标签：#asyncio

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 Shared One Server We shared one MonkeyCode free server for code review and refactoring. Each of us ran our own scripts. Nobody coordinated. The first symptom was latency: requests that took two seconds started taking thirty. Then came the 429s. Then came the retries. Retries made everything worse. The server spent more time rejecting requests than answering them. The timeline compressed quickly: Day 1: two developers, no issues Day 3: four developers, latency doubles Day 5: five developers, 429s appear Day 6: retries cause a thundering herd Day 7: the team stops using the server The root cause was not the server. It was the absence of coordination. Five independent clients hammered one endpoint. Each client assumed it was the only user. The server had no way to prioritize. HTTP 429 is the standard “too many requests” signal; we treated it as a retry cue instead of backpressure. That is how a shared free endpoint turns into a retry storm. The deeper problem was architectural. Each of us built a separate integration. Each integration had its own retry logic. Under load those retries multiplied. The server received about five times the intended traffic, not because we needed five times the work, but because five clients were guessing independently. Contrast the two modes we actually ran: Uncoordinated: five scripts, five retry loops, unbounded in-flight calls, no shared view of queue depth. Coordinated: one process, one priority heap, two in-flight calls, explicit rejection when the queue is full. The first mode failed in a week. The second mode is what we shipped. How We Built