# Cost attribution

**Source:** https://promtable.com/glossary/cost-attribution

> Cost attribution is the FinOps discipline of tracking LLM spend per-user, per-feature, per-tenant, per-model — the foundation for unit economics, abuse detection, and pricing decisions in 2026 AI products.

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Cost attribution is the FinOps discipline of tracking LLM spend per-user, per-feature, per-tenant, per-model — the foundation for unit economics, abuse detection, and pricing decisions in 2026 AI products.

LLM bills can spike unpredictably: one prompt-injected agent burns $1K in an hour, one viral feature 10×s the bill. Cost attribution tags every LLM call with metadata (user_id, tenant_id, feature, model, session) so the platform can answer: which features cost the most, which users are unprofitable, which tenants need rate-limiting, which models give the best $-per-quality. Implementations: Langfuse / Helicone / Braintrust capture cost metadata; custom proxies (LiteLLM, Portkey, OpenRouter) inject tags; OpenTelemetry attributes. Standard reports: cost per active user, cost per feature, cost by model, top-N spenders, week-over-week deltas. Without cost attribution, AI features hit unit-economics walls when scale arrives.

## When to use

- Any AI product with non-trivial LLM spend.
- Multi-tenant SaaS — needed for accurate billing.

## Common mistakes

- Aggregating at the API key level only — misses per-feature breakdowns.
- Skipping output token attribution — output dominates cost on Claude / GPT-4-tier models.

## Related terms

- [llm-observability](https://promtable.com/glossary/llm-observability)
- [rate-limit](https://promtable.com/glossary/rate-limit)
- [model-router](https://promtable.com/glossary/model-router)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/cost-attribution
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