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TokenOps Cost Auditor

TokenOps Cost Auditor turns your AI spend into a dollar-ranked waste audit — and then keeps watching. Connect your OpenAI or Anthropic account with one read-only key: the platform pulls your usage daily, audits it weekly with a deterministic rules engine (no AI reads your data), emails a daily spend digest, alerts you between audits, and mails your verified Savings Statement monthly. Prefer not to connect? Upload a log export and get the same audit on that file. No SDK, no proxy, nothing in your request path.

The problem, in your terms

You run engineering. Your OpenAI or Anthropic invoice grew faster than usage did, and nobody on the team can say which line items are waste versus load. 79% of enterprises overran their AI budgets last year (DoiT/Sapio Research, 2026, survey of 500 finance leaders at 1,000+-employee US/UK organizations) — and even mature FinOps teams in that survey overspent by 31% on average. 98% of FinOps teams now manage AI spend (State of FinOps, 2026), but the tooling they use needs SDK or proxy integration before it shows you anything.

Agent fleets make this worse. Coding agents and automation pipelines re-send the same context hundreds of times, retry silently, and never clean up their prompts. Routers pick a model; we find the other five kinds of waste — and prove it in dollars.

What you get

  • A savings waterfall: every finding ranked by estimated monthly dollar impact.
  • A daily spend digest — yesterday per source, month-to-date, and your budget line — plus alerts that watch between audits (paid plans).
  • Six waste classes checked: oversized models, missing prompt caching, prompt bloat, retry storms, unbounded max_tokens, and chatty agent loops.
  • Evidence rows for every finding — token counts and timestamps, never your text.
  • Methodology and pricing provenance printed inside the report itself.

First page of a sample audit report: executive summary with estimated monthly savings, spend cards, and the savings waterfall

The sample above is our engineered test fixture — the same synthetic traffic our golden tests pin to hand-derived dollar figures — not customer data.

What we never do

Your data policy, verbatim from our landing page and privacy policy: uploaded logs are "analyzed then deleted; nothing retained beyond 7 days; never used for training."

In plain terms:

  • Raw uploads are automatically purged 7 days after your report is generated, and every purge is written to an append-only audit log.
  • No prompt or completion text is ever written to our database. The engine stores token counts, aggregates, and hashes only — enforced by automated tests, not just policy.
  • No AI reads your logs. The analysis engine contains zero LLM calls, so it cannot hallucinate a number.

Where to start