Reserve Tokens Before Queuing AI Jobs to Prevent Silent Cost Overruns

A software engineering post from MonkeyCode argues that enqueueing AI model requests without first reserving token capacity leads to hidden resource waste on both free and paid tiers. When a client times out and retries, two attempts consume slots simultaneously, yet only one result can be used. The author notes that free model access masks this problem because no invoice appears, even though queue slots and context windows are still consumed. The proposed fix involves estimating input tokens, capping output, and adding a wait buffer before joining any queue — aborting locally if the budget cannot cover the full attempt. A Python code sketch illustrates the pattern using a single reservation and a deterministic attempt ID to prevent duplicate calls from a single user action.