Elastic launched Search AI Lake, an industry-first cloud-native architecture optimised for real-time, low-latency applications. It includes search, retrieval augmented generation (RAG), observability, and security, and it powers the new Elastic Cloud Serverless offering, which claims to remove operational overhead to scale and manage workloads automatically.
“To meet the requirements of more AI and real-time workloads, it’s clear a new architecture is needed that can handle compute and storage at enterprise speed and scale – not one or the other. Search AI Lake pours cold water on traditional data lakes that have tried to fill this need but are simply incapable of handling real-time applications. This new architecture and the serverless projects it powers are precisely what’s needed for the search, observability, and security workloads of tomorrow,” said Ken Exner, chief product officer at Elastic.
Search AI Lake benefits
The offering claims to have boundless decoupled compute and storage that enables scalability and reliability using object storage. It eliminates the need for replicating indexing operations across multiple servers and reduces data duplication.
It also includes enhancements to maintain query performance, including smart caching and segment-level query parallelisation, which reduces latency by enabling faster data retrieval.
It also claims to independently scale indexing and querying and includes GAI-optimised native inference and vector search, powerful query and analytics, and native machine learning.
The Search AI Lake is distributed - cross-region, cloud, or hybrid to normalise, index, and optimise any data format for faster querying and analytics while reducing data transfer and storage costs.
