FinOps for teams running production AI.

FinOps LLM is focused on one problem: making LLM and GenAI spend attributable, governable, and optimizable without slowing engineering teams down.

The product direction combines provider invoice reconciliation, token-level attribution, anomaly detection, model routing, semantic caching, prompt caching, and chargeback/showback workflows.

How we work

• Read-only billing and usage access by default.
• Optimization targets set after a real provider-invoice baseline.
• Quality and cost changes measured together, not separately.

Contact: hello@finopsllm.com

What we actually do

An engagement starts with a baseline, not a recommendation. We reconcile the provider invoices against usage exports for a full billing period, so every later claim of savings is measured against a number both sides already agreed on. Optimization proposals that arrive before that baseline exists are guesses — the invoice is the only ledger that settles the argument.

From there the work is ordinary and unglamorous: attribute spend to the teams and features that caused it, find the workloads whose cost per successful task is out of line with their value, and change the cheapest things first. Prompt caching and batch routing usually come before model substitution, because they do not change what the user sees and therefore do not need a quality trial to justify.

What we won't do

• Quote a savings percentage before the baseline. Published numbers from vendor case studies rarely survive contact with a different traffic mix.
• Read prompts or outputs by default. Baseline attribution needs billing and usage metadata, not content. Content review happens only when a named optimization requires it and the customer approves it.
• Ship a change without a quality measurement. A cost reduction that quietly degrades answers is a product regression wearing a finance costume.

Who this is for

Teams past the experiment stage — production LLM traffic, a bill large enough that someone in finance has started asking about it, and enough engineering ownership that the answer can actually change. If AI spend is still on a single company card and nobody is being asked to explain it, the honest advice is to wait and set up attribution first, which is the one thing worth building before you need it.