Defined term
Vector store
A database optimized for similarity search over embeddings.
A vector store indexes embeddings using algorithms like HNSW or IVF for sub-linear nearest-neighbor search. Choices include managed services (Pinecone, Weaviate Cloud), open-source (Qdrant, Weaviate, Milvus), and Postgres extensions (pgvector). The right choice depends on scale, latency target, hybrid search needs, and operational constraints.
When it matters
When you have 10,000+ documents to retrieve over and your query volume justifies dedicated infrastructure. Below that, pgvector in your existing Postgres often beats specialized vector DBs.
Real example
A 4M-vector store on Pinecone for a legal-research workflow: 99.9% uptime, P99 query latency 45ms, 12 metadata filters per query (jurisdiction, date, topic, doc-type). Reranking on top-100 results before final answer generation.
KPIs to watch
Query latency P95 (<100ms target), retrieval recall@10 (>0.85), index refresh cycle time (<4h for daily ingest).
Related terms
Embeddings
Numerical vectors that represent the meaning of a text, image, or other piece of content.
RAG (Retrieval-Augmented Generation)
Generation grounded in retrieved source documents rather than the model's parametric memory alone.
Semantic search
Search by meaning, not by keyword overlap.
Agentic AI
AI systems that can plan, take multi-step actions, and use tools to complete tasks autonomously.
See it in action
We use this every week
Book a 30-min call and we'll walk you through how Vector store shows up in a real engagement we're running.
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