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Qdrant

It is an open-source vector database built for AI and semantic search applications. It is designed to store, index, and search high-dimensional vectors, often generated by machine learning models (e.g., embeddings from LLMs or image models).

Key Features​

  • High-Performance Search: Utilizes the Hierarchical Navigable Small World (HNSW) algorithm for fast and accurate nearest neighbor searches.
  • Advanced Filtering: Supports filtering during vector search, allowing for more precise results based on metadata.
  • Payload Support: Allows attaching metadata (payload) to vectors, which can be used for filtering and enriching search results.
  • Quantization: Offers vector quantization techniques to reduce memory usage and improve search performance.
  • Scalability: Designed to handle massive datasets, making it suitable for large-scale AI applications.
  • Multi-Language Support: Provides client libraries for Python, Go, Rust, and TypeScript, facilitating integration into various tech stacks.
  • Cloud & On-Premises Deployment: Available as a fully managed cloud service (Qdrant Cloud) and can also be self-hosted using Docker or Kubernetes

Pros​

  • Extremely fast and lightweight (written in Rust).
  • Great performance for large-scale, real-time applications.
  • Supports hybrid search (text + vector).
  • Simple integration with external embedding models.
  • Active open-source community and well-documented.

Cons​

  • No native graph-like relationships (unlike Weaviate).
  • May require more orchestration for complex data workflows.
  • Lacks built-in ML model hosting—you bring your own embeddings.
  • Fewer governance and RBAC features compared to Weaviate.