AI

The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/11/2026, 05:03:24 UTC.

Briefings

This article proposes the Two-Ledger Model to resolve a mismatch between FinOps billing data and AI transaction costs. The model combines a settlement ledger of FOCUS-conformant billing data with an attribution ledger of invocation-grain usage records. It uses OpenTelemetry GenAI telemetry and probabilistic matching to assign business transactions to AI invocations, enabling near real-time pricing and reconciliation. A simulation with known ground truth demonstrates the model can sustain cost coverage above 99% while recovering unmetered spend within 0.24 percentage points and cutting p99 transaction costs by up to 61%.

Key points

• The Two-Ledger Model formalizes a grain mismatch between FinOps billing data and AI transaction costs by combining a settlement ledger with an attribution ledger.
• The model assigns business transactions to AI invocations using OpenTelemetry GenAI telemetry and probabilistic matching to record method and confidence.

Context

Enterprises use multi-agent AI systems across clouds, LLMs, and systems of record, but providers do not see enterprise transaction boundaries. The FinOps Open Cost and Usage Specification (FOCUS) provides billing data with one schema but remains settlement-grain, creating a need for a new model to bridge this gap.

Limitations

The evidence is limited to a single preprint (arXiv:2611.06989v1) without peer review. The simulation results are based on known ground truth and may not reflect real-world conditions. The model's performance depends on trace propagation and probabilistic matching accuracy, which are not empirically validated in the source.

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