What Changed When We Swapped Models Mid-Rollout and Cut Tail Latency

June 18, 2025 - during a scheduled feature ramp for a customer-support assistant that handled live chats and email triage, a sudden latency cliff made escalation routing time out. The incident coincided with a marketing push that tripled daily traffic for 48 hours, and the system's core model began dropping context on conversations longer than six messages. As the lead solutions architect responsible for uptime and cost, the situation required a rapid, evidence-driven intervention: keep the feature live, restore SLA, and reduce per-conversation spend without regressing accuracy. This is a focused case study of that single, high-stakes migration: what failed, why we chose the replacement models we did, how we executed the swap in production, and what actually improved when the dust settled. Discovery We were running a heavyweight foundation model tuned for long-context understanding inside a service mesh with synchronous inference calls. The plateau surfaced as two measurable problems: