DepthData

The ALLOCATED vs measured label distinction is the right call. We ran into this exact thing with transaction categorization, once you're inferring instead of reading a hard number from the source, you have to keep that visibly separate or people start treating estimates as facts six months later when nobody remembers which number was which.

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?

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.

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!

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.

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.

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.

Hey everyone, Ali here 👋

I'm a product designer, and over the past year I kept noticing the same thing: companies are buying more and more AI tools, but nobody actually knows what they're spending across all of them or who's really using what. The answer usually means logging into five different admin consoles and cobbling together a spreadsheet, and even then you're mostly guessing.

So I built DepthData to pull all of it into one clear, audit ready view of AI spend and adoption.

The part I care most about: I made a hard rule that we never show a number we can't actually verify from the tool's own API. Every figure is labeled by how we know it, and we never read prompts, only metadata like usage and seats. I got tired of dashboards that look confident but fall apart the moment you ask "where did this number come from?" I wanted the opposite.

It's early and I'm building it mostly solo, so I'd genuinely love your honest feedback. What would make this actually useful for your team? What am I missing? Happy to answer anything.