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A tenant metered API works when five separate parts agree with each other: the authenticated tenant, the durable record of each billable action, the quota decision made during the request, the billing totals built later from those records, and the operational telemetry that tells you the system is healthy. Keep these as distinct layers. Authenticate the caller, write an idempotent usage event for every billable ticket, email, or meeting, check quotas against your own tenant state before doing the work, and aggregate the events for billing. Throttling and metrics sit beside that ledger. They cannot replace it.
The five layers and what each one owns
Most metering systems fail because one counter is asked to do five jobs. A rate limiter counts requests, a dashboard counts events it happened to sample, and an invoice total is computed from a summary that may lag behind. Separating the layers makes each number explainable.
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| Layer | Question it answers | Source of truth | Timing |
|---|---|---|---|
| Tenant identity and authorization | Who is calling, and which tenant do they act for? | Trusted credentials and authorization context | Every request |
| Billable event ledger | What billable action happened, for which tenant, and in what quantity? | Your append-only usage event store | Written with the action |
| Quota decision | May this tenant perform this action now? | Your tenant-period usage state | Synchronous, in the request path |
| Billing aggregation | What does the tenant owe for the period? | Summaries derived from the ledger, sent to a billing provider if you use one | Asynchronous and periodic |
| Telemetry and throttling | Is the service healthy, and is any client overloading it? | Metrics and gateway request counters | Sampled or aggregated |
Step 1: Resolve the tenant from trusted authentication
Derive the tenant from the authenticated credential or the caller’s authorization context. Do not take the tenant from a tenant ID in the request body, query string, or path and trust it. You can validate a client-supplied tenant ID against the credential, but it should never be the thing that grants access. AWS’s SaaS Lens treats tenant isolation as a core design concern for multi-tenant systems, and the same reasoning applies to metering: a usage event recorded under the wrong tenant is both a billing error and an isolation failure.
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SELECT id, status FROM tickets WHERE tenant_id = $1 AND id = $2;In this query the tenant comes from the verified credential ($1), and the ticket ID alone is never enough to fetch the row.
Step 2: Define the billable events before writing code
Name event types with a stable vocabulary such as ticket.created, email.sent, and meeting.completed. For each type you need three decisions: the billable moment, the unit, and the rule for failure, cancellation, or correction. The sources behind this guide describe event-based usage metering in general but do not define these product-specific units, so the answers belong in your pricing terms and API reference.
| Event type | Candidate billable moment | Candidate unit | Questions to settle |
|---|---|---|---|
ticket.created |
The ticket record is committed | One ticket | Are tickets created by automation, merges, or splits billable? Is reopening a ticket a new unit? |
email.sent |
Your system accepts the message, or the provider confirms delivery | One message, or one per recipient | Is a bounced message billable? Does a message to ten recipients count as one unit or ten? |
meeting.completed |
The meeting ends | One meeting, or billable minutes | Are cancelled or never-started meetings excluded? How are partial minutes rounded? |
Version the event schema from the first release. Add fields through a new schema version, and never change the meaning of an existing field. Events written today may be billed months later, and their meaning must still be readable.
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The safest pattern is to write the usage event in the same database transaction as the state change it describes: the ticket row, the message record, or the meeting status. A transactional outbox, where the event is written to a table in that transaction and published by a separate process, serves the same purpose. Either way, a failed ticket creation produces no billable event, and a successful one cannot exist without its event. This is architectural guidance; neither the billing sources nor OpenTelemetry prescribe a database schema.
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A usable event record contains:
- tenant_id, taken from the authentication step and never copied from the request body;
- event_type and schema_version;
- quantity and unit, for example
1andticket, or42andminute; - occurred_at for when the action happened, and recorded_at for when your system wrote the row;
- source_object_id, the ticket, message, or meeting identifier;
- idempotency_key, a globally unique identifier generated by the producer of the event;
- corrects_event_id, empty for normal events and set only for reversals or adjustments.
{n "idempotency_key": "01J9Z3KQ7M8W2R5T6Y1N4P0V3C",n "tenant_id": "ten_48213",n "event_type": "meeting.completed",n "schema_version": 2,n "quantity": 42,n "unit": "minute",n "source_object_id": "mtg_7781",n "occurred_at": "2026-10-08T14:32:00Z",n "corrects_event_id": nulln}Treat this table as append-only. A correction is a new row that points to the original, not an update to the original. That keeps the history you need to investigate duplicates, late arrivals, and customer disputes.
Step 4: Make retries and corrections safe
Retries are where usage gets double-counted. A client that times out and resubmits should produce the same idempotency key, and the ledger should enforce uniqueness with a unique index on idempotency_key. Application-level checks alone are not enough, because two workers can check at the same moment and both insert.
Decide policy for three cases before launch:
- Late events, which arrive after their billing period has closed. Choose whether they roll into the next period, reopen the closed period, or are recorded for audit without billing. Whichever you choose, state it in the contract.
- Invalid events, which fail schema or tenant checks. Reject them at write time, log the reason, and never drop them silently.
- Corrected events, where the original quantity was wrong. Append a correcting event that references the original. Stripe documents meter event adjustments for correcting events, and its API reference describes identifiers for meter events; confirm the current constraints in that reference before relying on them.
Step 5: Enforce quotas synchronously against your own state
A quota decision has to finish inside the request, which means it cannot wait for a billing summary. Stripe documents that meter events are processed asynchronously and that summaries are eventually consistent. A billing summary is therefore the wrong place to ask “may this tenant create another ticket right now?” Keep a tenant-period counter in your own store and check it before the work happens.
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BEGIN;
SELECT used_units, allowance_units FROM tenant_period_usage
WHERE tenant_id = $1 AND period_start = $2 FOR UPDATE;
-- If used_units + $3 is greater than allowance_units and the quota is hard, ROLLBACK and return an error.
INSERT INTO usage_events (idempotency_key, tenant_id, event_type, quantity, occurred_at)
VALUES ($4, $1, $5, $3, now())
ON CONFLICT (idempotency_key) DO NOTHING;
-- Increment the counter only if the insert above actually wrote a row.
UPDATE tenant_period_usage SET used_units = used_units + $3
WHERE tenant_id = $1 AND period_start = $2;
COMMIT;The conditional increment matters. If a retry hits the conflict clause, the event is already recorded and the counter must not move again. The FOR UPDATE lock serializes writes for one tenant and period, which is correct for accuracy but limits throughput for a single very busy tenant. If that becomes a bottleneck, consider partitioning the counter, but measure first.
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| Enforcement mode | Behavior at the allowance | What it requires | Main risk |
|---|---|---|---|
| Hard quota | Rejects the action with a customer-facing error | Synchronous counter check and a defined error response | Customers blocked mid-workflow; support load |
| Soft quota with warning | Allows the action, notifies the tenant, and bills the excess | Overage pricing in the contract and a warning threshold | Unbounded usage if no ceiling exists |
| Hard quota with overage cap | Allows overage up to a fixed ceiling, then rejects | Both a warning path and a ceiling value | Two thresholds to explain and test |
Step 6: Aggregate by tenant, event type, and billing window
Summaries serve two readers: the tenant looking at a usage dashboard, and the finance process that produces an invoice. Build them from the ledger, grouped by tenant, event type, and billing period. Stripe’s model uses meters to aggregate events over billing periods and associates each meter with a price, so the same grouping can be sent to a billing provider.
The contract has to settle several details that change invoice totals:
- Timezone and period boundaries. A period that ends at midnight UTC and one that ends at midnight in the customer’s local time will bill a Friday-night meeting differently.
- Rounding. Meeting minutes that round up per meeting produce different totals from minutes summed and rounded once per period.
- Included allowances. Decide whether the allowance is shared across event types or tracked per type, such as 500 tickets plus 2,000 emails as separate pools.
- Overage policy. Specify the price per excess unit and whether overage is capped.
- Refunds and corrections. Decide whether a correcting event in a closed period changes the invoice, produces a credit, or only appears in the next period.
Throttling and metering are different controls
Amazon API Gateway request throttling protects throughput and service capacity for REST APIs. It measures requests. Billing measures units of work. A single request that creates three tickets is one request for the throttle and three units for the meter. A thousand read-only requests can be zero billable units and still need a rate limit.
| Control | What it measures | Typical scope | Customer-facing result |
|---|---|---|---|
| Request throttle | Requests per time window | Client, route, or plan | HTTP 429 Too Many Requests, with retry guidance |
| Usage quota | Billable units per billing period | Tenant | Rejection or overage, as defined in the contract |
| Meter | Billable events recorded | Tenant and event type | None; the event is recorded silently |
Give throttle errors and quota errors different codes and messages. A customer who hits a rate limit should retry later; a customer who hits a quota needs to buy more allowance or wait for the next period. Mixing them produces support tickets that are impossible to answer.
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Instrument operations without making metrics the ledger
The OpenTelemetry Metrics API defines counters for additive values and histograms for distributions such as request duration or response payload size. Use these for operational visibility: whether ingestion is keeping up, whether duplicates are spiking, and whether latency is climbing.
- Counter for usage events recorded, labelled by event type and outcome, such as accepted, duplicate, or rejected.
- Histogram for ticket API request duration and for email send latency.
Keep tenant identifiers out of metric labels. Every tenant becomes a separate time series, which can overwhelm a metrics backend. Tenant-level detail belongs in the ledger, where you can query it exactly. Sampling, export failures, and process restarts can all lose or distort telemetry. That is tolerable for alerting and unacceptable for invoices.
Reconcile the ledger to billing
Reconciliation is what makes the meter defensible. Run it on a schedule, not only at invoice time.
- Export or report events from the ledger to the billing provider, and record the provider’s acknowledgement for each event.
- Retry failed reports using the same idempotency key, so a retry cannot be counted twice.
- At the end of each billing period, compare the provider’s aggregate for each tenant and event type with the ledger total. Investigate every difference before the invoice is finalized.
- Route adjustments through a support workflow. The workflow should write a correcting event that references the original and record who approved it.
- Keep the tenant-to-billing-customer mapping in its own table with effective dates. Test two cases: reassigning a workspace to a different customer, where earlier events must stay with the original customer, and deleting a tenant, where billing records are retained according to your retention policy.
Choosing a billing provider
Stripe’s usage-based billing documentation covers recording usage through meter events and billing it through meters attached to prices. Stripe’s support guidance describes recording customer usage and billing it at the end of a subscription period, and gives an email SaaS business that records API calls as its example. Microsoft’s metered API model works differently: a consuming application is associated with an Azure subscription, and monitored costs can be split by application, calling tenant, or meter. That model fits an API exposed on Microsoft’s platform. It is not a general invoicing model for your own customers.
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| Comparison axis | Stripe usage-based billing | Microsoft Graph metered APIs |
|---|---|---|
| Billing customer of record | Your customer, through a subscription and its prices | The Azure subscription associated with the consuming application |
| What the provider bills | Your SaaS customers’ subscriptions | Costs tied to the consuming application’s Azure subscription |
| Event ingestion path | Meter events, per Stripe’s usage recording documentation | Not stated in the reviewed Microsoft page |
| Aggregation timing | Asynchronous processing; eventually consistent summaries | Not stated in the reviewed Microsoft page |
| Correction and duplicate handling | Meter event adjustments for corrections; event identifiers in the API reference | Not stated in the reviewed Microsoft page |
| Tenant-to-customer mapping | Your application maps each tenant to a billing customer | Monitored costs can be split by calling tenant |
| Reconciliation and export | Not covered in the reviewed Stripe pages; check the API reference | Monitored costs can be split by application, calling tenant, or meter |
| Pricing-model flexibility | Meters attached to prices allow usage-based pricing | Not stated in the reviewed Microsoft page |
| Regional availability | Not stated in the reviewed Stripe pages | Not stated in the reviewed Microsoft page |
| Lock-in | Meters and prices live in Stripe; your ledger stays in your own store | Tied to Microsoft Graph and Azure billing |
Verify current limits, throughput, and semantics in the provider’s official documentation before you commit to a design. The reviewed pages describe the model, not the current service limits.
Decisions to settle before launch
- The contract definition of one billable ticket, email, and meeting, including failure, cancellation, retry, and edit cases.
- The billing period, timezone, and rounding rules.
- Retention periods for usage events, including how long disputed and corrected events are kept.
- Privacy and data-jurisdiction requirements that determine where tenant usage records may be stored.
- Volume and latency targets, which determine the database, queue, and partitioning choices.
- Whether quotas are hard, soft, or capped overage for each event type.
The right storage engine and partition strategy depend on those targets, so treat the architecture above as a shape to fill in rather than a fixed stack.
The Bottom Line
Start with the ledger. A tenant-scoped, idempotent, append-only usage record with units you can defend on an invoice is the part that is hard to retrofit. Build quota checks against your own tenant state, use a billing provider for invoicing if you want one, and keep request throttling and operational metrics as separate controls.
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