UsageBox meters tokens, GPU minutes, agent runtime, and tool calls per customer. Open-source storage engine. Connects to Stripe or your own invoicing.
Stripe Billing, Chargebee, Recurly: built for SaaS subscriptions in the 2010s. AI usage is a different shape of problem.
Stripe's metered usage assumes thousands of events per customer per month. AI products send millions per day. Generic tools throttle, fail silently, or charge you per-event.
An agent run is N tool calls + M LLM calls + K memory ops. Each costs different amounts. Generic billing tools can't roll those into a single billable unit without engineering work you keep redoing.
A user can manipulate prompts to trigger expensive generations. Without per-user spend ceilings and real-time anomaly detection, the first sign of an attack is your AWS or OpenAI invoice next month.
Six primitives. Each one designed for the AI billing patterns generic tools fight you on.
Meter LLM input + output tokens per request, per agent, per tenant. Idempotent ingestion handles retries without double-billing.
Bill inference time, fine-tuning runtime, or per-job GPU usage. Catalog-driven pricing rules; no code changes to update rates.
Track cost for each agent run across N tool calls + M LLM calls + memory ops. Roll up to tenant invoices automatically.
Cost-amplification attacks land in your usage data first. Per-user spend ceilings, alerting on token surges, kill-switches.
Raw events feed hourly aggregates. Invoice generation is O(1) per account, not a scan over millions of rows.
Plug into Stripe for self-serve. Or generate finance-ready invoices for enterprise contracts. Same metering pipeline.
usagedb is the Rust storage engine UsageBox runs on. Append-only, idempotent, immutable raw event audit trail, hourly rollups for invoice queries. Apache 2.0 on GitHub.
Read the code that produces every invoice line. Fork it. Self-host the ingestion layer while still using UsageBox for the platform side. The right answer to “is your billing math correct” is “read the code yourself.”
Or read the architecture overview in our usagedb article, then go deep with the 10-part engine internals series: ingest, dedupe, columnar segments, rollups, the query engine, and how it is tested.
Practical writing on metering patterns, AI cost attribution, and what we learn from production billing systems.
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