Why TileDB as a Vector Database

TileDB has introduced vector search capabilities to its array-based database, positioning itself as a versatile vector database suitable for handling complex data modalities. As vector databases gain traction with the rise of Generative AI and large language models (LLMs), TileDB stands out by leveraging its inherent structure to offer efficient vector search through its new TileDB-Vector-Search library. This library enhances the database with features like fast approximate similarity search and native support for arrays, making it up to 8 times faster than popular alternatives like FAISS. TileDB's serverless, cloud-native architecture supports various deployment modes, ensuring scalability and cost-effectiveness, while its unified system manages vector embeddings alongside raw data, offering flexibility across multiple data modalities. Despite its focus on vector search, TileDB's broader vision is to redefine data management by integrating diverse data types into a single, modernized database, aiming to support emerging technologies like LLMs to unlock deeper insights from data.