Ongoing improvements to OpenSearch cover testing, faster and improved feedback for contributors, and better agentic AI support.

Generating embeddings from arrays of objects

A Trail of Bits report found OpenSearch outperformed Elasticsearch in vector search and general workloads, thanks to OpenSearch's support for multiple search engines, vector embeddings customization, and advanced metadata filtering options.

A Trail of Bits found OpenSearch outperformed Elasticsearch in vector search and general workloads, thanks to OpenSearch's support for multiple search engines, vector embeddings customization, and advanced...

Explore the OpenSearch journey from an AWS-led project to a vendor-neutral, community-driven open-source platform under the Linux Foundation. Learn about the project's growth, challenges, and future i...

Discover more about what's new at AWS with Amazon CloudWatch and Amazon OpenSearch Service launch an integrated analytics experience

OpenSearch’s stability as a Linux Foundation project will undoubtedly drive a significant uptick in community participation and enterprise adoption.

At All Things Open in October, Anandhi Bumstead, AWS’s director of software engineering, highlighted OpenSearch's journey and the advantages of the Linux Foundation's stewardship. OpenSearch, an open...

Ten years after the demise of Precrime, crime-solving is different and justice leans more on sophisticated and trusted technology than on the instincts of the precogs—individuals who able to see the f...

UltraWarm is a fully managed, warm storage tier that’s designed to deliver cost savings on the Amazon OpenSearch Service. With OpenSearch 2.17+ domains, you can now store k-NN (vector) indexes on UltraWarm storage reducing the cost of serving infrequently access k-NN indexes through warm and cold storage tiers. With UltraWarm storage, you can further cost optimize vector search workloads on the OpenSearch vector engine. To learn more, refer to the https://docs.aws.amazon.com/opensearch-service/latest/developerguide/ultrawarm.html.

UltraWarm is a fully managed, warm storage tier that’s designed to deliver cost savings on the Amazon OpenSearch Service. With OpenSearch 2.17+ domains, you can now store k-NN (vector) indexes on UltraWarm storage reducing the cost of serving infrequently access k-NN indexes through warm and cold storage tiers. With UltraWarm storage, you can further cost optimize vector search workloads on the OpenSearch vector engine. To learn more, refer to the documentation.

Amazon OpenSearch's vector engine can now run modern search applications at a third of the cost on OpenSearch 2.17 domains. When you configure a k-NN (vector) index for disk mode, it becomes optimized for operating in a low memory environment. With disk mode on, the index is compressed using techniques like binary quantization and search quality (recall) is retained through a disk-optimized rescoring mechanism using full-precision vectors. Disk-mode is an excellent option for vector search workloads that require high accuracy, cost efficiency and are satisfied by low hundreds-of-milliseconds latency. It provides customers with a lower cost alternative to the existing in-memory mode when https://opensearch.org/benchmarks is unnecessary. To learn more, refer to the https://opensearch.org/docs/latest/search-plugins/knn/disk-based-vector-search/.

Opensearch doesn't like ISO 8601 date timestamps that don't have an explicit Z (meaning UTC) or +04:00 timezone specifier. However, not having one at all is the same as UTC. Python .isofo...

The open source analytics engine will be directed by the community from now on, AWS announced at Open Source Summit Europe.

The open source analytics engine will be directed by the community from now on, AWS announced at Open Source Summit Europe.