Most AI coding-cost dashboards show a number and let you assume it’s a bill. Sometimes it is. Sometimes it’s a model built on token counts and a price sheet, dressed up to look exactly as confident as a real invoice. For a CFO signing off on a growing AI coding spend line, that distinction is the whole ballgame — and it’s one most AI analytics platforms don’t bother making. Olakai does, and Google Vertex AI is the clearest example of why it matters.
Usage is real-time. Cost isn’t — and the product says so.
Vertex AI token usage syncs into Olakai in real time — every request, every token, visible almost as soon as it happens. Cost is a different story. Google’s Cloud Billing export into BigQuery, which is what Olakai reconciles against for the real, billed dollar figure, typically takes 24 to 48 hours to catch up. Until it does, the Total Cost stat card, the Cost by Model breakdown, and the Cost by Developer table on the Vertex page all read $0 or an explicit “No cost data yet” — and the page carries an inline notice above the summary cards saying exactly that, not a footnote buried in documentation. Recent spend reads low for a day or two, on purpose, because the alternative is guessing.
That’s a real tradeoff, not a cosmetic one. A dashboard that always shows a confident-looking cost number, updated in real time, is easier to build and easier to demo. It’s also wrong for the most recent day or two of every reporting period, every time, without telling you. Olakai’s Vertex integration chose the less flattering, more accurate option: token usage updates immediately because it’s genuinely known immediately, and cost updates only once it’s genuinely known — reconciled to Google’s actual bill via the Cloud Billing → BigQuery export, not modeled from a public price sheet.
Three providers, three different honesty postures
Vertex isn’t the only provider with a gap between what’s easy to show and what’s actually true — it’s just the most transparent about it. Anthropic and Cursor’s cost data comes straight from their Admin APIs in real time, with no lag at all; what you see is what’s billed, as soon as it happens. OpenAI’s Admin API has a different limitation entirely: it doesn’t expose a user dimension on usage or cost data, so every cost figure on Olakai’s OpenAI page is tracked per API key, not per developer, until someone manually maps a key to a person through Developer Identities. Olakai surfaces that as an inline notice on the OpenAI page too, the same way it surfaces the Vertex cost lag.
Line those three up and you get three genuinely different honesty postures across five supported providers: Anthropic and Cursor (real-time, no gap), OpenAI (real-time, but per-key rather than per-person until mapped), and Google Vertex (accurate, but lagged 24-48 hours, reconciled to an actual invoice rather than estimated). Most cost dashboards flatten this into one undifferentiated “estimated cost” figure across every vendor. Treating five providers as five different measurement problems, and disclosing the difference in the product itself, is a small thing that adds up to a much more defensible number when it lands in front of finance.
Unattributed spend, by design
Per-developer cost allocation for Vertex works by taking each developer’s share of tokens consumed and reconciling that share against the actual billed total once it lands — an allocated figure, not a metered one, and Olakai is specific about which of the two it is. There’s a real limitation baked into that method worth stating plainly: per-developer Vertex cost specifically covers Gemini CLI usage. Antigravity and other Vertex activity that Olakai can’t track at the token level carries no token count to allocate by, so that spend lands in a distinct “Unattributed” row instead of getting force-divided across developers who may not have generated it.
That’s the same instinct as the cost-lag notice, applied to a different problem: when the system doesn’t have a reliable basis for attributing a dollar to a specific person, it says “Unattributed” instead of guessing. For a finance team trying to reconcile AI coding spend against headcount and productivity, an honest “we don’t know whose this is” line is more useful than a falsely precise number that quietly absorbs error into every developer’s total.
Why this matters more than it sounds like it should
None of this is exotic engineering — it’s disclosure. But disclosure is exactly what’s missing from most AI cost tools, and it’s exactly what a CFO needs before treating a number as board-ready. “This $4,200 is Google’s actual invoice, reconciled through BigQuery” and “this other number is a real-time token estimate that hasn’t been billed yet” are two different claims, and conflating them into one undifferentiated “AI spend” figure is how companies end up surprised by their own AI bill months after the fact — the same surprise Olakai’s budget alerts are built to prevent going forward.
The broader point of building this level of provider-specific honesty into the product is the same one that runs through the rest of the AI Impact Dashboard: a vendor-neutral platform that treats every provider’s data the way that provider’s data actually behaves, rather than smoothing five different billing realities into one uniform-looking chart. It’s a less impressive-sounding pitch than “real-time cost visibility across every AI vendor.” It’s also the one that survives an audit.
Want to see exactly which of your AI coding costs are real invoices and which are estimates waiting to reconcile? Talk to an Expert.