AI Needs More Than a Vector Database

Interest in vector databases, crucial for applications like recommendation systems and natural language processing, is soaring, driven by their ability to enable fast semantic similarity searches essential for enhancing AI applications. Forrester classifies these databases into native vector databases, optimized for scale and performance, and multimodal databases, which handle various data types to simplify system management. However, vector databases alone do not fulfill the comprehensive needs of generative AI, which requires robust search capabilities across diverse data types, including unstructured data like PDFs. Emerging as a significant advancement, AI databases integrate vectors with structured and unstructured data, applying AI models to enhance computing efficiency and scalability. They support machine learning, natural language processing, and generative AI models to predict trends, interpret text, and generate content based on data patterns. Despite their advantages, AI databases still lack application logic and runtime management, necessitating a holistic platform that seamlessly integrates data, application logic, and execution. Vespa.ai is highlighted as an open-source platform for developing AI-driven applications, efficiently managing data, inference, and logic to support high-volume data and concurrent queries, available as both a managed service and open source.