Event-Driven Billing Infrastructure: Algorithmic Pricing Engines, Stream-Based Metering, and Revenue Analytics for Usage-Based Payment Platforms

Abstract

Usage-based billing presents a class of engineering problems that flat-rate invoicing tools were never designed to solve. Payment platforms serving AI companies, IoT operators, and cloud infrastructure businesses require a continuous, low-latency pipeline from raw consumption events through metered aggregation, algorithmic pricing, invoice generation, and revenue analytics, each layer independently scalable, fully auditable, and idempotency-guaranteed at event-stream scale. This paper presents the design rationale and component architecture of a production-grade event-driven billing infrastructure: a stream-based metering engine built on Apache Kafka and ClickHouse columnar aggregation; a composable pricing layer supporting flat, per-unit, tiered, progressive, volume, and counter-based models; an automated dunning and invoice pipeline; and a revenue analytics layer with ML-based forecasting and statistical anomaly detection. Config-driven plan versioning and per-tenant charge overrides enable pricing evolution without code deployment. The paper offers a generalisable reference architecture for usage-based billing infrastructure in high-volume financial technology platforms, with emphasis on the separation-of-concerns decisions that distinguish maintainable billing systems from their monolithic, brittle counterparts.