Usually an engineering lead or a platform owner, with finance downstream. Both work in the same account, and each sees the part that is theirs.
Engineering holds the seats, the keys and the question of which tools help. Finance holds the month and the posting. ai-cur serves both without deciding which of them signs the order.
You said yes to Cursor, Copilot and Claude one request at a time, and now one budget line carries all of it. You want to know who uses what, which seats nobody opened, and whether a budget would have caught last month.
Start with the free Tracker for yourself, then open the team view as people install.
Several teams, API keys behind agents, maybe a gateway, and a leadership group asking whether the spend shows up in delivery. You need teams side by side without ranking a team that is too small, and caps your gateway can act on.
Team and Business add budgets, caps, commitments and your identity provider.
You get a settled month by cost centre, provider and commercial model, with open charges named and held back, and a CSV whose total matches the page. No individual is named in your view.
You see your own spend, your own seats and your own merged work, and nobody sees you by name unless they run your team. The Tracker on your Mac is yours, free.
What you need first depends on how the spend is bought, not on how many people you have. A company can buy in more than one of these ways at once.
Cursor, Copilot and Claude seats: start with idle seats and plan fit.
An annual commitment or a discount off list: start with declared commitments, drawdown and the rate basis of each charge.
API keys, on-demand plans and gateways: start with budgets, caps and the cap decision.
The FinOps Foundation's State of FinOps 2026 report puts it plainly: whether AI is providing value is a question nobody can answer yet1.
Engineering leaders say the same from their side. DX asked 50 engineering budget holders, and 86% were unsure which tools gave the most benefit, while 40% lacked the data to make a return on investment case2.
Across more than 400 organizations tracked for 14 months, DX observed a median gain in pull request throughput of 7.76%3. So the tools do something; what is missing is the record that sets their cost beside it.
What does your company spend on AI tools per employee each month?
For scale, Ramp's card and bill-pay data for June 2026 put the median company at $11.38 a month on AI for each employee, while the top 1% of companies spent $7.45K4. That spread is a question to ask yourself, not a line we draw: wherever your own figure lands, it tells you whether seats or budgets deserve your first hour.
Sales, support and finance teams use AI tools too, and their seats show up in the same bill. ai-cur reads that spend.
What it does not do is tell you what those teams got back. There is no open, vendor-neutral standard for measuring that return, and ai-cur makes no claim to one: its measured work is engineering work, pull requests and issues.
Each person signs in to the same month and sees only their part. Try it on yours, with the team for sixty days and no card.
