TL;DR
A specialized data storage system designed to store, index, and query high-dimensional vector embeddings quickly and efficiently for real-time applications.
Vector databases are optimized for similarity search rather than traditional keyword or relational queries. They index embeddings generated by machine learning models and use approximate nearest neighbor algorithms to locate related records. This allows applications to perform semantic search, detect duplicates, and retrieve relevant contexts instantly.
Why this matters for your business
It is the fundamental infrastructure component that powers semantic search engines and retrieval pipelines for production-ready AI applications.
