Vector databases are purpose-built for similarity search over high-dimensional embeddings. They're the backbone of RAG systems, recommendation engines, and semantic search — enabling sub-millisecond retrieval across millions of vectors.
Best for: Production workloads needing managed infrastructure.
Fully managed, serverless scaling, namespaces for multi-tenancy, metadata filtering, and hybrid search. Zero ops overhead but higher cost at scale.
Best for: Teams wanting open-source with advanced features.
Supports hybrid search (vector + keyword), GraphQL API, built-in vectorization modules, and multi-modal embeddings. Self-host or use Weaviate Cloud.
Best for: Prototyping and small-to-medium applications.
Embeddable, runs in-process or as a server, simple Python/JS SDKs. Great for getting started quickly; may need migration for large-scale production.
Start with Chroma for prototypes. Move to Pinecone or Weaviate when you need scale, SLAs, and production monitoring. Consider pgvector if you're already on PostgreSQL and vector count stays under ~1M.