An AI application can generate charges across cloud infrastructure, model APIs, software licences, labor, and contracts. The usage record may be in a different system. Bringing the two together helps explain what is being consumed and what it costs.
What counts as AI cost?
A complete AI cost view may include cloud and GPU compute, input and output tokens, model API charges, AI SaaS licences, internal labor and data preparation, contractors, vendors, and commitments. Source coverage depends on your integrations and the records loaded in Yarken.
What counts as AI usage?
Common consumption measures include tokens, requests, calls, GPU hours, active seats, application usage, and workload activity. Different sources provide different measures; do not compare usage metrics unless their definitions match.
Connect cost, usage, and ownership
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Identify the AI source and confirm that its cost or usage records are available.
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Match records to the model, application, team, initiative, or other available owner.
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Choose a consistent reporting period and currency.
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Review cost and usage together to identify changes, missing attribution, and unusual consumption.
For analysis, use Getting Started with Analytics. For cloud billing and ownership, see FinOps. For natural-language investigations, see Ask Yarken.
Check the result
Confirm that the reporting period, currency, source records, usage units, and assigned owners agree. An incomplete cost base can understate an AI workload's true cost.
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