AI Glossary
Vector Database
A specialized database that stores and searches embeddings (numerical representations of text, images, or other data). Vector databases power semantic search, recommendation systems, and RAG pipelines.
Understanding Vector Database
Vector databases store data as high-dimensional numerical vectors (embeddings) and find similar items using mathematical distance calculations. This enables semantic search — finding content by meaning rather than exact keyword matches.
For businesses, vector databases are the backbone of RAG systems. Your documents are converted to embeddings and stored in a vector database. When a user asks a question, the system finds the most semantically relevant passages and feeds them to the AI model.
Popular vector databases include Pinecone (managed cloud), Weaviate (open-source), Qdrant (open-source), and pgvector (PostgreSQL extension). The choice depends on your scale, infrastructure preferences, and whether you want managed or self-hosted.
Vector Database in Canada
Canadian businesses requiring data residency can use self-hosted vector databases (Weaviate, Qdrant) on Canadian cloud regions or pgvector within their existing Canadian-hosted PostgreSQL deployments.
Related Services
Frequently Asked Questions
For semantic search (finding content by meaning), yes. For simple keyword search, traditional databases work fine. If your users search for concepts rather than exact terms, a vector database adds significant value.
Modern vector databases scale to billions of vectors. For most business applications (millions of documents), any major vector database will perform well. The choice depends more on operational preferences than scale limits.
See Vector Database in Action
Book a free 30-minute strategy call. We'll show you how vector database can drive real results for your business.