Launching today
DepthData
The system of record for your company's AI spend.
155 followers
The system of record for your company's AI spend.
155 followers
Companies now pay for four or five AI tools (ChatGPT, Claude, Copilot, and more) but can't answer the basics: what are we spending, who's using it, and which seats sit idle? DepthData connects every AI tool into one audit ready view of spend and adoption. What makes it different: every number is labeled by how it's verified, we never read prompts, and we show exactly what each vendor's API can and can't expose. The trusted system of record for your company's AI spend.
DepthData
@aliberkuyanik We lost track of AI spend the moment every team started expensing their own API keys and seat licenses. Finance saw one number, eng saw another, nobody could explain the gap. Are you pulling from provider billing directly or from usage logs, and can you attribute spend down to a team or project?
DepthData
Good question @arturbrugeman Honestly, it's both, and it depends on the tool. Anthropic, OpenAI, and Cursor have real cost APIs, so we pull actual dollar spend straight from them. Some tools don't offer that, so we pull usage data and combine it with the seat prices you enter from your contract. There's a contract pricing panel in Depthdata for exactly this. And every number gets a label, so you always know where it came from.
Teams are the funny part. The AI tools know who has a seat, but they have no idea what your org chart looks like. So in Depthdata, managers just assign people to departments right in the employee table, and spend rolls up from there.
Projects we built out fully. You create one, add people, and click into it to see cost, activity, and coaching signals. Some tools report project spend directly (OpenAI's API platform, Vercel if you set up tagging), so we pull that as is. Claude tells us project usage but not project cost, so we split each person's real spend across their projects based on how much they used each one. That number gets an ALLOCATED label instead of a measured one, because I'd rather be upfront that it's a split than pretend it's a measurement.
Basically: real data where the APIs give it, your input where they don't, and everything labeled so finance can actually trust the numbers. All of this is live in the demo if you want to see how it works.
@aliberkuyanik Congrats on the launch!
This is a real gap for us too. We run our own product on top of Anthropic's API, and tracking what each feature actually costs per run, especially after switching between plans, has been a spreadsheet exercise so far.
Curious how granular this gets, can you trace spend down to a specific feature or session, or is it more of a monthly aggregate view right now? We currently do that by hand and it's the part that doesn't scale.
Did the same exercise on AWS spend this year and the total was never the hard part, attribution was. What actually moved the number wasn't the dashboard, it was being able to put a name next to each line so somebody had to defend it.
Your rule about never showing a figure you can't verify from the tool's own API is the right call. The follow up I'd want: what happens to the spend no provider API will tell you about, like the personal ChatGPT subscription someone quietly expenses? That shadow half is usually where the surprise lives.
DepthData
@dalemooney That AWS experience is exactly the thesis. The total never changes behavior, a name next to the line does.
On shadow spend, straight answer: no AI vendor API will ever show a personal subscription someone quietly expenses. That money lives in your expense system, not the vendor's org account. So Depthdata shows it as a gap, not a number. Closing it properly means pulling from the expense side, which is a different connector class. Until then, same rule as everything else: no API to back it, we show the gap instead of a guess.
@aliberkuyanik Showing it as a gap rather than a guess is the part I'd actually pay for, and I think it's a stronger line than you're currently selling it as. In the AWS work the number that changed behaviour wasn't the total, it was the list of spend nobody would claim. A named gap forces a conversation. A total just gets nodded at and filed.
One thought before you build a whole connector class for the expense side: finance already exports that data monthly, usually as CSV, because they do it for the accountant anyway. A dumb upload that reconciles vendor names against the tools you already know about would close a lot of the gap without integrating with anything. Might be worth checking whether people need the full connector or just the first thing you'd reach for.
The verification label is the actual product here, the dashboard is just where it lives. What you're missing is that idle seats are the easy half. On anything usage priced, one person's month can outspend the other forty put together, and a seat view shows those two people as identical, so you cut the wrong licence and save nothing. Worth naming which vendors can't expose per user consumption at all, because that gap is where the spreadsheet quietly goes wrong.
DepthData
@asadmalik901 You're right, the label is the product. And agreed, idle seats only matter on seat priced tools. On usage pricing the money concentrates, one heavy user can outspend a team, and a seat view hides that. So Depthdata shows cost per person, not just seats. Those numbers come straight from vendor APIs, Anthropic and OpenAI and Cursor all expose per user spend in their docs. Demo runs on sample data today, but nothing on screen an endpoint can't back.
The gaps, Gemini bundles AI into Workspace so per user cost doesn't exist, Replit pools credits with no per member API, Vercel needs tagging first. We show those as gaps instead of estimating over them.
Never reading prompts, only metadata, is the detail that gets this past a security review. Most spend trackers ask for way more access than the actual problem needs.
DepthData
@irahimiam Exactly. Most spend trackers ask for way more access than the problem needs. Depthdata is read only and metadata only: seats, usage counts, spend. The endpoints we connect to don't carry prompt content at all, so conversations never enter our system, there's nothing to leak. Narrow scope is the architecture, not a promise, and that's what security teams actually check.
This is a smart wedge ,most spend dashboards mix hard numbers with guesses and never tell you which is which, so the second someone actually audits it, trust falls apart. Curious how you handle vendors that barely expose anything beyond seat counts , do you just flag those as low-confidence, or is there a minimum bar of data before a tool even gets added? Also wondering if you're planning to cross-check against SSO/IdP logins (Okta, Entra etc.) at some point, since that's often where you get a more honest "who's actually using this" than the vendor's own console gives you.
the verification labeling and the per-user vs seat point already cover most of what I'd have asked. one thing I didn't see come up: overlap across tools rather than idle seats within one tool. we've ended up paying for two AI tools that do 80% the same job for the same people, because each one got adopted separately by a different team before anyone compared them side by side. that's not an idle seat in either tool, both look fully used, the waste is that the org didn't need both. is that something DepthData could ever surface, or is it necessarily out of scope since it's a cross-tool judgment call rather than a per-tool number?
Pazi
Congrats on the launch! Getting a single source of truth for AI spend is exactly what teams need right now — love the audit-ready angle. Best of luck today!