Pinecone
by Pinecone Systems Inc.
The vector database that makes AI knowledgeable.
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).
At a Glance
- 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
Key Features
- ✓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.
Capabilities
Use Cases
- •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.
Ideal For
Best For
- ✓Retrieval-augmented generation (RAG) for LLM applications
- ✓Production-scale semantic search and recommendation systems
- ✓Long-term memory and knowledge retrieval for AI agents
Integrations
Market & Ratings
9,000+ customers and 800,000+ developers
Market Analysis
Pros
- ✓Fully managed with zero infrastructure maintenance
- ✓Low-latency, high-scale similarity search (handles billions of vectors)
- ✓Developer-friendly API and strong Python/LangChain integration
- ✓Robust enterprise security and compliance
Cons
- ✗Closed-source (no self-hosting option)
- ✗Costs can scale quickly and predictability is a common complaint
- ✗Free Starter tier limits are reached quickly during experimentation
- ✗Standard plan carries a $50/mo minimum commitment
Pricing
Starter
$0
- ✓Free for small applications
- ✓Up to 2 GB storage
- ✓2M write units and 1M read units per month
- ✓AWS us-east-1 region only
- ✓1 project, up to 2 users
- ✓Community support
Standard
From $50/mo minimum
- ✓Pay-as-you-go beyond included $15/mo credits
- ✓Unlimited serverless, inference, and assistant usage
- ✓Choice of cloud and region (AWS, Azure, GCP)
- ✓Multiple projects and users
- ✓User and API key RBAC
- ✓Backup and restore
Enterprise
From $500/mo minimum
- ✓Everything in Standard
- ✓99.95% uptime SLA
- ✓SAML SSO
- ✓Private networking
- ✓Customer-managed encryption keys
- ✓Audit logs and HIPAA compliance
- ✓Pro support included
Dedicated
Contact for pricing
- ✓Custom dedicated infrastructure
- ✓Tailored configuration for high-throughput workloads
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.
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