AI FinOps: Why Request-Level Cost Attribution Matters for LLM Spending

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Global enterprise AI spending is projected to reach $2.59 trillion this year, yet many organizations struggle to show measurable returns on it. Autonomous agents have made flat-rate IT budgeting obsolete, since a single user request can trigger chained model calls costing anywhere from a fraction of a cent to several dollars. The article argues that AI FinOps, which tracks per-token LLM costs, must attribute spending to specific business requests to prevent budget overruns. It cites Gartner's forecast that 70% of organizations will face cloud shock from untracked LLM API calls, and Datadog telemetry showing 5% of users generate over 60% of spend. The author recommends tagging requests with correlation IDs at the API gateway and emitting OpenTelemetry spans with token metadata.

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