Every serious social media operator I know has a spreadsheet they don’t talk about. It’s not the content calendar. It’s the quiet list of AI subscriptions, API credits, and “just trying it for a month” SaaS charges that have attached themselves to our workflows over the past eighteen months. ChatGPT for captions, Claude for research, Copilot for whatever Microsoft is doing now, Canva for design, CapCut for edits, plus a scheduling tool and a half-dozen analytics platforms. The subscriptions sit on different cards, some annual, some monthly, and nobody can answer the only question that matters: is this thing actually paying for itself? So when DepthData launched on Product Hunt with the phrase “the system of record for your company’s AI spend,” I didn’t read it as enterprise finance software. I read it as the same disease I’ve been managing with a color-coded tab. The product is built for companies, not creators. But the discipline it’s pushing — verified numbers, named gaps, no fake precision — is exactly what social media operations need.
The AI sprawl is now a content-operation problem
The creator economy stopped being a hobbyist funnel and became a P&L line. The content calendar is no longer just a calendar; it’s a production pipeline that runs on AI tools at every stage. Most teams I talk to have at least one AI writing assistant, one AI image generator, one video editing tool with AI features, and one analytics platform that claims to use AI to tell you when to post. The problem isn’t the tools. It’s the fact that nobody can say what they cost, who actually uses them, and which seats are just burning money.
DepthData’s launch copy puts it bluntly: companies now pay for four or five AI tools but can’t answer the basics — what are we spending, who’s using it, and which seats sit idle. That sentence is written for finance teams, but it maps directly onto the social media stack. I’ve run accounts where the “creative tooling” line in the budget was a prayer, not a number. A team of five can have six AI subscriptions and a contract for a scheduling tool that only one person uses, and nobody notices until the annual renewal hits.
The deeper issue isn’t the total. It’s attribution. In the Product Hunt comments, one commenter named Dale Mooney describes doing the same exercise on AWS spend and lands on the core insight: “The total never changes behavior, a name next to the line does.” That is the most useful sentence in this entire launch thread. For social media operators, the equivalent is knowing that one content editor is burning $80 a month on an AI video tool while the person who actually edits the videos uses something else. The total is just a number. The named line item forces a conversation.
That’s where DepthData is aiming. The product promises an audit-ready view of AI spend and adoption, and the maker has a hard rule that matters: never show a number that can’t be verified from the tool’s own API. Every figure is labeled by how it was verified, and the product never reads prompts — only metadata like usage and seats. For anyone who has been burned by a dashboard that looks confident and then falls apart when you ask “where did this number come from?” that is a genuinely refreshing posture. Most social media analytics tools should be forced to adopt the same rule.
What DepthData actually does — and what it refuses to do
DepthData is the work of Ali Uyanik, a product designer who says he built it mostly solo after watching companies buy more and more AI tools without knowing what they were spending or who was using what. The product connects AI tools into one spend-and-adoption view, but the interesting part is how it handles the messiness of vendor APIs. In the comments, Uyanik explains that the data pulling is a mix of billing APIs and usage logs. Anthropic, OpenAI, and Cursor have real cost APIs, so DepthData pulls actual dollar spend from them. Other tools don’t offer cost APIs, so it pulls usage data and combines it with seat prices entered from your contract. There’s a contract pricing panel for exactly that.
The verification labeling is what separates this from a typical dashboard. If a number comes straight from a vendor’s cost API, it gets one kind of label. If it’s based on usage data plus contract seat prices, it gets another. If a number has to be allocated because the vendor doesn’t report it, it gets an “allocated” label rather than being presented as a measurement. Uyanik’s example is telling: Claude reports project usage but not project cost, so DepthData splits each person’s real spend across their projects based on how much they used each one. That number gets an allocated label, not a measured one, because he’d rather be upfront that it’s a split than pretend it’s a measurement.
The “no fake precision” stance also shows up in what the product won’t do. When a commenter asks whether DepthData can trace spend down to a specific feature or session, Uyanik’s answer is the honest kind of no: as deep as Anthropic’s API goes, which is daily cost by workspace, model, and token type, and usage down to minute buckets by API key, workspace, and model. Session-level reporting isn’t exposed by the vendor API, so DepthData shows that as a visible gap, not an estimate. That’s a small line, but it’s a big deal. The entire product is built around the idea that a gap you can see is more useful than a number you can’t defend.
Why verification labels matter more than dashboards
Every social media dashboard reports numbers. Very few tell you where the numbers came from. I’ve spent years staring at platform analytics where “reach” is a modeled estimate, “engagement rate” is calculated differently in every tool, and “views” can include autoplay loops, sound page views, and paid impressions that are never cleanly separated. DepthData’s labeling system is a good reminder that provenance is a trust feature, not a technical detail.
Imagine if your social analytics tool labeled every metric the way DepthData labels spend: “measured from the platform API,” “estimated from a sample,” “allocated based on our model,” or “unknown because the API doesn’t expose it.” That would kill half the arguments in weekly reporting meetings. A number that comes with a source and a verification method is a number you can act on. A number that arrives with no source is a vibe. Vibe reporting is how teams end up doubling down on a platform that isn’t working because the dashboard said engagement was fine.
How it differs from the tools you’re already using
The natural comparison for an AI spend tracker is a finance tool like Expensify or Ramp. Those are great at tracking card transactions, but they don’t understand the difference between a seat license, a usage-based API bill, and an “allocated” cost split across projects. They can tell you that someone spent money at OpenAI. They can’t tell you which project consumed the tokens, which team owns the workspace, or whether a seat has been idle for three months. DepthData is trying to be a layer above the spend capture layer — closer to a system of record than an expense report.
It’s also not a business intelligence tool. You could build something similar in Tableau or Metabase, but you’d have to wire together every vendor API, normalize the data yourself, and then maintain it every time an API changes. The hard part isn’t the dashboard. It’s the API plumbing and the labeling logic. That’s why the “built with” nod to Claude by Anthropic on the Product Hunt page makes sense: this is a tool that lives and dies by how well it understands vendor APIs.
For social media operators, the closest analog is the gap between a social media management tool like Buffer and a full content-performance ledger. Buffer and its peers are great at scheduling and basic analytics. They tell you what you posted and how it performed. They usually don’t tell you the true cost of producing that post across every AI tool, human hour, and subscription involved. DepthData is built for a different job: not “what should we post?” but “what did this actually cost us and was it worth it?” That distinction matters as content teams get asked to prove ROI beyond vanity metrics.
There’s one more difference worth naming. Most spend management tools are happy to show you a total. DepthData is trying to show you a named gap. When shadow spend comes up — the personal ChatGPT subscription someone quietly expenses — Uyanik’s answer is direct: no AI vendor API will ever reveal that, and DepthData shows it as a gap rather than a guess. Then, after a commenter suggests uploading the monthly CSV that finance already exports for the accountant, Uyanik says he built it. The expense reconciliation feature runs entirely in the browser, matches vendor names against connected tools, and everything unclaimed shows up as a named gap with an amount next to it. That’s a feature born directly out of a user comment, and it’s the right product instinct: don’t paper over the unknown, expose it.
Why TikTok creators should care more than LinkedIn ones
If you’re a solo creator posting thought-leadership text on LinkedIn, AI spend tracking is probably overkill. Your stack might be one AI writing tool and one scheduling tool. But if you’re producing short-form video for TikTok or YouTube, the production loop is different. You’re generating hooks, scripts, voiceovers, captions, and variations — often across multiple AI tools — and then cutting versions to test against algorithm distribution.
TikTok’s algorithm rewards watch time, completion rates, and rewatch behavior. That means volume matters. You’re often making multiple video variations, and each variation costs AI credits and human editing time. If you don’t know which AI tool is eating your budget per video, you can’t make a rational decision about whether a five-variant test is worth it. LinkedIn posts are cheaper to produce and the feedback loop is more tied to network effects and early engagement. Not to mention that video editing tools with AI features are often priced per project or per export, which makes the cost per publish completely opaque. I’d bet most TikTok-first teams are sitting on more AI tool waste than they think, simply because they’ve never looked at it as a production cost.
What creators and social teams can borrow from it
Even if you never open DepthData, the mental model is worth stealing. The first thing to do is label your data. For every number in your content report, know whether it came directly from the platform API, from an analytics vendor’s model, or from a spreadsheet you built yourself. That doesn’t mean you have to build a verification system. It means you stop presenting estimates as facts to your client, boss, or team. A label like “platform-reported” versus “vendor-estimated” changes how much weight a number can carry in a decision.
The second thing to borrow is the named gap list. Write down what you don’t know, with a cost attached when possible. If you don’t know which AI tool produced which conversion, that’s a gap. If you don’t know whether a platform’s “reach” metric includes paid impressions, that’s a gap. If you don’t know which seat licenses are actually being used, that’s a gap with a dollar amount. The point is to make the unknown visible enough that somebody has to own it. As the commenter put it, a named gap forces a conversation. A total just gets nodded at and filed.
The third thing to borrow is the separate-workspace trick. Uyanik’s advice for tracking per-feature AI spend is to give each feature its own API key or workspace so the vendor’s cost report does the attribution for you. Social media teams can do the same thing with content functions. Use one workspace for captions and written content, another for image generation, another for video scripting, and tag your projects when the tool supports it. If you’re using OpenAI’s API platform or Vercel, project tagging means you can see exactly what a content experiment cost. The more you can get attribution from the tool itself, the less you have to rely on estimates.
The fourth thing is the monthly reconciliation habit. Finance teams already export expense data as a CSV every month because they do it for the accountant. You can do the same for your creator stack. Export your card transactions, match vendor names against the tools you know about, and treat everything that doesn’t match as a first-class output rather than an error. The unmatched pile is where the waste lives. It’s also where the forgotten annual renewals live. That habit alone will pay for an hour of your time.
Where I’d pump the brakes
DepthData is early, and the product page doesn’t disclose pricing. That’s not a dealbreaker for a launch, but it makes it hard to evaluate the “should I use this” question. For a solo creator with two AI subscriptions, this is probably overkill. For a social media agency with a team and a real tool budget? It becomes interesting. For a company with an actual finance department, pricing and security review will matter a lot more than a Product Hunt demo.
The bigger limitation is the dependency on vendor APIs. DepthData is only as good as what Anthropic, OpenAI, and Cursor expose. Those APIs change, rate limits exist, and not every AI tool has a cost API at all. The product’s honesty about that is a strength, but it’s also a constraint. If a vendor stops exposing daily cost data, the “system of record” gets a new gap. The product can’t fix that. No product can.
There are also open questions the launch thread doesn’t fully answer. When commenter Gal Dayan asks whether the “cost of the second tool” comparison holds up when one tool is per-seat and the other is usage-based, Uyanik doesn’t have a clean answer yet. That’s a real problem for any team comparing cross-tool overlap, because the pricing models rarely match. One tool’s cost is fixed and predictable; another’s is a variable usage bill. Comparing them requires normalization, and normalization requires assumptions. The labels help, but they don’t make the comparison clean.
And if you want granular, run-level cost data for content experiments, this isn’t the tool. The vendor APIs don’t provide it. DepthData will honestly show you that gap, which I respect, but it also means you still need your own logging if you want to know what one specific video concept cost from prompt to publish. For social media operators, that’s the kind of data that makes or breaks a content ROI conversation.
What I’d watch / test next
This week, do a one-hour AI stack audit. Write down every AI tool you pay for, every seat you own, and every API key that has a card attached to it. Label each line as “billed,” “allocated,” or “unknown.” The unknown lines are the ones that will surprise you. Then do the CSV trick: export last month’s card transactions and match vendor names against that list. Keep the unmatched items in a visible list. That list is your shadow spend.
If you have a team, give each content function its own AI workspace or API key so the vendor does your attribution for you. Add a “named gaps” section to your next content report and put at least one dollar amount in it. And if you’re genuinely dealing with multi-tool AI sprawl, take DepthData’s demo for a spin and ask the question Uyanik clearly wants you to ask: “Where did this number come from?” If the label doesn’t survive that question, the tool isn’t doing its job. If it does, then this is exactly the kind of discipline the creator economy needs to grow up into.
