AI Cost Attribution & Real-Time Outage Monitoring from Shared Provider Spend
Restores per-application cost visibility from a single shared AI invoice. A near-zero-touch SDK captures durable usage “receipts”, and invoice-anchored reconciliation turns one opaque bill — “$100 Gemini” — into a defensible split: “$84 image generation, $16 chatbot”. The same SDK now adds a real-time outage-monitoring path, turning silent, log-only failures — in a dependency or the app itself — into instant, structured incident alerts.
Organizations run a fleet of applications against a few shared AI provider accounts (OpenAI, Gemini, Grok). It is operationally convenient but financially opaque: the provider issues one consolidated invoice with no breakdown of which application, team or feature drove the spend — and as AI scales from experiment to core infrastructure, that blind spot becomes a real governance and budgeting problem.
Per-application cost vs. real invoice
One opaque bill
Data loss under any component failure
Silent dependency & app failures
Log-only, unnoticed
Integrity, resilience, accuracy & outage suite
An accuracy-first platform that treats the provider as a passive source of truth: it captures a durable “receipt” for every AI call to establish each application’s share of usage, then anchors those proportions to the provider’s actual invoice — so every per-application dollar always sums to the real bill, regardless of estimation error.
Central insight — proportions × anchored total: trust captured events only for relative share, then scale to the invoice the org actually pays, so numbers reconcile and stay defensible to finance.
A durable local outbox plus at-least-once, idempotent ingestion yields an exactly-once effect — no loss, no double-count, even under repeated failure.
Universal duck-typed capture handles chat, embeddings, image, audio and streaming across OpenAI, Gemini and Grok — including custom or wrapped response shapes.
Versioned, effective-dated pricing auto-syncs from community feeds, so historical events stay correctly priced and stale-price drift is surfaced automatically.
A separate, real-time-first telemetry path auto-detects external dependency outages and internal application errors, raising immediate structured alerts (with a durable fallback so none are lost) plus a dashboard of outages by app, service, status code and type.
SDK reads usage receipts
Durable local write
At-least-once, idempotent
Versioned, effective-dated
Anchor to real invoice
Cost + outage dashboard
Turns a single shared AI invoice into a defensible per-application, per-environment and per-model breakdown — the basis for chargeback, showback and budget governance.
Independently audited “kill any component” resilience: an outage or crash of any part costs only data freshness, never accuracy.
Surfaces silent, log-only failures — a failing dependency or the app itself — as real-time, de-duplicated incident alerts with an audit trail, from the same integration.
Runs at a modest footprint — no message broker, embedded SQLite by default — with a documented, dialect-compatible PostgreSQL scale-out path.
A robot-arm benchmark found GPT-6 Astra refused 2 of 100 hazardous instructions. The same gap applies to any LLM agent with write access to your data.
Why LLM features fail in production when the demo worked: messy real inputs, queues that starve each other, and failures that never raise an alarm.
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