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Moving Scheduled LLM Curation from Cloud APIs to Local Models

Guatu 2026年08月14日 08:15 1 次阅读 来源:Dev.to

Scheduled LLM curation is the least glamorous agent workload you run. A cron job wakes up at 3am, reads a pile of memory, asks a model to dedupe it, summarize it, re-rank it, and writes the result back. Nobody is watching. There's no chat window, no streaming tokens, no human to click a button. It just has to work, quietly, every night. That "nobody is watching" part is exactly what makes the cloud-versus-local decision harder than it looks. When you have a human in the loop, a failed API call throws an error you can see and retry. In a headless cron context, the same failure turns into a job that hangs on an approval prompt no one will ever answer, or a pod that curated three months of context into an emptyDir that vanished on restart. I've run curation both ways: nightly jobs hitting a hosted API, and the same logic pointed at a local model on my Kubernetes cluster. Both work. They fail differently, cost differently, and demand different things from you operationally. Here's the actual tradeoff, not the marketing version. The decision point You reach this fork once your agent memory stops being a toy. Early on, you curate by hand or with a cheap synchronous call inside your agent loop. Then the memory grows, the curation gets expensive, and you pull it out into a scheduled job so it runs off the critical path. Now you're paying an API on a timer, and two things start to bug you. First, the data. Curation reads your entire memory store to make decisions. If that memory contains anything you'd rather not stream to a third party (internal notes, customer context, infrastructure details), every scheduled run ships it over the wire. I wrote about the general version of this problem in privacy-routed LLM inference , and scheduled curation is the workload where it bites hardest, because it touches everything, repeatedly, forever. Second, the cost shape. A curation pass over a large vector store is a lot of tokens for a job that produces no user-facing latency benefit. Yo

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