Connect AI Spend to Product and Margin

Govern access before spend happens

AI features can look successful on adoption metrics while quietly eroding gross margin. A dashboard that only shows total model spend can't answer the question finance actually asks: is this feature worth what it costs, and is that still true as usage scales? Teams need cost measured against the business outcome the call produced, not just a provider total.

• Token totals don't say whether an AI feature is profitable
• No agreed way to compare LLM cost against the value a resolved ticket, generated document, or completed workflow actually delivers
• ROI math gets redone by hand every quarter, with no consistent baseline
• A new pricing negotiation or model swap silently invalidates last quarter's ROI numbers

One policy layer across model usage

Usage is grouped by business dimensions — customer, workspace, plan, workflow, run, or product feature — and by the business-transaction fields your SDKs and gateway calls already carry: transaction id, transaction type, unit count, outcome status, and business value. Cost per unit of value is then measured directly: cost per resolved ticket versus the value of a resolved ticket, cost per generated document versus its baseline cost pre-LLM. ROI calibration makes that comparison durable — each business transaction type has a customer-configured value per successful outcome, a pre-LLM baseline cost or effort, an owner, and an effective date range, so calibration changes don't rewrite history.

• Business-transaction fields (transaction id/type, unit count, outcome status, business value) flow from SDKs and gateway calls into unit economics without a parallel ROI model
• ROI calibration: value per successful outcome, pre-LLM baseline cost, owner, and effective date range, versioned so a recalibration doesn't retroactively change last quarter's numbers
• Unit denominators let teams compare LLM cost against operational and revenue metrics directly, not just against itself
• Existing reports stay tied to the calibration version active when they ran; new reports use whatever calibration is active for their effective date

Start with one app, then expand

Define one or two core units, such as cost per ticket or cost per customer workspace, set an initial ROI calibration for that transaction type (value per success, and a pre-LLM baseline if one exists), then use model and app breakdowns to find the workflows with the highest margin pressure.

Built for private production AI

Cloptima's attribution layer combines AI usage, cloud ownership, Kubernetes context, and business metadata so unit economics can be measured across the full product cost stack. Business-transaction fields are attribution metadata only — they enrich cost analysis but can never override the authenticated tenant identity a request actually belongs to, so ROI reporting can't be spoofed by a caller-supplied field.