Vector Quantization: Scale Search & Generative AI Applications

MongoDB Atlas Vector Search has introduced a set of vector quantization capabilities, which reduce vector sizes while preserving performance. This enables developers to build powerful semantic search and generative AI applications at scale and lower costs. The flexible document model in MongoDB allows for greater agility in testing and deploying different embedding models quickly and easily. Vector quantization is a technique that compresses vectors while maintaining their semantic similarity, offering solutions to challenges faced by large-scale vector applications. It significantly reduces memory and storage costs without compromising important details. The most impactful benefit of vector quantization is increased scalability and cost savings through reduced computing resources and efficient processing of vectors. In the coming weeks, additional vector quantization features will be released, including support for binary quantized vectors and automatic quantization and rescoring.