With the development of learning-based embedding models, embedding vectors are widely used for analyzing and searching unstructured data. As vector collections exceed billion-scale, fully managed and horizontally scalable vector databases are necessary. In the past three years, through interaction with our 1200+ industry users, we have sketched a vision for the features that next-generation vector databases should have, which include long-term evolvability, tunable consistency, good elasticity, and high performance

Recently, there has been a trend towards managing high-dimensional vector data in data science and AI applications. This is fueled by the proliferation of unstructured data and machine learning (ML), where ML models usually transform unstructured data into feature vectors for data analytics. Existing systems and algorithms for managing vector data have limited functions and usually incur serious performance issue when handling large-scale and dynamic vector data.