Quotas, Plans and Usage Limits — Rate Limiting, Quotas & Abuse Protection

Quotas cap total usage per billing period (requests, records, tokens, seats) by plan. Track usage with an append-only metering pipeline, enforce with fast counters (Redis) reconciled against the meter, warn users at thresholds (80%, 100%), and decide what happens at the limit: hard block (429/403 with upgrade link), soft limit with overage billing, or degraded service. Expose usage via API and dashboard so customers aren't surprised.

Quota enforcement styles

Plan behavior at limit	Pros	Cons
Hard block	Predictable cost	Breaks customer workflows
Overage billing	No interruption	Bill shock risk
Soft limit + grace	Time to upgrade	Revenue leakage
Degrade (lower priority / rate)	Service continues	Complexity

GET /v1/usage
{"period": {"start": "2026-10-01", "end": "2026-10-31"},
 "plan": "pro",
 "limits": {"requests": 1000000, "exports": 50},
 "used": {"requests": 812345, "exports": 12},
 "alerts": [{"threshold": 0.8, "sent_at": "2026-10-09T08:00:00Z"}]}

Common pitfalls

Quota counters that drift from billing data.
No warnings before hard limits.
Counting retries/failed requests against quotas without saying so.

Key points

Plans define quotas
Fast counters + reconciled meters
Warnings and visible usage

Further reading: Stripe: Usage-based billing

Practice questions

Should failed or rate-limited requests count against a customer's quota?

Answer

Usually not for server errors; it's a policy decision for client errors — document it clearly, since customers will reconcile your counts against their logs.

Why reconcile Redis quota counters with a metering pipeline?

Answer

Redis counters can be lost or drift during failures; the durable meter is the billing source of truth and corrects the fast counters.