Bringing LLMs to Cloud Log Analysis: Applied Techniques and Engineering Guidance

Abstract

Cloud-native computing has completely overhauled the software design and operation of today's software systems, and has enabled organizations to deploy scalable applications using distributed microservices, containers, and orchestration platforms like Kubernetes. These architectures deliver agility and scalability, but also produce vast amounts of diverse log data that are difficult for traditional monitoring and observability solutions to handle. The traditional log analysis methods, such as keyword matching, rule-based filtering, and statistical anomaly detection, are unable to capture the contextual relationships between the complex operational events, which leads to delayed fault diagnosis, too many false alarms, and higher operation burden. The introduction of Large Language Models (LLMs) has opened the door to new ways of using cloud log analysis, such as natural language understanding, contextual reasoning, and semantic interpretation, to help engineers better identify operational problems quickly.
In this paper, we explore how LLM models can be used in three essential cloud log analysis tasks: failure signal detection, anomaly explanation, and cloud incident summarization. The paper does not offer a benchmark comparison of different models but rather consolidates applied engineering methodologies to enable a successful integration of LLMs within cloud observability workflows. Special focus is placed on how to prompt engineers to work with log-centric inputs, how to manage high-cardinality log repositories with Retrieval-Augmented Generation (RAG), and deployment considerations, including latency, computational cost, scaling, and operational governance.