Vector Databases
Vector Databases
Overview
Vector databases are specialized storage engines designed to store and query high-dimensional embeddings (vectors). They are a critical component of [[Retrieval-Augmented Generation (RAG)]] systems.
Popular Options (2026)
| Database | Best For | Notable Features |
|---|---|---|
| Pinecone | Managed SaaS | Serverless scaling, built-in reranking. |
| Weaviate | AI-Native | Built-in vectorization, strong hybrid search. |
| Milvus / Zilliz | Enterprise-Scale | GPU-accelerated, billions of vectors. |
| Qdrant | Performance | Rust-based, advanced filtering. |
| pgvector | Convenience | PostgreSQL extension, keeps vectors near relational data. |
Key Trends
- Serverless Scaling: Most major providers now offer serverless models to minimize cost.
- Real-Time Indexing: Documents are searchable within seconds of creation.
- Built-in Reranking: Moving the “Reranking” step closer to the data to reduce latency.
Sources
- [[rag_research_2026]] (Research April 2026)