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Cost attribution & AI FinOps
73–79% of enterprises blew their AI budgets in 2026, and the most common root cause is that spend shows up as one opaque line item (OpenAI, Anthropic, Bedrock) with no mapping to features, teams, or customers. You cannot cut what you cannot see.
Per-request metadata tagging (feature, team, customer, environment), unit-economics dashboards (cost per task, per user, per feature), and budget alerts turn the bill from a surprise into a managed system — and tell you exactly which of the methods on this site to apply where.
How to do it
- Tag every LLM call with feature/team/customer metadata via your gateway or logging layer.
- Build one dashboard: spend by feature, tokens per task, cost per active user.
- Set budget alerts at the feature level, not just the org level.
- Review weekly; feed the top spender into the tactics on this site.
Frequently asked questions
Do I need a FinOps team for this?
No — 98% of FinOps teams now track AI spend, but for most companies a gateway with metadata tagging plus one dashboard is enough to find the 20% of features driving 80% of the bill.
Tools for this method
LiteLLM
The default self-hosted gateway: one OpenAI-compatible proxy across 100+ providers with budgets, caching, routing, fallbacks, and …
Portkey
Managed gateway with the strongest built-in semantic caching, plus guardrails, routing, and cost analytics. The low-ops route to t…
Helicone
Long the default open-source LLM cost dashboard. Acquired by Mintlify in March 2026 and now maintenance-only — existing deployment…
nOps
Cloud-FinOps platform that extended into AI spend (Bedrock, Azure OpenAI, GPU fleets). The fit when your AI bill is one slice of a…
Next method: #9 Cheaper & open models / self-hosting