Vector Databases: Leading Products You Should Know
The generative AI boom has elevated the vector database from a niche position to a star player. A vector database stores and queries embeddings of data from unstructured data sources like text files, images, audio & video. Embeddings are mathematical representations of words, sentences, and documents as numerical vectors in a high-dimensional space that captures the semantic meaning and relationships between these linguistic entities.
While companies have long used vector databases to recognize patterns and recommend actions, now they are using them to search documents as part of GenAI initiatives. Vector databases feed relevant content to language models (LMs), enabling companies to enrich prompts, fine-tune models, and govern outputs. Vector databases are becoming an increasingly important component of the generative AI stack. They support retrieval augmented generation, which supplement LM prompts with domainspecific content to make responses more accurate.
This report evaluates, scores, and compares the leading vector database products. The aim is to help zero in on the right vector database for your organization across use cases and domains. The report outlines the strengths and weaknesses of each product; and compares products along different dimensions in a visual quadrant chart. Most importantly, it publishes the worksheet that our analysts used to evaluate and score vendor products. You can modify the sheet and create your own scoring and ranking.
Vendor Comparison Chart