it actually takes to build AI chargeback from the ground up: how to meter AI traffic at the gateway layer, how to aggregate usage data by team, product, or application, and how to turn raw consumption metrics into something Finance can act on.

We'll cover:

• Metering at the source — capturing token usage, model calls, and latency without instrumenting every app
• Usage attribution — tagging requests to cost centers, teams, or tenants at the API layer
• Aggregation and reporting — turning per-request data into monthly cost summaries
• Chargeback vs. showback — when to bill internally, when to just make usage visible, and why the distinction matters