Pinecone

Pinecone is a fully managed vector database with no infrastructure to run — it handles index management, scaling, and replication automatically. It supports billion-scale vectors with consistent low-latency search and namespace isolation for multi-tenant RAG systems. We choose Pinecone when scale, reliability, and minimal operational overhead matter most. pgvector is the right choice for lower-scale systems where keeping everything in PostgreSQL simplifies the architecture. Weaviate is preferred when you need built-in hybrid search (dense + sparse) or tight object storage coupling.

A production Pinecone deployment includes: namespace strategy for multi-tenant isolation, metadata filtering to scope queries to relevant subsets, a separate document store (PostgreSQL, S3) for full content retrieval, hybrid search configured for your query patterns, embedding model versioning to handle reindexing when models change, and monitoring on query latency and recall rate. We build ingestion pipelines that keep the index current as source documents are updated or deleted.

Integrating Pinecone into an existing application (ingestion pipeline, query layer, and basic RAG) typically takes 2-4 weeks. A complete production RAG system — document processing, chunking strategy, embedding pipeline, Pinecone integration, LLM orchestration, and evaluation framework — takes 5-8 weeks end-to-end.