Why Vector Databases Matter Now
Vector databases are emerging as essential infrastructure for generative AI. By converting unstructured data into vector embeddings, they unlock “dark data” for use in semantic search, question answering, and retrieval-augmented generation (RAG). This makes enterprise data accessible to LLMs and dramatically improves the relevance and reliability of AI outputs.

Enabling Natural Language Interfaces Across the Enterprise
These technologies are transforming how organizations interact with data—shifting from SQL queries to natural language prompts usable by everyone from data scientists to business users. Vector search engines support highly contextual, multimodal search across text, images, and more.

From Niche to Mainstream
While still early in adoption, vector databases are rapidly evolving. Their capabilities are being integrated into general-purpose databases and streaming platforms, with a trend toward hybrid and multimodal search functionality.

Market Momentum and Buyer Guidance
This second annual GigaOm Sonar report tracks the accelerating maturity of the vector database landscape, from pure-play solutions to feature enhancements in traditional systems. It provides clear criteria to help technology leaders evaluate vendors and align capabilities to business needs.