How a Simple Credits Counter Becomes a Billing Race Condition in AI SaaS

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A common pattern in AI SaaS apps stores user credits as a single integer column and deducts on each request, but this approach breaks down when API calls are processed asynchronously. When two concurrent requests arrive simultaneously, both can read the same balance before either transaction commits, allowing a user to spend more credits than they actually have. Using a SELECT FOR UPDATE query inside a database transaction acquires a row-level lock, forcing concurrent transactions to queue and see the updated balance before proceeding. However, row locks alone do not prevent duplicate charges from retries or redelivered jobs, which requires a separate idempotency key tied to a unique job ID. Together, row-level locking and idempotency constraints address two distinct races: overspending across different jobs and double-charging the same job.

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