We’re thrilled to announce that Elasticsearch Serverless is now generally available. We’ve re-architected Elastisearch as a fully managed service that autoscales with your data, usage, and performance needs. It has the power and flexibility of Elasticsearch without operational overhead.
Since its technical preview this spring, we’ve introduced new capabilities to help developers build and manage applications faster. Whether you’re implementing semantic search, keyword search, or even image search, Elasticsearch Serverless simplifies the process, allowing you to focus on innovation instead of infrastructure.
Designed to eliminate the complexity of managing resources, Elasticsearch Serverless makes it easier to run search, RAG, and AI-powered applications while maintaining the speed, relevance, and versatility Elasticsearch is known for.
In this post, we’ll share how Elasticsearch Serverless simplifies building search applications with its modern architecture and developer-friendly features.
Elasticsearch is the backbone of search experience
Elasticsearch has long been the trusted engine for developers, data scientists, and full-stack engineers seeking high-performance, scalable search, and vector database capabilities. Its powerful relevance features and flexibility have made it the backbone for countless search-driven applications.
Elasticsearch’s innovations in query speed and vector quantization have positioned it as a leading vector database, supporting scalable AI-driven use cases like semantic and hybrid search.
Today, Elasticsearch continues to set the gold standard for search by combining:
- High speed and relevance for text search.
- Flexible query capabilities to tailor search workflows.
- Seamless handling of hybrid queries, combining vector and lexical search.
- An open-source core, rooted in Lucene, with continuous optimizations that push the boundaries of search technology.
As search use cases evolve—incorporating hybrid search, AI and inference, and dynamic workloads—teams have more options than ever for scaling and managing infrastructure to meet their unique needs. These evolving demands present an exciting opportunity to rethink how we design for scale.
Elasticsearch with serverless speed and simplicity
Elasticsearch Serverless builds on Elasticsearch’s strengths to address the demands of modern workloads, characterized by large datasets, AI search, and unpredictable traffic. Elasticsearch Serverless meets these challenges head-on with a reimagined architecture purpose-built for today’s demands.
Foundationally, Elasticsearch Serverless is built on a decoupled compute and storage model. This is an architectural change that removes the inefficiencies of repeated data transfers and leverages the reliability of object storage. From here, separating critical components enables independent scaling of indexing and search workloads, and resolves the long-standing challenges of balancing performance and cost-efficiency in high-demand scenarios.