Usage metering and rating engine with data transformation, commitments, credits, and shadow-billing simulation.
m3ter is a metering and rating platform that sits between a product's raw usage data and whatever issues the invoice: it ingests events, transforms and aggregates them into billable measures, rates them against pricing plans — including commitments, prepaid credits, and tiered schemes — and feeds the rated output to existing billing, CPQ, and ERP systems. It is built for B2B software companies adopting usage-based pricing on top of an established quote-to-cash stack, rather than replacing that stack. RevOps, finance, and engineering share it: engineering wires the events, pricing teams change plans without code.
Which of the capability map's modules m3ter covers — each links to the module's own page, with every tool that supports it.
| Module | Phase | Depth | Note |
|---|---|---|---|
| Fulfill & Bill | |||
| Usage Event Ingestion (API) | Consume & Meter | Core | |
| Aggregation & Rollups | Consume & Meter | Core | Transforms raw events into billable measures and rollups. |
| Mediation Engine | Consume & Meter | Core | Data transformation layer normalizing usage from multiple sources. |
| Rating Engine | Rate & Bill | Core | Rates usage against plans including commitments, tiers, and credits. |
| Wallet / Credit Drawdown | Consume & Meter | Supported | Prepaid commitments and credit balances drawn down by usage. |
| Billing Simulation / Dry Run | Rate & Bill | Supported | Shadow-rates real usage against candidate pricing before launch. |
Scored against UsagePricing's Usage-based billing & metering rubric v1.0 (0 weak · 1 adequate · 2 strong), assessed July 2026. Requirements we couldn't verify from public material stay unscored — never guessed. Read the method.
| Requirement | Score | Why |
|---|---|---|
| Real-time balances & drawdown Can a customer (and your product) see an accurate credit or spend balance mid-period? | 2 · Strong | Balances, prepayments, and commitment drawdown are core objects with API access. |
| Correction & re-rating When a meter was wrong, can you fix history without hand-editing invoices? | 1 · Adequate | Recalculation and backdated corrections are supported within operational bounds. |
| Commits, credits & custom rate cards Can it express how enterprise AI deals are actually signed? | 2 · Strong | Commitments, prepay, and per-account pricing built for enterprise usage deals. |
| Billable-metric flexibility Can finance define a new meter without re-instrumenting the product? | 2 · Strong | Compound and derived meters defined declaratively over ingested measurements. |
| Invoice & proration correctness Do mid-cycle changes, consolidation, and multi-currency come out right? | 1 · Adequate | Rates and bill calculations feed existing billing/ERP systems, which own final invoice presentation. |
| Rev-rec & ERP handoff Can the numbers survive an audit once they leave the billing system? | 1 · Adequate | Positioned to feed NetSuite/ERP and billing systems; ledger drill-down lives downstream. |
| Ingestion scale & integrity Does the meter stay correct at production event volumes? | 2 · Strong | Purpose-built high-volume measurement ingestion with idempotent submission. |
| Price-change velocity How fast can you ship a pricing change safely? | 2 · Strong | Pricing modeled and tested against historical usage before rollout — what-if analysis is a headline feature. |
The compose-with-your-stack posture is the distinctive choice: m3ter deliberately slots into Salesforce, NetSuite, Stripe, and existing billing rather than demanding a rip-and-replace. Its billing-simulation capability — shadow-rating usage against hypothetical pricing before you launch it — turns pricing changes from a leap of faith into a modeled decision, which is rare even among modern usage platforms.
Platform fee, sales-quoted. Sized to usage data volume and contract scope.
No — that is its defining choice. m3ter meters and rates, then hands rated usage to the invoicing, CPQ, and ERP systems you already run. It competes with building a metering pipeline in house, not with your billing or payments vendors.
Running real usage through a candidate pricing model in parallel with production pricing, so you can see exactly what every customer would have paid before you ship the change. For usage-priced businesses it removes most of the guesswork — and customer-surprise risk — from repricing.
By overlap on the capability map — computed, not curated.