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DirectoryAI & ML PlatformsVector Databases & RAGPinecone

Pinecone

About Pinecone

Pinecone is a company offering an AI knowledge platform, including Database, Nexus, and Marketplace products. Pinecone was created to provide storage and retrieval infrastructure for building and running AI applications. The company was founded in 2019 by Edo Liberty. Pinecone runs on fully managed infrastructure that scales with usage. The platform supports semantic search applications using vector search and hybrid search.

Products

  • Pinecone Database: A serverless, object storage-based vector database.
  • Pinecone Nexus: A knowledge engine for agents that offloads retrieval, assembly, and reasoning from the LLM to a dedicated knowledge layer.
  • Pinecone Dedicated Read Nodes: Provides dedicated, provisioned infrastructure for queries reserved for an index.
  • Pinecone Assistant: A service that allows building production-grade chat and agent-based applications.
  • Pinecone BYOC: Deploys a zero-access Pinecone environment within a customer's cloud account.
  • Pinecone Inference: Provides access to embedding and reranking models hosted on Pinecone's infrastructure.

Used for

  • Building recommendation engines
  • Building forum answering bots using RAG
  • Supporting knowledge base applications

Who it is for

  • Used by solo developers and small teams.
  • Used for production applications at any scale.
  • Used by organizations requiring mission-critical production applications.
  • Used by organizations requiring the highest level of security and control.

From Pinecone's own website, read 2026-10-23. How we build listings.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Pinecone. It comes from our Vector Database & AI Search buyer guide.

25%
Retrieval Quality & Index Options
ANN algorithms offered (HNSW, IVF/IVF-PQ, DiskANN, GPU/CAGRA), achievable recall@k at your latency budget, quantization for memory savings, and whether the index supports real-time inserts or needs rebuilds
20%
Hybrid Search & Filtering at Scale
Native vector+keyword (BM25 / sparse) fusion, reranking, and — critically — in-graph metadata pre-filtering that holds recall under selective filters rather than degrading to brute force or empty result sets
20%
Scale, Performance & Freshness
Vector ceiling per node and horizontally, p95/p99 latency and QPS under your real filters, multi-tenancy and namespaces, multi-region, and how fast new or updated embeddings become queryable (insert-to-searchable lag)
15%
Operating Model & Lock-In
Managed SaaS vs. self-hosted vs. bring-your-own-cloud, license terms (Apache 2.0 / BSD vs. proprietary), data-export and migration path, and how much sync/ETL you take on if vectors live apart from the source data
10%
Security, Governance & Residency
RBAC and API-key scoping, encryption in transit and at rest, SOC 2 / ISO 27001 / GDPR posture, data residency and VPC/air-gapped options, and inherited governance (e.g. Unity Catalog, platform IAM) where applicable
10%
Developer Experience & Ecosystem
Client SDKs and API ergonomics, LangChain / LlamaIndex / framework integrations, built-in or hosted embedding and reranking, docs quality, and operational maturity (backups, observability, upgrades)

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Quick Facts

www.pinecone.io Pricing page
CategoryAI & ML Platforms
SubcategoryVector Databases & RAG

We publish a detail only when we can point at the page it came from. Know when Pinecone was founded or where it is based? Send it to us, free.

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