Pinecone
Free tierThe fully managed vector database built for knowledgeable AI — fast retrieval, accurate results, lower costs.
Free tier available·All audiences·API available
Key strengths
Writes are instantly searchable with <100ms acknowledgmentAutomatic indexing with no manual tuning requiredConsistent low-latency queries at billion-vector scale (31ms p50 at 1B vectors)Up to 95% reduction in token consumption per AI agent via semantic cachingEnterprise-grade security: SOC 2 Type II, HIPAA, GDPR, ISO 27001, CMEK, SSO, RBAC
Free tier + paid plans
San Francisco, USA
Founded 2019
No ratings yet
- RAG (Retrieval-Augmented Generation) — embed documents with models like OpenAI
text-embedding-3-largeor Cohere, upsert into Pinecone, and retrieve top-K chunks at query time to inject into LLM context windows. - Multi-tenant agent memory — use one index with up to 1.7M namespaces to provide isolated, per-agent vector stores without provisioning separate infrastructure.
- Billion-scale ANN search — run approximate nearest-neighbor queries across 1B+ dense vectors at 31ms p50 with automatic index rebalancing and no manual HNSW/IVF tuning.
- Metadata-filtered vector search — apply structured filters (e.g.,
category == "legal" AND date > 2024-01-01) evaluated inside the query engine to avoid post-filtering latency overhead. - Semantic cache layer — store past LLM prompt-response pairs as vectors; incoming queries that exceed a similarity threshold are served from cache, cutting token consumption by 70-95%.
- Hybrid search — combine dense and sparse vector indexes (BM25 + embeddings) for lexical + semantic retrieval in a single query.