Vector Databases
Databases optimized for storing and querying embeddings — the storage layer behind semantic search and RAG systems.
Vector databases store high-dimensional embedding vectors and enable fast approximate nearest-neighbor search — the foundation of semantic search and RAG pipelines. Popular options include Pinecone, Weaviate, Qdrant, Chroma, and pgvector (PostgreSQL extension). Choosing between managed cloud vector DBs and self-hosted options involves trade-offs in cost, latency, and operational complexity that are now standard engineering decisions at AI-building companies.
Typical time to job-readiness: ~3 weeks.