How We Cut Our LLM Summarizer's Cost And Latency While Protecting Quality

We run an LLM pipeline that summarizes support tickets for a large network operations team: it reads long, noisy ticket logs and produces short summaries, customer status notifications, technician ticket summaries, and regional executive rollups. It works. But once it was running steadily, a quieter problem showed up. On an ordinary day, summarizing every ticket with a top-tier model and a full log was more expensive and slower than it needed to be.

Four levers — incremental summarization, trimming, right-sizing, and parallel execution — are compounding to cut blended LLM cost by roughly 60%.