Elastic Stack AIOps Labs 8.12 has launched the general availability of its log rate analysis feature, which employs advanced statistical methods to swiftly identify reasons for changes in log rates, such as specific services, regions, or shared log message characteristics. Initially released in a tech preview as "explain log rate spikes" in version 8.4, the tool has been refined to analyze both spikes and dips in log data, offering improved reliability and scalability for large data sets. Essential to its functionality are Elasticsearch features like p_value scoring for identifying significant field/value pairs, frequent_item_sets for detecting patterns, and random_sampler for efficient data sampling. The user-friendly interface in Kibana’s Machine Learning section allows users to investigate log rate deviations by comparing results against baseline data, while integration with Kibana’s alerting system and AI Assistant further enhances observability and provides actionable insights. Despite its standalone capabilities, log rate analysis is part of Elastic’s broader AIOps suite designed for comprehensive observability solutions.