Cost-Aware LLM-Style Routing for AIOps Log Analysis: Log Parsing, Anomaly Detection, Fault Diagnosis, and Incident Summarization on LogEval Task Files

This study investigates a local and cost-aware routing framework for AIOps log analysis using the LogEval benchmark. The evaluation covers four tasks: log parsing, anomaly detection, fault diagnosis, and incident summarization. Instead of relying on external large language model APIs, the experiment implements deterministic local policies that simulate zero-shot and few-shot LLM-style inference under controlled token-cost and latency assumptions. Six approaches were compared: regex normalization, TF-IDF with machine learning, a local character-based classifier, zero-shot policy, few-shot retrieval policy, and a routing cascade. At a risk threshold of 0.20, the router directed only 12.9% of queries to the few-shot retrieval policy while achieving parsing accuracy of 0.991, anomaly F1-score of 1.000, diagnosis accuracy of 1.000, ROUGE-L of 0.743, and BLEU-1 of 0.814. The routing strategy reduced simulated token cost by 80.1% compared with always using few-shot retrieval. Additional unseen-template evaluation revealed limited generalization for closed-label classifiers and retrieval methods when encountering unseen patterns. The findings indicate that routing can effectively reduce AIOps inference costs, while further validation with real LLMs and stronger generalization testing are required before production deployment.