Metered Billing for LLM Inference: A Reference Architecture
A pragmatic reference architecture for metering LLM token consumption and billing per inference at scale.
Large language models turned inference into a metered utility. Every completion, embedding, and agent step is a discrete unit of cost — and increasingly a unit of revenue. The companies winning at AI monetization treat tokens the way utilities treat kilowatt-hours: measured, priced, and reconciled in real time.
A sound LLM billing architecture has three layers. First, an ingestion edge that reports usage the moment a request returns, tagged with customer, agent, and model. Second, a pricing layer that maps metrics to tiers, prepaid wallets, and hybrid plans. Third, an enforcement layer that checks spend caps before the next request is allowed to run.
Orvlin collapses these into one API. Report tokens with the request correlation id, and the engine handles aggregation, wallet burn-down, and per-agent caps with sub-10ms checks. Finance gets a single ledger; product gets a live P&L per customer.
The payoff is pricing freedom: per-token, per-agent, per-feature, or any combination — configured in an afternoon, not a migration. That is what lets AI companies ship new models and new price points as fast as they ship code.
Frequently asked questions
How do I bill per LLM inference?
Report token counts (and model) to a metering API per request, map them to a price in your pricing layer, and invoice from the same ledger. Orvlin does this with one ingestion call.
How do I stop runaway AI costs?
Enforce per-customer and per-agent spend caps at request time, before tokens are consumed, using prepaid wallets and hard limits.