AI cost management that answers the margin question

A provider dashboard tells you what you spent last month. It does not tell you which feature drove it, which customer is unprofitable at current usage, or whether a cheaper model would have held quality. As AI moves from a demo to a line item that scales with every request, that gap becomes the expensive one: spend grows with adoption, but nobody can point to the margin behind it.

The missing piece is attribution with economics attached. Not just “we spent this on tokens,” but “this feature, for this tenant, on this model, at this margin.” That is the question CloudJaeger is built to answer — and to answer honestly, with the numbers it cannot stand behind clearly marked rather than filled in.

Your application POSTs a small usage event after each model call: provider, model, the feature and use-case labels you choose, an optional tenant id for chargeback, and the token breakdown. From that stream, CloudJaeger attributes cost across every dimension that matters — feature, customer, model, and task — so a single monthly total becomes a set of numbers each team can actually own.

Cost is computed from a canonical, server-side price book, not scraped from your logs. Each event is priced once, when it arrives, from the book in force that day and stored — so a later rate change does not rewrite past reports, and an event you backfill is priced at the rate in force when it arrives, not when the call was made. A model the book does not carry is recorded as unpriced — its tokens kept, no cost guessed — and the AI hub counts those events beside its totals. Token spend is computed only from the tokens behind it; AI spend read from your cloud bill, or a cost your app reports for an image or audio call, is shown as billed or reported — never as token spend. Where a field is missing — no token detail, no attributed revenue — the affected view says so. A null is rendered as unknown — an em-dash marked “unpriced” or “cost unknown” — never as $0.

Once you attribute revenue to calls, spend becomes a profit-and-loss view. CloudJaeger shows blended and per-customer margin, margin by feature, cost per request from a real denominator, and margin-floor breaches where a customer is underwater at current usage. For a team pricing an AI product, that is the difference between a bill and a business case.

Every dollar of opportunity is a projection — modeled from your own usage and labeled as such. We never present a projected figure as money already saved, and we never promise a number. The honest version you can verify next month is worth more than an optimistic one you cannot.

The advisory layer reads the same token stream and proposes the moves: swapping a model where quality allows, batching where latency permits, or reserving capacity where steady load makes it worthwhile. Each suggestion carries its projected impact and the caveat behind it, so a reviewer can start with the safe, reversible changes rather than guess.

Governance sits alongside it: budgets and kill-switches for agent loops that can otherwise burn through spend unattended, runaway-detection on cost bursts, and — for self-hosted inference — GPU fleet depth that reads billed-versus-used hours and idle telemetry. Nothing here executes on your behalf. CloudJaeger recommends; a human decides and acts.

The reason to trust an AI cost number is that the tool refuses to invent one. Tokens are priced from a server-side book, not your logs. Projected dollars are labeled projected, and none is ever shown as money saved. Absent data reads as unknown, not zero. That discipline is the point: a margin view you can take to a pricing conversation has to be one you can defend line by line.

CloudJaeger runs a ten-team design partner programme — you request access, it is reviewed by hand, and a read-only first look comes before anything else. No agent runs on your workloads to collect this; it is the usage events your application chooses to send.