PostgreSQL Constraint Triggers for AI Feature Usage Quotas: Enforcing Hard Limits at the Database Layer
PostgreSQL Constraint Triggers for AI Feature Usage Quotas: Enforcing Hard Limits at the Database Layer I've watched quota systems fail at scale more times than I'd like to admit. You implement soft checks in FastAPI—verify the tenant hasn't exceeded their monthly Claude API calls, then call the API. Works fine locally. Ships to production. Then during a traffic spike, three concurrent requests…
PostgreSQL database layer enforcement of quota systems can prevent application-layer quotas from failing during high traffic periods. Application-layer quotas implemented in FastAPI can lead to race conditions where multiple requests may all read the same count value, decide they are under the limit, and then all proceed to make API calls.
This results in exceeding the quota. Wrapping the database transaction with row-level locking addresses this issue but holds a database lock while making the external API call, leading to connection pool exhaustion. A more efficient approach is to enforce quotas at the database layer using triggers.
In this schema, tenant_feature_limits stores the monthly limit for each tenant and feature. Tenant_usage_log keeps track of each API call attempt. A materialized view, tenant_monthly_usage, calculates the current month's usage per tenant and feature. An index on this view allows fast lookups. The trigger function check_usage_quota is triggered after each new record is inserted into tenant_usage_log.
It retrieves the current month's usage and limit for the new record's tenant and feature. If the limit is reached or exceeded, the trigger rejects the insert, preventing the API call. This ensures quotas are enforced atomically and without the need for holding database locks during external API calls.
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