Traditional databases are unable to meet the growing demands of generative AI (genAI) due to limitations in supporting modern vector multidimensional data and performing similarity searches. Vector databases overcome these challenges by providing cutting-edge data management capabilities for storing, indexing, processing, and searching vectors efficiently. They are specifically engineered to store vector embeddings, which are numerical representations of complex data such as text, audio, images, and video. These embeddings are generated by providers like OpenAI, Hugging Face, and Cohere, with the number of dimensions varying based on the data and ML model used for vector generation. Additionally, vector databases use a variety of advanced indexing and hashing techniques, including K-dimensional trees, hierarchical navigable small world (HNSW), locality-sensitive hashing (LSH), and graph-based indexes.

Vector databases are gaining attention due to genAI, leading to a plethora of new use cases. They are crucial for providing reliable and enriched data to support genAI applications, enabling more insightful and contextually relevant responses from genAI models. Vector databases also offer an effective solution for detecting data anomalies by analyzing deviations from expected norms and providing insightful recommendations. Forrester anticipates broad growth in vector database initiatives across various industries, including financial services, retail, healthcare, manufacturing, and energy.