Vector databases are the unsung heroes of the AI stack. They enable semantic search, recommendations, and RAG — making LLMs truly useful.
What Are Vector Databases?
They store embeddings — high-dimensional vectors capturing semantic meaning of text, images, or audio.
Embeddings are coordinates in semantic space. Similar concepts are close; unrelated are far. Vector DBs find "nearest neighbors" at speed.
Why They Matter
LLMs only know training data. Vector DBs connect LLMs to your proprietary data through RAG.
Leading Platforms
Pinecone
Managed, serverless. Auto-scaling, free tier. Best without DevOps overhead.
Weaviate
Open-source with hybrid search (vector + keyword). Self-host or cloud.
Qdrant
Written in Rust. Blazing fast. Filtering, payload indexing, multi-tenancy.
PGVector
PostgreSQL extension. Simplest path if already on Postgres.
99%
Relevance accuracy
10ms
Avg query latency
Use Cases
- Semantic search: Search by meaning
- RAG: Grounded LLM responses
- Recommendations: Similar items
- Anomaly detection: Unusual patterns
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Start with Pinecone for prototyping, migrate to self-hosted Weaviate/Qdrant for production. Laurel Jar can architect your strategy.