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Enforce a free-plan limit where usage is accepted or reserved: in a server-side operation backed by the database. A frontend check can show remaining usage and explain the limit, but it cannot make the final allow-or-deny decision safely. The server should apply the quota condition atomically, and permit the protected action only when that operation succeeds.
Why a limit in the UI is not enforcement
A browser can display a usage count and disable a button, but its state is not authoritative. A request may come through another interface or code path, or multiple requests may reach the server before the browser receives an updated count. If the server merely trusts a value the client supplied—or reads usage, compares it in application memory, and writes later—competing requests can each see room under the limit.
Use the UI to communicate the rule, not to secure it. Every server path that consumes or reserves quota must pass through the same authoritative check.
Choose what the quota counts before implementing it
A correct database condition cannot compensate for an ambiguous accounting rule. Define the unit, the period, and when usage is recorded before writing the enforcement logic.
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- Unit: Decide whether the limit counts actions, items, seats, bytes, or another measurable quantity. If one request can consume several units, account for the full amount.
- Window: Specify whether the cap applies per billing period, calendar period, rolling window, or lifetime. Define the reset boundary and timezone where relevant.
- Usage state: Decide whether quota is consumed when work is requested, reserved while it is in progress, or counted only after completion.
- Failure handling: Determine whether a failed downstream operation releases a reservation. Otherwise, a user may lose quota for work that never completed.
- Comparison: State whether reaching the limit blocks only the next action or also affects existing functionality. Make sure the unit and limit use the same scale and that arithmetic cannot overflow.
Use an atomic conditional update for a simple counter
For a single counter row, make the allowance check and increment one conditional database mutation. The following is an illustrative SQL shape, inferred from PostgreSQL’s documented conditional UPDATE behavior; it is not a tested, drop-in implementation or a PostgreSQL quota feature:
UPDATE account_usage
SET used = used + :amount
WHERE account_id = :account_id
AND used + :amount <= :limit;Permit the protected operation only if the database adapter reports that exactly one row was updated. If the predicate is false, the update affects zero rows and the application denies the request. PostgreSQL documents that an UPDATE can affect zero rows when its condition matches none; treating the affected-row result as the quota decision is an application design pattern, not a built-in quota system. See the PostgreSQL UPDATE documentation.
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This pattern assumes the account’s usage row already exists, the amount and limit are expressed in compatible units, the reset period is correct, and the adapter reliably reports affected rows. Handle a missing row explicitly rather than accidentally treating it as an available allowance. For a limit that can change concurrently, ensure the limit used by the predicate is itself read or stored consistently with the quota update.
Keep the quota decision and protected writes together
If accepting a request requires both reserving usage and changing business data, perform those database writes in one transaction. If any required step fails, roll back the group so the reservation and business mutation do not diverge. PostgreSQL describes a transaction as an all-or-nothing operation and notes that intermediate changes are hidden from other concurrent transactions. Read PostgreSQL’s transaction documentation.
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Do not hold a database transaction open while waiting on a payment provider or other external service. For work that crosses systems, a common design is to reserve quota transactionally, then run an idempotent workflow to complete or reconcile the external effect. Define how that workflow releases or finalizes the reservation if the external step fails.
Choose a concurrency strategy for more complex rules
A conditional update is useful for a straightforward counter, but quotas involving multiple rows, predicates, or shared resources need a deliberate consistency strategy. PostgreSQL warns that business-rule checks can be difficult to maintain under Read Committed isolation. Its documentation describes Serializable transactions as a way to enforce consistency for such rules, while requiring applications to handle serialization failures and retry when appropriate. See PostgreSQL transaction isolation.
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Explicit row locks
PostgreSQL’s SELECT ... FOR UPDATE locks selected rows against competing writers and lockers until the transaction ends. This can coordinate a check and subsequent mutation, but correctness depends on locking the right rows and keeping the check and writes in the same transaction. Lock acquisition order matters: competing transactions that lock resources in different orders can deadlock. See PostgreSQL explicit locking.
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Serializable isolation can be appropriate when the invariant depends on a wider set of records or conditions that are awkward to encode in one atomic update. It does not mean every attempt will succeed: the application must recognize serialization failures and retry the transaction safely. Choose transaction and locking behavior for the specific database and workload; PostgreSQL’s documentation covers versions 18, 17, 16, 15, and 14, and the details should not be assumed to apply unchanged to another database.
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There is no universal performance winner among conditional updates, explicit locks, and serializable transactions for this workload. If throughput matters, benchmark the actual schema and traffic pattern rather than relying on a general speed claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decide what reaching the limit means to the customer
Enforcement is also a product and pricing choice. Stripe describes hard caps, soft caps, tiered allowances, and rate-based limits as distinct approaches. Compare their trade-offs against the cost or value metric your quota measures:
| Policy | Cost control | Service continuity | User clarity |
|---|---|---|---|
| Hard cap | Stops additional usage at the threshold. | Blocks the capped action after the limit is reached. | Clear if the threshold and next step are visible. |
| Soft cap | Allows an overage or applies throttling rather than stopping immediately. | Can preserve access, depending on the chosen overage or throttle. | Requires clear notice of what continued use means. |
| Tiered allowance | Sets different limits for different plans. | Can offer more capacity on a higher tier. | Works best when plan allowances are easy to compare. |
| Rate-based cap | Constrains use over a defined time interval. | May allow access again when the interval resets. | Needs a clear explanation of the window and reset. |
For a free plan, say what is blocked, when the allowance resets, whether failed attempts count, and whether the user can upgrade or wait. Show current usage and provide an alert before the threshold where useful, along with a concrete next action. Stripe’s guidance recommends aligning caps with the metric tied to cost or value, making usage visible, and establishing threshold alerts and upgrade paths. Its usage-based billing documentation also describes fixed-fee-plus-overage, pay-as-you-go, and credit-burndown approaches for paid usage models; those are pricing alternatives, not substitutes for enforcing the underlying usage rule.
Quick Recap
What the implementation should do on each request
- Identify the account and requested amount on the server. Do not rely on a client-provided usage total or limit as the authority.
- Apply the quota rule atomically. For a simple counter, use a conditional update and check its affected-row result. For a multi-record invariant, use an appropriate transaction and consistency strategy.
- Perform the protected database mutation in the same transaction when it must succeed or fail with the reservation. Roll back both on failure.
- Handle retryable database failures deliberately. In particular, catch serialization failures if using Serializable isolation and retry only when the operation is safe to repeat.
- Return a clear outcome to the interface. The UI should explain whether the action was accepted, the current limit state, and the available next step; it should not be the mechanism that enforces the quota.
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