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SAN FRANCISCO - Elastic (NYSE:ESTC), a $9.2 billion market cap search technology provider with robust revenue growth of 17% year-over-year, has released two new vector search enhancements aimed at improving performance and reducing costs for AI applications, according to a press release issued by the company.
The search technology provider, which maintains a healthy gross profit margin of 74.5% and strong liquidity position according to InvestingPro data, has introduced ACORN, a filtering algorithm for vector queries, alongside Better Binary Quantization (BBQ) as the default method for high-dimensional dense vectors in Elasticsearch 9.1.
ACORN integrates filtering directly into the search process, allowing for flexible filter definition at query time even after document ingestion. The company states that in benchmarks, ACORN delivers up to five times faster performance for filtered vector searches without compromising accuracy.
The BBQ quantization method, now set as default for dense vectors of 384 dimensions or higher, provides approximately 32 times compression while improving ranking quality. When tested across industry-standard BEIR datasets, BBQ outperformed traditional float32-based search in nine out of ten cases using the NDCG@10 metric for ranking accuracy. For deeper insights into Elastic’s technological innovations and financial performance, discover more with InvestingPro, which offers exclusive analysis and 6 additional ProTips for informed investment decisions.
"ACORN for filtered vector queries and default Better Binary Quantization represent a step-change in performance and efficiency," said Ajay Nair, general manager of Platform at Elastic, in the press release.
These enhancements are designed to help developers build AI applications that can execute complex queries with lower latency while reducing memory requirements and infrastructure costs.
The new capabilities are available in Elasticsearch 9.1, with documentation accessible through the company’s website.
In other recent news, Elastic announced that its Elastic Cloud Hosted service has achieved FedRAMP High "In Process" status on AWS GovCloud (US), marking a significant step in supporting sensitive U.S. federal government workloads. This designation requires over 400 security controls to protect cloud environments handling sensitive information. Meanwhile, Elastic’s fiscal fourth-quarter results showed mixed performance, with a weaker federal sector affecting earnings, leading Citi analysts to lower their price target from $160 to $125 while maintaining a Buy rating. Similarly, Canaccord Genuity adjusted their price target to $110 from $135, citing potential upside due to what they consider conservative guidance.
Monness, Crespi, Hardt upgraded Elastic’s stock rating from neutral to buy, setting a price target of $111, noting the company’s underperformance compared to AI-fueled tech stocks. DA Davidson maintained a Neutral rating and a $75 price target despite Elastic’s revenue growth surpassing expectations, as the revenue forecast fell short of consensus. Management expressed optimism about sales execution and identified Generative AI as a potential growth area. These developments highlight Elastic’s ongoing efforts to navigate market challenges and capitalize on emerging opportunities.
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