Built for AI products

Track AI usage.
Bill it accurately.
Without rebuilding your stack.

UsageBox meters tokens, GPU minutes, agent runtime, and tool calls per customer. Open-source storage engine. Connects to Stripe or your own invoicing.

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Idempotent
Ingestion
Immutable
Audit trail
Open source
Storage engine
POST /v1/events
curl -X POST https://api.usagebox.com/v1/events \
-H "Authorization: Bearer $UBX_KEY" \
-d '{
"event_id": "req_8x42jk",
"account_id": "acme-co",
"meter": "llm_tokens_in",
"model": "claude-4.5-sonnet",
"quantity": 12450,
"timestamp": "2026-05-16T18:42:11Z"
}'
 
# Idempotent. Retries are safe.
# Rolls up hourly. Invoiced via Stripe.

Why generic billing breaks on AI workloads

Stripe Billing, Chargebee, Recurly: built for SaaS subscriptions in the 2010s. AI usage is a different shape of problem.

Volume breaks generic billing

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.

Attribution needs a graph

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.

Attacks show up in the bill

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.

Built for the way AI products actually meter

Six primitives. Each one designed for the AI billing patterns generic tools fight you on.

Token Metering

Meter LLM input + output tokens per request, per agent, per tenant. Idempotent ingestion handles retries without double-billing.

GPU Minute Pricing

Bill inference time, fine-tuning runtime, or per-job GPU usage. Catalog-driven pricing rules; no code changes to update rates.

Per-Agent Cost Attribution

Track cost for each agent run across N tool calls + M LLM calls + memory ops. Roll up to tenant invoices automatically.

Real-Time Anomaly Detection

Cost-amplification attacks land in your usage data first. Per-user spend ceilings, alerting on token surges, kill-switches.

Hourly Rollups

Raw events feed hourly aggregates. Invoice generation is O(1) per account, not a scan over millions of rows.

Stripe + Manual Invoicing

Plug into Stripe for self-serve. Or generate finance-ready invoices for enterprise contracts. Same metering pipeline.

The storage engine is open source

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.”

pbudzik/usagedb

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.

Notes on AI billing

Practical writing on metering patterns, AI cost attribution, and what we learn from production billing systems.

Browse all articles →

June 16, 2026 · 7 min read

UsageBox Kata #1: From Token Event to Invoice Line in 30 Minutes

A hands-on kata: take a raw AI usage event - a chunk of Claude tokens, a tool call, a credit burn - and turn it into a stable, auditable invoice line using UsageBox, in about 30 minutes, without building a billing database. Six steps against the real metering API: send your first usage event; make retries safe (idempotent dedupe by event_id, with same-id-different-payload surfaced as a conflict); read a cheap month-to-date total from rollups; pull the immutable audit trail behind a disputed line with /explain; close the period to freeze the invoice while corrections land as net adjustments; and run a raw-vs-rollup /verify so the fast number always equals the true number. Plus production notes, kata variations (per-model cost, live spend caps, vendor-bill reconciliation, ad-hoc SQL), and what you just avoided building.

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June 16, 2026 · 7 min read

UsageBox Kata #2: Live Spend Caps and Real-Time Usage

Catch and cap AI spend before the bill lands. A hands-on kata against the real metering API: read an account month-to-date total fast from rollups, understand why the open current hour falls back to raw so the live number is both fast and current, compute headroom against a budget, run a real-time burn-rate check, and act at the threshold - soft caps that alert and hard caps your app enforces (the meter measures, your app gates). Plus per-meter caps with group_by, production notes, variations (Slack alerts, per-model caps, prepaid-credit countdowns), and FAQ.

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June 16, 2026 · 8 min read

UsageBox Kata #3: Reconcile a Vendor Bill Against Your Meter

Close the gap between what a model vendor like Anthropic or OpenAI bills you and what you metered and charged customers. A hands-on kata: pull your per-model total, verify your own rollups against a raw scan before you blame anyone, lay your number next to the vendor invoice, localize the gap with /explain and dimensions, separate a metering gap from unbilled pass-through overhead (retries, cached reads, system-prompt tokens), and close the loop with Correction events so the audit trail proves the reconciliation. Production notes, variations (daily reconciliation, per-customer margin), and FAQ.

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