The text discusses the use of frozen indices in Elasticsearch, particularly within hot-warm architecture, to optimize resource usage and cost efficiency when managing large datasets, such as logs and metrics. It explains how frozen indices maintain searchability while significantly reducing memory footprint by dropping transient data structures from memory, making them beneficial for handling less frequently queried historical data. The use of frozen indices is highlighted as an innovative feature in Elastic Stack, allowing businesses to retain searchable historical data without incurring significant memory overhead or operational costs associated with traditional methods like data snapshots and archiving. The process involves using the Freeze index API, ensuring indices are force-merged before freezing to enhance performance, and integrating with tools like Kibana for visualization. Additionally, the text outlines the operational nuances of managing frozen indices, including the necessity of unfreezing them for write operations and configuring search settings in Kibana to access frozen data.