Elastic Introduces Better Binary Quantization Technique in Elasticsearch

Developers using Elasticsearch to store vectorized data now benefit from 95% reduction in required memory

Elastic Introduces Better Binary Quantization Technique in Elasticsearch

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Elastic (NYSE: ESTC), the Search AI Company, announced Better Binary Quantization (BBQ) in Elasticsearch. BBQ is a new quantization approach developed from insights drawn from a technique proposed by researchers at Nanyang Technological University Singapore called RaBitQ. Elastic’s BBQ is a distinct evolution of the ideas presented in the RaBitQ paper. It delivers advancements in quantization for Lucene and Elasticsearch, offering high-ranking quality while achieving a 95% memory reduction and similar storage offered by scalar quantization.

“Elasticsearch is evolving to become one of the best vector databases in the world, and we see our users wanting to put more and more vectorized data in it,” said Ajay Nair, general manager, Platform at Elastic. Better Binary Quantization is our latest innovation to reduce the resources needed to store vectorized data and provide freedom to our users to vectorize all the things.”

BBQ is now available as a tech preview in Elasticsearch for self-managed and cloud users and will be contributed to Apache Lucene. Read the Elastic blog for more information.

About Elastic

Elastic (NYSE: ESTC), the Search AI Company, enables everyone to find the answers they need in real-time using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform, the development platform used by thousands of companies, including more than 50% of the Fortune 500. Learn more at elastic.co.

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