PINECONE-SPEC

Pinecone managed vector database SaaS leader.

Definition

Pinecone features: (1) Vector storage: embedding vectors (768 dim ada-002 OpenAI, 1536 dim text-embedding-3-large OpenAI, 384 dim sentence-transformers MiniLM, 4096 dim Mistral, etc.). (2) Index types: HNSW (Hierarchical Navigable Small World) + IVF-PQ (Inverted File Product Quantization) index, Pinecone proprietary optimizations. (3) Metadata filtering: key-value metadata associated each vector, filter at query time (category, source, date, etc.). (4) Namespaces: multi-tenant per index isolation. (5) Hybrid search: combine sparse vectors (BM25, SPLADE) + dense vectors, single fused score. (6) Pinecone Inference: embedded models hosting (host embedding model alongside vector DB, single API call generate embedding + query). Pricing Serverless: $0.085/M read units + $4.00/M write units + $0.33/GB-month storage (us-east-1), ~10x cheaper than Pod-Based traditional. SDKs: Python, Node.js, Java, Go, .NET. Integration: LangChain, LlamaIndex, Vercel AI SDK, OpenAI Assistants API. Customers: Notion AI, Shopify, Brex, Cohere, You.com, Glean.

Origin

Pinecone founded 2019 by Edo Liberty (ex-Yahoo Research, AWS) + Greg Levitan ; Series A $10M 2021 ; Series B $100M 2023 (Andreessen Horowitz lead, $750M valuation) ; Pinecone Serverless launched March 2024.

Example in context

Notion AI uses Pinecone Serverless for RAG search across its ~100M+ user workspace pages: OpenAI text-embedding-3-large embeddings stored + searchable via Pinecone, user query 'find my product roadmap docs' returns top-10 semantically similar pages in <100ms.

Related terms

• Weaviate — OSS competitor.