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A tenant-metered API is an accounting system with an HTTP front end. To bill or cap support tickets, emails, and meetings per customer, you need five separate parts: a tenant identity taken from trusted authentication, a durable ledger of billable events, a real-time quota check run by your own service, billing aggregation that turns the ledger into per-period totals, and operational telemetry plus request throttling. Each part has a different source of truth, latency, and failure mode. Most metering bugs happen when one part stands in for another, such as treating a request counter as an invoice or a dashboard total as the ledger.
This guide follows the build in the order you need it. It uses Stripe’s usage-based billing and Microsoft Graph’s metered API model as reference points. The product decisions, such as what counts as one billable ticket, belong to your own contract. The sections below show where each decision goes in the system.
Start with the tenant boundary
Every usage record is only as reliable as the tenant it is attached to. Resolve the tenant from the authenticated credential: a workspace or organization claim in a verified token, or an API key bound to a tenant record on your side. Do not accept a tenant ID in the request body or query string as the authority for who is calling. A client can send any tenant ID it likes, but it cannot produce a valid signature on your token.
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Then enforce that tenant scope in every read and write path: the ticket store, the email log, the meeting records, the usage ledger, and the summary tables. AWS treats tenant isolation as a core design concern for SaaS in its Tenant Isolation guidance in the SaaS Lens. Microsoft’s metered API setup shows the same attribution principle: usage is tied to the calling tenant, so costs can be split by that tenant (see Enable metered APIs and services in Microsoft Graph).
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Define billable events before writing code
Metering starts with a vocabulary. Name each billable action as an explicit, versioned event type with one unit. The examples used here are ticket.created, email.sent, and meeting.completed. Keep tickets, emails, and meetings as separate event types rather than one generic API-call counter, so you can price, cap, and report each one on its own.
The names are the easy part. The moment that makes an action billable is a commercial decision. Neither Stripe’s nor Microsoft’s reference defines units for your product, so the table below lists the questions your pricing and API documentation must answer.
| Event type | Candidate billable moment | Unit options | Decision to record |
|---|---|---|---|
ticket.created |
Ticket accepted and stored by the API | 1 ticket | Whether bulk imports and automation-created tickets count |
email.sent |
Message accepted by your send pipeline, or confirmed delivered | 1 message, or 1 per recipient | Whether a send that is retried after a failure is billed once or each time it is attempted |
meeting.completed |
Meeting ends and its record is finalized | 1 meeting, or billed minutes | Whether cancelled or never-started meetings count, and how partial minutes are rounded |
Record a durable usage event
Write a usage event to your own store before anything calls a billing provider. Each record should carry:
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- the event type and schema version
- the quantity and unit
- the event time, meaning when the billable action happened, stored in UTC
- a reference to the source object, such as the ticket, message, or meeting ID
- a globally unique idempotency key
- the ingestion time and the export status for the billing provider
An illustrative record for one ticket looks like this:
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{
"tenant_id": "ten_8f21c",
"event_type": "ticket.created",
"schema_version": 2,
"quantity": 1,
"unit": "ticket",
"occurred_at": "2026-10-09T14:32:05Z",
"source_ref": "ticket:tk_550193",
"idempotency_key": "ticket:tk_550193:created",
"export_status": "pending"
}
Keep the ledger append-only. A correction is a new row that references the original event, never an edit or delete of the original. This gives you the audit history you need for duplicate investigations, late arrivals, and customer disputes. Stripe’s metering model establishes event-based usage and aggregation, but it does not dictate your internal schema; the design above is yours to adapt.
Make retries and corrections safe
Timeouts cause clients and background workers to resend requests. Make the write idempotent by placing a unique constraint on the idempotency key. A second insert with the same key should return the existing record rather than create a second billable row.
Handle the other cases explicitly:
- Late events arrive after their billing window has closed. Decide in advance whether they roll into the next open window or go to a manual review queue, and write that rule into the contract.
- Invalid events fail validation at write time. Return a validation error to the caller and store the rejection with its reason, so a later dispute can be traced.
- Corrections are compensating events that reference the original. Stripe documents meter event adjustments for correcting events and identifiers for meter events in its API reference. Confirm the current identifier rules and adjustment behavior there before you implement, because provider constraints can change.
Separate quota enforcement from invoicing
A quota check answers “may this tenant do this now?” and must be answered inside the request. An invoice answers “what does this tenant owe for the period?” and can be computed later. Keep these on different paths.
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For a hard cap, the request path should look like this:
- Authenticate the caller and resolve the tenant as described above.
- Atomically reserve one unit against the tenant’s counter for the current window. Use a conditional update that succeeds only while usage is below the limit, for example
UPDATE tenant_window SET used = used + 1 WHERE tenant_id = ? AND window_id = ? AND used < limit. If no row is updated, return a quota error and stop. - Perform the work and write the usage event in the same database transaction as the created object, or in an outbox row written in that transaction.
- If the work fails before commit, release the reserved unit so the counter does not drift upward.
A soft limit uses the same counter but returns a warning in the response instead of rejecting the request.
Aggregate by tenant, event type, and billing window
Stripe’s model uses meters to aggregate events over billing periods, and it associates meters with prices. That gives a common shape: the ledger holds the events, a summary per tenant and period is derived from them, and a price converts the summary into an invoice line. You can build the same structure in your own system. Keep a summary table keyed by tenant, event type, and window, and rebuild it from the ledger whenever you need to verify it.
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| Decision | Why it changes the total |
|---|---|
| Billing timezone and period boundaries | An event at 23:30 in one timezone can fall into the next month in another |
| Rounding rules, such as for meeting minutes | Partial units change the billed quantity |
| Included allowance per period | Determines the point at which overage begins |
| Overage policy: hard cap, soft warning, or metered overage | Determines whether requests are rejected or billed beyond the allowance |
| Treatment of refunds and corrections | Determines whether a summary is restated or a credit line appears on the invoice |
Choose a billing provider model
Stripe usage-based billing
Stripe records usage through meter events and aggregates them with meters for billing. Its support guide describes the same pattern for a metered subscription: record each customer’s usage across the period and bill at the end. Its example is an email SaaS business that charges for API calls (see Charge customers different amounts each billing period). In this model, your tenant maps to a billing customer, and your ledger remains the record of what happened.
Microsoft Graph metered APIs
Microsoft’s model is platform-billed. The consuming application is associated with an Azure subscription, and monitored costs can be split by application, by calling tenant, or by meter. This is useful when your API is consumed through Microsoft’s own billing. If you invoice your own customers, you still need your own tenant-to-customer mapping and ledger.
| Comparison axis | Stripe usage-based billing | Microsoft Graph metered APIs |
|---|---|---|
| Billing customer of record | Your billing customer, mapped from your tenant | The Azure subscription associated with the consuming application |
| Who bills | Your business, through Stripe Billing | Platform-billed through the Azure subscription |
| Event ingestion path | Meter events sent through the API | Not stated in the linked setup guidance |
| Aggregation timing | Asynchronous processing; eventually consistent summaries | Not stated in the linked setup guidance |
| Corrections and duplicate handling | Meter event adjustments; event identifiers in the API reference | Not stated in the linked setup guidance |
| Tenant-to-customer mapping | Explicit mapping you maintain | Calling tenant is the attribution key for cost splits |
| Export and reconciliation | Not stated in the linked Stripe pages | Not stated in the linked setup guidance |
| Pricing-model flexibility | Meters associated with prices | Not stated in the linked setup guidance |
| Regional availability | Not stated in the linked Stripe pages | Not stated in the linked setup guidance |
| Lock-in | Billing state and invoices live in Stripe; keep the usage ledger in your own store so it can move | Costs flow through Azure billing; keep your tenant ledger independent |
Verify limits and semantics in the chosen provider’s official documentation before launch, because these details change over time.
Use throttling as a separate control
API Gateway request throttling protects throughput and backend capacity. AWS documents it in its guide to throttling requests to REST APIs. It limits how many requests a client can send. It does not decide how many billable tickets, emails, or meetings those requests produced.
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| Control | Question it answers | Unit | Enforced by | Customer-facing result |
|---|---|---|---|---|
| Request throttling | How fast can this client send requests? | HTTP requests | API gateway | Requests rejected with a throttling error (a 429 response is typical for API Gateway) |
| Usage quota | How many billable units has this tenant used in the current window? | Tickets, emails, meetings | Your service, against your ledger | A quota error that names the event type and the limit |
Consider two cases. One request that imports 200 tickets is one request to the gateway and 200 ticket units in your ledger. Fifty requests that only read tickets create no billable units at all. Apply both controls where they fit, and give each its own error code so support staff and customers can tell them apart.
Instrument operations without making metrics the ledger
OpenTelemetry’s Metrics API defines counters for additive values and histograms for distributions such as request duration or response payload size (see the OpenTelemetry Metrics API specification). Use counters to watch usage rates and detect sudden spikes. Use histograms to watch latency and payload size. Add tenant as a metric attribute only where you can control the number of distinct tenants that appear, since each distinct attribute value creates a separate series.
Sampled, aggregated, or interval-exported telemetry can drop or double-count values. Use it for alerts and dashboards, and tie invoices and dispute responses to the ledger.
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Quick Recap
Reconcile the ledger to billing
- Export events from the ledger in batches by billing window, carrying each event’s idempotency key.
- Record each provider acknowledgement or failure against the event row. Retry failures with the same idempotency key.
- Compare internal totals with the provider’s aggregate for each tenant, event type, and window. Investigate every difference before invoices are finalized.
- Apply corrections as new compensating events, then run the comparison again.
- Keep the billing customer mapping explicit. Test what happens when a customer moves a workspace to another tenant, and decide how historical events follow that move.
- Give support staff a usage summary view and an adjustment workflow that records who approved each adjustment and why.
When your totals do not match
- Your ledger has more events than the provider. Check whether an export batch failed without being retried, and whether validation rejected the event. The stored rejection reason shows which.
- The provider has more events than your ledger. Look for writes that bypass the ledger path, such as a background job that creates billable objects directly, or an export retry sent without the original idempotency key.
- Totals match per tenant but differ per window. Check the timezone and period boundaries used during export against the contract definition.
- Totals differ only for the most recent window. Compare after the provider’s summary has settled, because aggregation is asynchronous and eventually consistent.
Decisions still open for your deployment
- Data retention for usage events and rejected events, and how long customers can dispute a charge.
- Privacy and jurisdiction requirements, including where tenant usage records may be stored.
- Volume and latency targets, which determine the database, queue, and partitioning strategy.
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