Vector databases can be categorized into two types:
Dedicated vector databases. These databases have an advantage over traditional databases when scaling to billions of vectors. They offer optimized storage and query capabilities for vector embeddings. Many organizations are using these databases for genAI, and we are hearing very positive feedback on their usage.
Extended vector databases. These databases don’t support vectors natively but through vector indexes and functions. We believe that most traditional databases will offer some level of vector processing capabilities in the near future. Some traditional database vendors already support vector data, offering broader multimodel capabilities. Organizations are using them to integrate traditional structured and unstructured data with high-dimensional vectors to support semantically driven LLMs.