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    1. Home
    2. Managed Vector Databases
    3. Neon

    Neon

    Serverless Postgres with native pgvector support for vector embeddings and similarity search. Features instant provisioning, autoscaling, and scale-to-zero with separated compute and storage. This is a commercial managed service with free tier.

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    Information

    Websiteneon.com
    PublishedMar 6, 2026

    Categories

    1 Item
    Managed Vector Databases

    Tags

    3 Items
    #Commercial
    #Serverless
    #Postgresql

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    Overview

    Neon is Serverless Postgres built for the cloud with native support for pgvector extension, enabling efficient vector storage and semantic search. Architecture separates compute from storage for serverless features.

    Key Features

    • Serverless Architecture: Separate compute and storage layers
    • Instant Provisioning: Databases ready in seconds
    • Autoscaling: Automatic scaling based on demand
    • Scale to Zero: Automatic pause when idle to reduce costs
    • pgvector Support: Native vector similarity search in PostgreSQL
    • Branch-Based Development: Git-like branching for databases

    Vector Search Capabilities

    • Full pgvector extension support
    • Store embeddings alongside regular data
    • Vector similarity search with SQL queries
    • Compatible with OpenAI and other embedding providers
    • Elasticsearch-grade vector features in Postgres

    Serverless Features

    • Instant Provisioning: No waiting for database setup
    • Autoscaling: Compute scales automatically with load
    • Scale to Zero: No charges when database is idle
    • Branching: Create instant database copies for development
    • Time Travel: Point-in-time recovery

    Integration

    Semantic Kernel

    • Official Neon Serverless Postgres Connector
    • Powers RAG workflows
    • Enables intelligent AI applications

    LangChain

    • Native Neon Postgres integration
    • Vector store for LangChain applications
    • Seamless embedding storage and retrieval

    OpenAI

    • Direct integration with OpenAI embeddings
    • Examples in OpenAI Cookbook
    • Optimized for GPT-powered applications

    Use Cases

    • RAG (Retrieval Augmented Generation) systems
    • Semantic search applications
    • AI-powered search with natural language
    • LLM applications requiring vector memory
    • Multi-modal search combining vectors and structured data

    Enabling pgvector

    CREATE EXTENSION vector;
    

    Available in Neon SQL Editor or via any PostgreSQL client.

    Architecture Benefits

    • Separated Storage: Durable, scalable storage independent of compute
    • Elastic Compute: Scale compute resources independently
    • Efficient Costs: Pay only for actual usage
    • High Availability: Built-in redundancy and failover

    Pricing

    Commercial managed service with free tier:

    • Free Tier: Generous limits for development and small projects
    • Pro Plan: Usage-based pricing for production workloads
    • Enterprise: Custom pricing with SLA and dedicated support

    Detailed pricing available at: neon.com/pricing

    Resources

    • Jupyter notebooks for vector search with OpenAI
    • Comprehensive guides for building intelligent search
    • Active community and documentation