Usage metering

Metering records billable and limitable events as they happen: an API request, a document processed, a minute of voice, a thousand LLM tokens. Each event is attributed to a tenant, timestamped, de-duplicated and aggregated into the units that plans and invoices use. The pipeline usually runs through a queue or event stream so that counting never slows the product, with periodic reconciliation against source systems to catch drift.

AI features made metering essential rather than nice to have. Inference cost varies per request and per model, so a flat seat price can quietly lose money on a heavy user. Metering token usage and model choice per account lets you set fair limits, price AI tiers sensibly and see which customers are profitable. The same data feeds in-product usage dashboards and alerts before a customer hits a cap.

Metering is the measurement; subscription billing is the invoice. Product analytics is a third thing again: it tracks behaviour for insight and tolerates sampling, whereas metering must be exact because money depends on it.

• CFO: metering is the only way to know gross margin per customer once AI inference is part of the cost base.
• Product manager: usage data shows which features drive value and where plan limits should sit.
• CTO / Head of Engineering: a metering pipeline must be exact, idempotent and reconcilable, which is harder than it looks.
• Support manager: customers dispute bills; an itemised usage record settles the conversation.

Metering exists because products increasingly charge for what is used rather than who is logged in, and because AI features carry a real per-request cost that seat pricing cannot absorb. Exact counts protect margin, make fair limits possible and give customers visibility. The trade-off is a pipeline that must never lose or double-count an event, plus reconciliation work every billing cycle. Approximate counts are fine for analytics and unacceptable for invoices, so metering is built to a higher standard.

• A SaaS product metering LLM tokens per tenant to price an AI add-on and cap free-tier usage.
• A document intelligence platform for NBFCs billing per KYC document processed, with per-page reconciliation.
• A voice agent platform tracking call minutes per customer for per-minute pricing.
• An education platform counting active learners per month for institutional invoicing.
• A logistics API metering delivery lookups per partner to enforce plan limits and detect abuse.

Technique / practiceAn engineering pattern for event capture, aggregation and reconciliation rather than one specific tool, though billing platforms provide parts of it. Eazyware builds metering pipelines for SaaS and AI products under the SaaS development service.

Eazyware service that covers it: SaaS / Cloud-native Application Development. Starting prices are on the pricing page.