Analyze
Live margin. Clear attribution.Analyze usage.
Revenue, cost, and usage data captured at the same moment, with clear vendor, customer, and feature attribution — always live, reported the moment usage happens.
What changed?
Your best customer could be your worst deal.
Why teams analyze usage with Limitr
Catch margin erosion while it's still small.
A drift in price or spike in usage caught this week can be a quick fix. Caught at quarter-end or after finance has already closed the books on it, and it's a larger problem. Margins that update with each call catches the difference while it's still small, and alerting ensures the correct team is informed, live.
Know which customers are currently profitable.
Revenue size and profitability aren't the same measurement, and they're changing minute by minute, not just month to month. See margin per customer as it happens so you know which accounts are quietly losing money.
Prioritize features that are worth building further — not just popular.
It's not enough to just know what's popular or how much you're spending on tokens. A heavily-used feature can be a margin sink if its cost outpaces what it's priced to capture. A worth-while determination is nuanced — make sure you have the right data to make decisions against.
Know what each vendor relationship is worth.
Running multiple models or vendors means cost-per-equivalent-outcome varies more than most teams realize. Minimize spend with dynamic model switching based on plan, usage, current metadata, and/or margin. Most teams seek cost-to-deliver AI, but are unable to answer margin-to-deliver outcomes — be one that can answer both.
One usage policy
Monetize, Control, and Analyze aren't three separate systems — they're three views into the same policy document. You can't price what you can't measure, and you can't control what you can't see.
See the spec
One exchange, every dimension
Currency and credit exchange in one mechanism, enables slicing margin by customer, feature, or vendor. Ask questions from any angle, and be confident that your comparisons are correct and meaningful — USD, tokens, seconds, credits — both backward and forward.
Credit exchange
Alerted, not just reported
The runtime evaluates every call as it happens. Notify the right team the millisecond a threshold crosses — a margin gone negative, a sudden usage spike — instead of missing it in next week's report.
Set alerts
Common analytics questions
Margin & attribution
Each credit definition holds your price and an optional overhead cost. This is how margins are calculated in real-time per call while remaining flexible enough to capture custom or non-list prices.
Live. Margin per customer reads from the same ledger that meters usage and prices it in real time — there's no separate batch job reconciling yesterday's data overnight, because there's no gap between when a call happens and when it's reflected in that customer's number.
Features map to entitlements, and entitlements are however granular you define them — a feature, a specific model call, an internal workflow step, whatever boundary is actually meaningful in your product. There's no requirement that it map to a UI-visible feature flag or a product team's own taxonomy.
Most teams start coarse — a handful of major capabilities — and split further only once the coarse view surfaces something worth investigating. You don't need a finished feature taxonomy before this becomes useful.
Entitlemens are also often stacked and used together for different purposes. The access gate, token meter, and outcome billed may all be separate entitlements used within the same operation, and the user may only get invoiced for outcomes or tokens, or not at all.
No — outcome is defined by you, not assumed to be a raw API call. If quality matters more than raw cost for a given use case, the outcome unit can be a resolved query, a passed eval, a customer-facing result — whatever the comparison should actually be measured against.
The point isn't always pick the cheapest vendor. It's making the real tradeoff visible — cost, quality, and reliability side by side — instead of defaulting to whichever vendor was integrated first and never revisited.
Same data, same integration. Analytics isn't a separate pipeline bolted on afterward — it reads from the identical ledger that Monetize prices against and Control enforces against, because it's the same policy document underneath all three.
Practically: if you're already metering usage for pricing or limits, the margin and attribution data is already there. There's nothing additional to instrument specifically for analytics.