With the general availability of Elastic Cloud Serverless, the search AI platform is improving capabilities for handling large, complex and performance-driven workloads.
To make this possible, the Elasticsearch architecture is transforming. It is built on a Search AI Lake tailored for real-time applications. “Our Search AI Lake inherently supports dense vectors and features crucial for applications like GenAI and RAG,” said Chief Product Officer Ken Exner.
With Elastic Cloud Serverless, compute and storage are separated. In addition, the platform uses indexing technology for search and cloud-native object storage. This allows the architecture to scale effortlessly while maintaining Elasticsearch’s low-latency search performance.
Quickly start to search
Elastic Cloud Serverless will focus on resource-based metrics, such as data entry, storage, and compute units. With this focus, customer budgets should remain manageable and scalable when needed, offering more control over managing workloads for different applications.
In doing so, Elastic differentiates between the various use cases of its search AI platform. For example, there will be three serverless options: Elasticsearch Serverless, Elastic Observability Serverless, and Elastic Security Serverless. The first allows developers to build AI-driven search applications quickly. The second is there to support observability workloads. Finally, Elastic Security Serverless offers security analysts a new cloud deployment option for their security analytics and SIEM applications.
Elastic Cloud Serverless is generally available on AWS immediately. Support for Azure is planned for early 2025.