The Evolution and Future of Vector Databases: Insights from Charles, CEO of Zilliz

Charles, CEO of Zilliz, discusses the evolution and future of vector databases in AI applications. He explains that vector databases are designed to manage and query unstructured data like images, videos, and natural languages through deep learning algorithms and semantic queries. They are widely used in recommendation systems, chatbots, and semantic search. The current landscape of vector databases includes purpose-built ones like Milvus, traditional databases with a vector search plugin like Elasticsearch, lightweight vector databases like Chroma, and more technologies with vector search capabilities like FAISS. Charles shares insights into building the Milvus vector database system, emphasizing its support for heterogeneous computing, both vertical and horizontal scalability, and offering a smooth developer experience from prototyping to production. He also provides guidance on choosing the right vector database for businesses based on performance requirements and projected data volume growth. Charles predicts that future vector databases will extend their capabilities beyond similarity-based search to include exact search or matching, as well as support additional vector computing workloads like clustering and classification.