M4A

Pinecone

The vector database for production AI applications

freemiumproductionmanagedserverlessscalablecloudproduction
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Memory Types

semantic, contextual

Integrations

langchain, llamaindex, openai, cohere, huggingface


Overview


Pinecone is a fully managed vector database designed for production AI applications. Founded in 2019 and having raised over $100 million in funding, Pinecone pioneered the managed vector database category. It provides fast, scalable similarity search for machine learning applications without the operational overhead of managing infrastructure.


Pinecone's serverless architecture automatically scales to handle billions of vectors while maintaining low latency. The platform is built specifically for production use cases, with enterprise-grade security, monitoring, and support. It has become one of the most popular choices for companies building RAG applications, recommendation systems, and semantic search.


Key Features


  • **Serverless Architecture**: Automatic scaling without infrastructure management
  • **High Performance**: Sub-100ms queries at billion-vector scale
  • **Hybrid Search**: Combines dense and sparse vectors for better accuracy
  • **Metadata Filtering**: Filter results by metadata attributes
  • **Live Index Updates**: Real-time updates without downtime
  • **Multi-Cloud**: Available on AWS, GCP, and Azure
  • **Namespaces**: Logical partitioning within indexes
  • **SOC 2 Compliant**: Enterprise-grade security and compliance

    • When to Use Pinecone


    Pinecone is ideal for:

  • Production AI applications requiring reliable vector search
  • Teams wanting managed infrastructure without DevOps overhead
  • Applications needing to scale to billions of vectors
  • Companies requiring enterprise SLAs and support
  • RAG applications with real-time data updates
  • Recommendation engines and semantic search systems

    • Pros


  • Fully managed with zero operational overhead
  • Excellent performance and scalability
  • Strong reliability and uptime guarantees
  • Great documentation and developer experience
  • Wide ecosystem integration
  • Enterprise-ready security and compliance
  • Active development and feature releases

    • Cons


  • More expensive than self-hosted alternatives
  • Vendor lock-in concerns with proprietary platform
  • Free tier is limited for production use
  • Less flexibility than open-source solutions
  • Storage costs can be significant at scale
  • No on-premise deployment option

    • Pricing


  • **Starter Plan**: Free tier with 100k vectors, 1 pod
  • **Standard**: Pay-as-you-go starting at $70/month per pod
  • **Enterprise**: Custom pricing with volume discounts and SLAs
  • **Serverless**: Usage-based pricing, ~$0.096 per million queries

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