FinOps for LLMs — Unit Economics and Multi-Tenant Attribution
Traditional FinOps breaks on LLM spend. Costs are token-transactions, not resource-uptime. Tags don't map — an API call is a transaction, not an asset. Engineering decisions (prompt design, context window, output length) are financial decisions. The 2026 playbook has three attribution dimensions to instrument on day one: per-user (userid) for seat pricing and expansion, per-task (taskid + route) for product surface cost and prioritization, per-tenant (tenantid) for unit economics and renewal. Four token layers — prompt, tool, memory, response — one bucket hides spend. Enforcement ladder for multi-tenant products: rate limits per tenant (2-3x expected peak, clear 429 + retry-after); daily spend cap (1.5-3x contracted ceiling; triggers rate tightening + alert); kill switches on spend z-score > 4 (auto-pause + page on-call). Attribution patterns: tag-and-aggregate, telemetry-joiner (trace-ID → billing; highest accuracy), sampling-and-extrapolation, model-based allocation, event-sourced, real-time streaming. Unit metric: cost per resolved query, cost per generated artifact — not $/M tokens. Retroactive tagging always misses; instrument at request creation. Explain why traditional FinOps (tags + tiers) breaks on LLM spend and name the three new attribution dimensions. Enumerate the four token layers (prompt, tool, memory, response) and why single-bucket billing hides cost. Design an enforcement ladder (rate → spend cap → kill switch) for a multi-tenant product. Pick a unit metric (cost per resolved query / artifact) instead of $/M…
FinOps for LLMs — Unit Economics and Multi-Tenant Attribution: Traditional FinOps breaks on LLM spend. Costs are token-transactions, not resource-uptime. Tags…
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