Free plan available

Pinecone

A fully managed vector database for high-performance semantic search and RAG applications.

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Intro

What is Pinecone?

Pinecone is a fully managed, purpose-built vector database designed to take vector search from research to production effortlessly without DevOps. Once you generate vector embeddings from your data, you can store, manage, and search through them using the Pinecone DB to power advanced AI applications like semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG). As a high-performance vector store, Pinecone AI enables organizations to search through billions of items for similar matches in milliseconds, making it a foundational tool for building knowledgeable AI and scalable domain-specific AI agents.

Pinecone at a glance
Free, Standard from $25/mo648K monthly visitsHas free access
Pricing

Pinecone Pricing Plans

Compare Pinecone free options, Pinecone paid pricing plans, and usage notes before you choose the best way to use this AI tool in 2026.

Free, Standard from $25/mo

Free

For trying out and small applications. Includes up to 2 GB storage, 2M write units/mo, 1M read units/mo, up to 5 indexes, 100 namespaces, and access to all embedding models. Pauses after 3 weeks of inactivity.

From $25 / month

For production applications at any scale. Includes $15/mo usage credits. Pay-as-you-go for Serverless, Inference, and Assistant usage ($0.33/GB/mo storage, $4/M write units, $16/M read units). Supports multiple projects, users, backup/restore, and all cloud regions. Never pauses.

From $500 / month

For mission-critical production applications. Includes $150/mo usage credits. Features everything in Standard, plus a 99.95% Uptime SLA, SAML SSO, Private Networking, Customer Managed Encryption Keys, Audit Logs, and included Pro Support.

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For organizations requiring the highest level of security and control. Includes everything in Enterprise along with a Bring-Your-Own-Cloud (BYOC) deployment model and Premium Support.

Pricing updated:Jun 11, 2026

Features

Pinecone AI Features

Fully managed serverless architecture with automated scalingHigh-performance similarity search across billions of items in millisecondsHybrid search combining sparse and dense embeddings for maximum accuracyReal-time indexing for immediate data availability and fresh readsAdvanced metadata filtering and data partitioning via namespacesIntegrated Pinecone Inference for managed embedding and reranking modelsEnterprise-ready security including SOC 2, GDPR, ISO 27001, and HIPAA compliance
Pros & Cons

Pinecone Pros and Cons

Pros

  • Zero-DevOps infrastructure with effortless automatic scaling
  • Low P90 latency (e.g., 150ms for 2.8B vectors) with high recall optimization
  • Robust developer experience with simple API integrations and popular cloud support (AWS, GCP, Azure)
  • Generous free tier for testing and small-scale applications

Limitations

  • Starter plan limits storage up to 2 GB and pauses after 3 weeks of inactivity
  • Advanced enterprise features require a minimum commitment starting at $500/month
  • Certain cloud and region restrictions apply to the free Starter tier (limited to AWS us-east-1)

Pinecone FAQ

Pinecone serves as the long-term memory or vector store for AI models. In a Retrieval-Augmented Generation (RAG) workflow, documents are converted into vector embeddings and stored in Pinecone. When a query is made, Pinecone quickly retrieves the most relevant documentation to provide accurate context for the AI agent or LLM.