Deep explanation
Production incident: Meta Llama outage in a compliance review automation system
Production incident on self-hosted Meta Llama: triage of vLLM errors at peak, KV-cache exhaustion and preemption, retry storms that double cost, safe degradation for compliance queues, and the recovery plan.
Quick answer
In the first minutes I declare an incident, stop the bleeding, and protect the compliance queue: cap retries, shed or defer non-urgent batch work, and make sure no item is silently marked reviewed. Then I check the obvious triggers in order: a recent deploy or config change, GPU nodes lost, KV-cache saturation causing timeouts, and a retry storm or fallback to a paid API that would explain the doubled cost. Recovery means restoring capacity, replaying failed items idempotently, and then fixing the root cause with load tests, backpressure, and alerts.
The interview question
At peak traffic, your compliance review automation system, which runs a self-hosted Meta Llama model (for example Llama 3.3 70B on vLLM) to classify and summarize flagged communications, starts failing requests. Error rate spikes, inference cost doubles, and reviewers open tickets because their queues are stalling. Describe your first 30 minutes and your recovery plan.
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