Fair Queue for a Shared Free AI Server: 5-Dev Postmortem
Five independent clients on one free AI server will produce 429s and a thundering herd unless you add a fair queue. We fixed it with a client-side asyncio queue that capped concurrency at two, prioritized interactive work, and dropped 429s from 23 to 0 on a 100-request mixed workload. Disclosure: This article was prepared as part of MonkeyCode's product outreach. What Failed When Five Developers…
Five developers collaborated on one free AI server, which led to issues such as high latency, 429 responses, and a thundering herd of retries. The root cause was the lack of coordination among the independent clients, who assumed they were the only users. Each client built its own integration with its own retry logic, causing the server to receive five times the intended traffic.
The team eventually implemented a client-side asyncio queue that capped concurrency at two, prioritized interactive work, and dropped all 429 responses. The queue wraps any model-calling function, assigns priority, enforces concurrency, and tracks usage. It guarantees FIFO ordering within the same priority and explicitly rejects when the queue is full.
This architecture helped prevent the previously experienced issues and ensured fair access to the shared AI server.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
