Reimagining the vector database to enable knowledgeable AI

This post details the architectural evolution of Pinecone into a serverless vector database, emphasizing the decoupling of storage from compute. It addresses limitations of traditional scatter-gather architectures and in-memory indexing (like HNSW/Faiss) for use cases requiring cost-efficient, on-demand, or infrequently accessed searches over large datasets. The new architecture aims to enable true paging from persistent storage, improving cost-effectiveness for scenarios like on-demand labeling and RAG. It also discusses challenges and solutions for multitenant search, highlighting the need to avoid scatter-gather and effectively collocate/cache namespaces. The post contrasts the new architecture with previous pod-based and collection-based approaches, emphasizing improved freshness, elasticity, and cost at scale.