Vector Databases

Vector databases are specialized data storage systems designed to efficiently store, index, and query high-dimensional vector embeddings. They enable similarity search and nearest neighbor operations on data represented as numerical vectors, which is essential for AI applications like semantic search, recommendation systems, and retrieval-augmented generation (RAG). Unlike traditional databases, they excel at handling the mathematical relationships between vector representations of data.

Companies need vector databases NOW to power the latest generation of AI applications, particularly those built on large language models (LLMs) and multimodal AI. The explosive growth of Retrieval-Augmented Generation (RAG) for grounding LLMs in private, up-to-date data has made efficient vector search a critical infrastructure component. This trend is driving demand at companies like Scale AI for data pipelines and RunwayML for creative AI, where fast similarity matching on complex data (text, images, video) is non-negotiable.