{"name":"Pinecone","slug":"pinecone","category":"vectordb","type":"cloud","website":"https://www.pinecone.io","pricing":"freemium","pricing_tiers":["Free (1 starter index, 2GB storage)","$50/mo Standard (usage-based)","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript","go","java"],"frameworks":["langchain","llamaindex","vercel-ai","openai-agents","haystack"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":true,"mcp_server":true,"serverless":true},"compliance":["soc2","hipaa","gdpr","iso27001"],"best_for":"Production RAG and agent memory at scale — serverless billing, generous free tier, and a managed experience with zero ops","limitations":"Cloud-only with no self-hosted option; vendor lock-in on proprietary index format; advanced features like dedicated read nodes add cost quickly at scale","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://docs.pinecone.io","pricing":"https://www.pinecone.io/pricing","changelog":"https://docs.pinecone.io/release-notes"},"feature_labels":{"hybrid_search":"Combined dense vector + sparse/keyword (BM25) search in one query","metadata_filtering":"Pre/post filtering by structured metadata — JSON payloads, tags, dates","multi_tenancy":"Native namespace or tenant isolation for per-user or per-agent vector spaces","managed_embeddings":"Built-in embedding generation at ingest and query time — no separate embedding service required","mcp_server":"Official Model Context Protocol server exposing vector operations as agent tools","serverless":"Pay-per-use scaling with separated storage and compute"},"comparisons":[{"slug":"chroma-vs-pinecone","title":"Chroma vs Pinecone","vs":"chroma"},{"slug":"elasticsearch-vs-pinecone","title":"Elasticsearch vs Pinecone","vs":"elasticsearch"},{"slug":"milvus-vs-pinecone","title":"Milvus vs Pinecone","vs":"milvus"},{"slug":"mongodb-atlas-vs-pinecone","title":"MongoDB Vector Search vs Pinecone","vs":"mongodb-atlas"},{"slug":"pgvector-vs-pinecone","title":"pgvector vs Pinecone","vs":"pgvector"},{"slug":"pinecone-vs-qdrant","title":"Pinecone vs Qdrant","vs":"qdrant"},{"slug":"pinecone-vs-weaviate","title":"Pinecone vs Weaviate","vs":"weaviate"}],"body":"# Pinecone\n\nPinecone is the most established managed vector database and the default choice when teams want a production-grade RAG store without running infrastructure. Its second-generation serverless architecture separates storage from compute, which keeps costs predictable for the bursty, long-tail workloads typical of agent applications.\n\nFor AI agents, Pinecone's standout capabilities are namespaces for per-tenant isolation, hybrid search with sparse-dense vectors, integrated embedding models so you can ingest raw text without a separate embedding pipeline, and a hosted MCP server (via Pinecone Assistant) that exposes retrieval as an agent tool.\n\nThe main tradeoff is that Pinecone is cloud-only — there is no self-hosted SKU at any price tier. Teams with strict data residency requirements or air-gapped deployments need to look elsewhere. The proprietary index format also means migrations off Pinecone require a full re-embedding and reindex.\n\n**Agent-specific features:**\n- Namespaces for cheap, isolated per-user or per-agent vector spaces inside a single index\n- Hybrid search via sparse-dense vectors with configurable alpha weighting\n- Integrated inference (`text-embedding-3-small`, `multilingual-e5-large`) to avoid wiring up a separate embeddings service\n- Pinecone Assistant exposes a hosted MCP server for managed RAG over uploaded files\n- Serverless billing scales to zero for idle namespaces — useful for per-end-user agent memory"}