AWS is offering to help enterprises address the growing cost of retaining telemetry for talkative AI applications with a new engine for its managed Amazon OpenSearch Service optimized for log analytics, which it claims can reduce storage costs by 70% and at the same time deliver better price-performance.

AI and agentic applications are generating more telemetry than conventional observability architectures were built to manage economically, forcing enterprises to balance retaining the operational data needed for security, compliance and incident response against rising related infrastructure costs.

The new engine will allow customers to continue using the same management console, APIs, security model and networking configuration as the service’s existing general-purpose engine, while storing data in Apache Parquet format and maintaining Lucene search indexes for searchable fields, AWS said.

It uses Apache Calcite to parse and optimize queries before routing analytical operations to Apache DataFusion and search predicates to Lucene, allowing search and analytical aggregation to run within the same query, AWS executives wrote in a blog post.

The optimized engine supports SQL and Piped Processing Language (PPL), they said.