Usage Metering Explained: Architecture and Pipelines

Usage metering is a critical process that tracks product consumption data, transforming it into structured metrics for billing and enforcement, but it faces challenges under AI workloads due to high event volumes and complex workflows. The metering pipeline consists of ingestion, metering, and rating stages, which capture telemetry, aggregate it into billable metrics, and apply pricing rules, respectively. Real-time usage metering is essential for AI products, as it processes events immediately to enforce limits and prevent overage before usage completes, unlike batch metering which reconciles billing post-usage. AI systems complicate metering with granular and variable token consumption, direct cost impacts tied to infrastructure, and multi-step workflows requiring detailed usage tracking. In-house metering systems often struggle with concurrent usage, state consistency, and accurate attribution under load, necessitating robust infrastructure for real-time enforcement and billing. Stigg offers a solution with its usage runtime for AI products, featuring a Sidecar deployment, local Redis cache, and real-time metering to maintain fast, reliable usage decisions and billing integration without dependencies on external systems.

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