Backpressure for LLM Pipelines: Queue Theory Applied to Token-Based Services
A retry storm at 3 a.m. usually starts the same way: a brief provider hiccup pushes a few requests over the rate limit, your client library retries them, those retries land on a still-recovering endpoint, more requests fail, and within ninety seconds your queue depth has gone vertical while your provider dashboard shows you sitting at 100% of your tokens-per-minute quota with a backlog measured in five-figure dollars. The post-mortem will say "thundering herd." The honest answer is that you built a fixed-throughput retry policy on top of a variable-capacity downstream and forgot that queue theory has opinions about that.
Most of the well-known service resilience patterns were written for downstreams whose throughput is a wall: a database with a connection pool, a microservice with a known concurrency limit. LLM providers are not that. Your effective throughput is a moving target shaped by your tier, the model you picked, the size of the prompt, the size of the response, the time of day, and whether someone else on the same provider is fine-tuning a frontier model right now. Treating it like a fixed pipe is the root cause of most of the LLM outages I've seen this year.
