Introducing Attribute™ — trace every AI token to its owner
Cloud Intelligence™Cloud Intelligence™

Your AI Bill Has No Tags. Now What?

A tactical guide for FinOps teams on closing the AI attribution gap without a tagging project or code changes.

AI spend arrives as one line item from OpenAI, Anthropic, or Bedrock. A token call has nothing to tag, so traditional Allocation breaks. This guide shows how traffic-level attribution maps every token back to the customer, feature, and agent that triggered it.

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01

The 5-point attribution audit

Score your current visibility on provider coverage, customer, feature, agent, and anomaly attribution, and see exactly which gaps reporting alone won't close.

02

Three attribution mechanisms, compared

Manual instrumentation vs. SDK wrappers vs. traffic-level attribution: the fragility trade-offs of each, so you pick the one your team can actually sustain.

03

One method for OpenAI, Anthropic, and Bedrock

How to keep customer, feature, and agent identifiers consistent across all three providers without asking engineering to change application code.

04

Where to start: cost per customer

Why cost per customer is the highest-value question to answer first, and how feature and agent attribution follow naturally once the pattern is in place.

05

What's inside the guide

Implementation patterns, common pitfalls, and the checklist for defining attribution units with finance before you instrument anything.

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Frequently asked
questions

How do I attribute OpenAI or Anthropic spend to a specific customer?

You read the traffic, not the invoice. Every token call carries context about which customer and feature triggered it, and traffic-level attribution captures that context before the bill aggregates it away.

We already have a FinOps tool that ingests our OpenAI and Anthropic bills. Isn't that enough?

Ingesting a bill gives you the total. Attribution gives you the cost per customer, feature, and agent. These are different problems, and provider invoices don't carry the data needed to answer the second one.

Can't we just tag our LLM calls in application code?

Manual instrumentation works at small scale. It breaks once you have multiple agents, teams, and providers to keep in sync. Traffic-level attribution removes the code-change burden and works consistently across providers.

Isn't this just another cost dashboard?

No. A dashboard tells you what you spent. Attribution tells you who caused the spend and why. The guide covers why that distinction matters for AI workloads specifically.

Does this fit inside the FinOps Framework?

Yes. The FinOps Framework treats Allocation as a core capability, and traffic-level attribution extends it to AI spend where tags have no anchor. The guide maps the approach to the Automation, Tools, & Services capability.

Where can I learn more?