Replacing Large Language Models with Jev Decision Models for Low-Latency Edge Service Orchestration

Abstract: Natural-language service requests can require a language-model decision before execution starts, consuming part of the request's latency budget. We integrate Jev's decision-oriented application programming interface (API) into edge service orchestration to reduce this overhead while retaining service completion. The integration extracts four bounded intent fields and applies a shared validator, admission policy, and scheduler, accounting for decision waiting throughout the request timeline. We compare Jev with a short, structured-output DeepSeek deployment using live API measurements followed by modeled execution, and then a real two-node optical character recognition (OCR) service with self-hosted Qwen and rule-based references. Across three consecutive measurement blocks, Jev reduces median client decision latency by 15.9-26.5%. In eight paired OCR conditions, Jev matches DeepSeek's correct, on-time completion count in seven and exceeds it in one. Without caching, median end-to-end latency on requests completed correctly by both systems is 11.1-25.3% lower; API fees per correct completion are 69.0-70.6% lower. Repeated-request caching largely removes the latency difference. The results demonstrate that decision-model substitution can lower both response latency and API fees in the tested service path, and identify fresh interpretation as the main opportunity for latency savings.