Pinecone

Pinecone is a fully managed, serverless vector database that lets developers store, index, and search high-dimensional embeddings at scale for AI applications such as semantic search, recommendation, and retrieval-augmented generation (RAG).

Pinecone is a managed vector database purpose-built to be the long-term memory and knowledge infrastructure for AI applications. Founded in 2019 by Edo Liberty (former research director at AWS, where he led the creation of Amazon SageMaker), Pinecone created the dedicated vector database category and offers a fully managed, object-storage-backed serverless service that removes the operational burden of running vector search infrastructure. It supports dense, sparse, and full-text indexes through a single API, enabling semantic, keyword, and hybrid retrieval with low-latency queries, metadata filtering, and reranking. The platform now extends beyond storage into a broader 'knowledge engine' (Pinecone Nexus) and includes Pinecone Inference (hosted embedding and reranking models) and Pinecone Assistant. According to the company it serves more than 9,000 customers and 800,000 developers worldwide, including Notion, Gong, Microsoft, Adobe, and ZoomInfo, and is used widely as core RAG and agent retrieval infrastructure.

Category

Infrastructure & Cloud

Pricing

Freemium, Usage-based, Subscription

Target Market

Enterprise Developers, Data Scientists, ML Engineers, CTOs, AI/Platform Engineers

Founded

2019

Headquarters

New York City, USA

Customers

9,000+ customers and 800,000+ developers

• ✓
  Serverless vector database

  Object-storage-backed serverless architecture that automatically scales storage and compute without managing infrastructure.

• ✓
  Hybrid search

  Supports dense, sparse, and full-text indexes through one API to combine semantic and keyword search in a single query.

• ✓
  Metadata filtering

  Attach key-value metadata to vectors and filter queries to retrieve only records matching specified conditions.

• ✓
  Pinecone Inference

  Hosted embedding and reranking models accessible via API so teams can generate and rerank vectors without managing separate model infrastructure.

• ✓
  Pinecone Assistant

  Managed service for building grounded, RAG-based assistants over uploaded files with built-in retrieval and context management.

• ✓
  Enterprise security and reliability

  Offers a 99.95% uptime SLA, encryption in transit and at rest, RBAC, SSO, private networking, and SOC 2, GDPR, and HIPAA compliance.

• •
  Retrieval-augmented generation

  Store document embeddings and retrieve relevant context to ground LLM responses and reduce hallucinations.

• •
  Semantic and hybrid search

  Power low-latency similarity search across millions to billions of vectors combining semantic meaning and exact keyword matching.

• •
  AI agent memory and recommendations

  Serve as scalable long-term memory and real-time recommendation infrastructure for AI agents and personalization systems.

Paid plans (Standard, Enterprise) use usage-based pricing for read units, write units, and storage above monthly minimums. A Builder plan (~$20/mo flat) and annual-commit discounts are also offered. Prices reflect October 2025 structure.

This page was written from 9 sources, 4 on domains other than pinecone.io.

1. 1.pinecone.io — companyvendor
2. 2.pinecone.io — productvendor
3. 3.pinecone.io — series bvendor
4. 4.pinecone.io — announcing vector databasevendor
5. 5.prnewswire.com — pinecone founder edo liberty to spearhead pinecones growing
6. 6.docs.pinecone.io — understanding cost
7. 7.velodb.io — pinecone vector database
8. 8.pinecone.io — customersvendor
9. 9.myscale.com — pinecone vs weaviate performance analysis vector databases