Data & Infrastructure

Vector Database

A vector database is a specialized database system that stores high-dimensional numerical vectors (embeddings) and enables lightning-fast similarity searches. Instead of exact key lookups, it finds semantically related content — the technical foundation for RAG systems, semantic search, and AI-powered knowledge platforms in enterprises.

Why does this matter?

Vector databases are the key to making enterprise knowledge accessible to AI. Manuals, contracts, emails — everything is indexed as vectors and searchable by meaning in milliseconds. Without a vector database, no RAG system, no intelligent customer support, and no knowledge-based AI agent can function.

How IJONIS uses this

We deploy pgvector for PostgreSQL-based stacks, Pinecone for managed cloud scenarios, and Qdrant for on-premise requirements. The choice depends on data volume, latency requirements, and GDPR constraints. Our indexing pipelines automatically keep your vectors current when source data changes.

Frequently Asked Questions

Do I need a dedicated vector database or does my existing database suffice?
For getting started, pgvector as an extension to your existing PostgreSQL database often suffices. With several million vectors or real-time requirements, a specialized solution like Pinecone or Qdrant pays off — they offer optimized indices and significantly faster queries.
How much storage does a vector database require?
A typical embedding vector (1,536 dimensions) occupies about 6 KB. One million document chunks thus require around 6 GB of pure vector storage plus index overhead. For most mid-sized companies, storage costs are in the low double-digit euro range per month.

Want to learn more?

Find out how we apply this technology for your business.