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. That keeps historical reports accurate when a provider changes rates, and it means a dollar figure is only ever shown when the tokens behind it are known. Where a field is missing — no token detail, no attributed revenue — the affected view says so. A null is rendered as “needs data,” never as $0.

Once you attribute revenue to calls, spend becomes a profit-and-loss view. CloudJaeger shows blended and per-customer margin, cost per request from a real denominator, and margin-floor breaches where a customer or feature 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 — until a real invoice moves and grades it. 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 graded against a real bill only after you act. Absent data reads “needs data,” 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 is in private beta and runs as a founder-led pilot — you request access, a founder reads the request, 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.