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How to track LLM costs per team and project.

Providers bill AI at the credential. Finance needs the team. How to attribute every model call to a team and a project, before the invoice arrives.

Topic
AI cost
Product
Novie Control
Format
Guide
Reading time
5 min
In short

To track LLM costs per team and project, record the cost of every model call when it runs and attach three tags to it: the team, the project and the workflow that caused it. Providers bill at the credential, so the tags have to be added in the path the call takes, before the money is spent. Then reconcile the tagged ledger to each invoice every month, and give anything untagged a named owner.

Why the invoice cannot tell you

A model provider bills by API key, account or project. It reports tokens and dollars per key and per model. It has no field for the team that ran the workflow, the project the work belongs to, or the business outcome it produced.

One key usually serves several teams. Marketing, engineering and operations share a credential because it was quicker to issue one than three. The invoice then shows a total and hides who caused it, and finance reconstructs the split from logs weeks later, when the month has already repeated itself.

The gap is common and measured. In 2026, 98 percent of FinOps practitioners manage AI spend, up from 31 percent two years earlier, according to the FinOps Foundation's State of FinOps 2026. KPMG's Q2 2026 AI Pulse Survey found 26 percent of large US enterprises had full, real-time visibility into what their AI systems cost to run. In New Zealand, Datacom's 2026 State of AI Index found one organisation in five has a dedicated AI budget with clear cost tracking.

Attribution is where it breaks. In the Tokenomics Foundation's State of Tokenomics, published in September 2026 from 472 large companies, visibility and attribution of spend was the second biggest challenge, after proving value. In research by the cost-management firm Harness, reported by CIO Dive in July 2026, one organisation in five could trace an unexpected AI cost spike to its source within hours. More than 40 percent still managed AI costs in spreadsheets.

What to record for every call

The record is a ledger line, written when the call runs. Ten fields cover it.

  • When. The timestamp of the call.
  • Team. The team accountable for the spend, taken from the caller's identity rather than typed in by hand.
  • Project. The piece of work the spend belongs to, as finance names it.
  • Workflow or agent. The process that made the call, so cost can be read per outcome.
  • Model and provider. Which model answered, from which provider, in which region.
  • Tokens. Input, output and cached tokens, counted separately because they are priced separately.
  • Price. The unit prices in force at the moment of the call, so a later price change does not rewrite history.
  • Cost. The computed dollar cost of the call.
  • Credential. The key or account the call used, which is what the invoice will show.
  • Budget. The budget line the call drew down, and who approved that budget.

Where to capture it

There are three places a call can be counted. The rule for choosing is simple: capture it where the call cannot avoid being seen.

  1. At the provider. Issue a separate key or project per team. It is quick and it gives a per-team view on the invoice, but it stops at the team: it cannot see the project or the workflow, and keys get shared again within a quarter.
  2. In the code. Wrap the model library so every call writes a ledger line. It is precise, but it depends on every team adopting the wrapper in every service, including the ones built before it existed.
  3. In the path. Route every call through one point that adds the tags, records the cost and checks the budget before passing the call on. It is one place to maintain, it covers every team by default, and it is where a cap can stop a call before it is paid for.

Most organisations start at the provider and end in the path. The first gives finance a number this month. The second gives it a ledger it can trust.

Rolling it out in four weeks

  1. Week one: list the spend paths. Every provider account, every key, every card charge for an AI tool and every vendor that has switched on an AI feature inside a product you already license. Ask each team separately; the differences between the lists are the first finding.
  2. Week two: put the tags in the path. Route model calls through one point that stamps team, project and workflow on each request. Where that is not possible yet, issue one key per team so the invoice splits at least that far.
  3. Week three: set the untagged rule. Decide that any call without tags is charged to a named person, the owner of the key it used. Untagged spend with an owner falls quickly; untagged spend labelled shared grows.
  4. Week four: reconcile and publish. Match the tagged ledger to each provider invoice, explain the difference, and send the first per-team and per-project report to finance and to each team lead.

Shared costs and the untagged remainder

Some cost belongs to everyone: the platform itself, evaluation runs, the agent that serves the whole company. Allocate it on usage where usage can be measured, keep it central where it cannot, and write the rule down before the first report so nobody argues it afterwards.

The untagged remainder is a line on the report with an owner and a trend. It should fall every month. When it rises, something new is calling a model outside the path, which is worth knowing for reasons beyond cost.

What finance should receive each month

  • Spend by team, against each team's budget.
  • Spend by project, against the business case that approved it.
  • The workflows that cost the most, with cost per run and per outcome.
  • The untagged remainder and its owner.
  • Every budget increase approved in the month, and who approved it.
  • The change from last month, with the reason in one sentence.

How Novie Control does it

Novie Control is live. Every dollar visible. Every budget governed. Every call optimised. Every agent governed. Every AI dollar is traced to the team, the project and the owner at the moment it is spent. Caps hold before spend is committed, with a named human on every increase. Each call is routed to the model that should take it, with failover when a provider stumbles.

It connects to the AI you already run and sits in the path the calls take, so the ledger is written as the work happens and finance reads attribution while the month is still open.

Sources

  1. FinOps Foundation, State of FinOps 2026
  2. KPMG, AI Quarterly Pulse Survey Q2 2026 (24 June 2026)
  3. Datacom, 2026 State of AI Index (September 2026), via ChannelLife
  4. Tokenomics Foundation, State of Tokenomics, Release Candidate 1.0 (23 September 2026)
  5. CIO Dive, 1 in 4 dollars spent on AI goes to waste, report finds (29 July 2026), reporting Harness research

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Frequently asked questions

How do you attribute LLM spend to a team or project?

Tag every model call with the team, project and workflow in the path the call takes, record its cost at the moment it runs, and reconcile the tagged ledger to each provider invoice monthly. A separate key per team gives a coarse split; tags give the project and the workflow.

Can a provider invoice show cost per team?

Only as far as your keys or projects are split by team, and only after the month has closed. An invoice has no field for the project or the workflow, so per-project cost has to be recorded when the call runs.

What should happen to untagged AI spend?

Charge it to a named person, usually the owner of the key it used, and report it as its own line. Untagged spend with an owner falls quickly. Untagged spend labelled shared tends to grow.

Is per-team tracking enough to control AI spend?

It shows where the money went. Control comes from a cap in the same path, set for each team and agent, that holds before spend is committed and asks a named person to approve any increase.

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