Kinetica, the real-time GPU-accelerated database for analytics and generative AI, recently announced the real-time vector similarity search engine that can ingest vector embeddings 5X faster than the previous market leader, based on the popular VectorDBBench benchmark.

Under the hood Kinetica uses NVIDIA RAPIDS RAFT to harness the power of the GPU for vector similarity search. With Kinetica’s combined data and query latency for vector embedding pipelines, large language models (LLM) can immediately augment their results with new information via embeddings as soon as they are generated, without delays at scale.

Unlike existing vector databases that suffer from data latency issues, Kinetica’s innovative ability to leverage the GPU in real-time ensures access to the latest data, empowering applications with unparalleled speed, accuracy, and responsiveness. Its capacity to deliver instant insights amid data growth and change presents a groundbreaking solution for industries reliant on quick and up-to-date AI-driven analytics.

“At Kinetica, our focus has always been on delivering real-time insights in a rapidly evolving data landscape through our natively vectorized GPU optimized architecture,” said Nima Negahban, Co-founder and CEO, Kinetica. “The introduction of real-time vector similarity search for pattern and anomaly detection perfectly aligns with our technology foundation and underscores our position at the forefront of data-driven innovation.”

“While other vendors offer vector-only databases as a product, our approach integrates vector search as a powerful feature within our mature, distributed, secure, and ANSI SQL compliant database, providing enterprises with a comprehensive solution for data analytics.” said Amit Vij, Cofounder and President, Kinetica.

Kinetica’s vector similarity search is now available in Kinetica 7.2 for users of Kinetica Cloud Dedicated, Developer Edition, and Kinetica Enterprise.