Quantization and dimensionality reduction are now available in Redis Query Engine

Redis has introduced new quantization and dimensionality reduction features for its Query Engine to significantly reduce memory costs and enhance performance, particularly useful for AI applications facing high cloud service expenses. By partnering with Intel and leveraging Intel Scalable Vector Search technology, Redis offers a reduction in vector memory footprint by up to 37% without compromising query speed or accuracy. The implementation of SVS-VAMANA algorithm and compression strategies like LVQ and LeanVec allow users to achieve memory savings ranging from 26% to 37% while improving query throughput up to 144% in some cases. These enhancements are seamlessly integrated, ensuring that existing application queries remain unchanged while benefiting from improved efficiency. The new system adapts to various data distributions, enabling high-quality compressed representations with minimal overhead and substantial performance gains, making it a cost-effective solution for handling high-dimensional data in vector databases.