M4A

Qdrant

High-performance vector similarity search engine with extended filtering

freemiumproductionopen-sourcerusthigh-performanceself-hostedmanaged
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Memory Types

semantic, contextual

Integrations

langchain, llamaindex, openai, cohere, haystack


Overview


Qdrant is a high-performance, open-source vector similarity search engine written in Rust. It's designed for production-ready semantic search and similarity matching with a focus on performance, scalability, and developer experience. Qdrant excels at combining vector similarity search with complex filtering, making it ideal for applications requiring precise result filtering.


Built with modern systems programming practices, Qdrant offers exceptional performance and low resource consumption. The platform provides both self-hosted and managed cloud options, with a rich REST and gRPC API that makes integration straightforward. Qdrant's advanced filtering capabilities and payload indexing set it apart from simpler vector databases.


Key Features


  • **Extended Filtering**: Rich filtering with nested structures and conditions
  • **Payload Indexing**: Fast filtering on metadata without compromising vector search
  • **Quantization**: Reduces memory usage while maintaining accuracy
  • **Sharding & Replication**: Built-in distributed deployment support
  • **REST & gRPC APIs**: Multiple API options for different use cases
  • **On-Disk Storage**: Efficient storage with mmap for large datasets
  • **Snapshot & WAL**: Point-in-time recovery and durability guarantees
  • **Multi-Vector Support**: Store multiple vectors per document

    • When to Use Qdrant


    Qdrant is ideal for:

  • Applications requiring complex filtering alongside vector search
  • High-performance requirements with limited resources
  • Production systems needing low latency and high throughput
  • Self-hosted deployments with full control
  • Applications with large-scale vector datasets
  • Systems requiring strong consistency guarantees

    • Pros


  • Excellent performance and low resource usage
  • Advanced filtering capabilities beyond most vector DBs
  • Open-source with permissive Apache 2.0 license
  • Great documentation and API design
  • Both cloud and self-hosted options
  • Active development and responsive community
  • Written in Rust for memory safety and speed
  • Comprehensive SDK support (Python, Go, Rust, JS)

    • Cons


  • Smaller ecosystem compared to Pinecone
  • Managed cloud is newer with fewer regions
  • Complex deployments may require Rust knowledge
  • Less integrated with some LLM frameworks
  • Smaller company with less enterprise support
  • Community size smaller than established databases

    • Pricing


  • **Open Source**: Free, self-hosted
  • **Free Tier**: 1GB cluster on Qdrant Cloud
  • **Standard**: Starting at $25/month for managed cloud
  • **Enterprise**: Custom pricing with SLAs and support

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