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Vector Databases

Vector databases are specialized database systems designed to store and efficiently query high-dimensional vectors, which are commonly used in AI and machine learning applications.

When to Use​

  • When you need to store and query large collections of embeddings
  • When you need fast similarity search across millions or billions of vectors
  • When you need to combine vector search with traditional metadata filtering
  • When you need to scale vector operations horizontally

When Not to Use​

  • When your dataset is small (< 100k vectors)
  • When you don't need real-time updates
  • When simple approximate nearest neighbor algorithms would suffice
  • When you're primarily doing exact matching rather than similarity search

Tradeoffs​

  • Cost vs Scale: Higher costs for larger vector collections
  • Accuracy vs Speed: Trade-off between search accuracy and query performance
  • Flexibility vs Performance: Some databases optimize for specific vector operations at the expense of general-purpose functionality
  • Managed vs Self-hosted: Considerations between operational complexity and control

Commercial Implementations​

  • Pinecone

    • Fully managed vector database
    • Strong focus on ease of use
    • Good documentation and support
    • Higher cost per vector

  • Weaviate

    • Open source
    • Can be self-hosted
    • Strong schema support
    • Good for hybrid searches

  • Milvus

    • Open source
    • Highly scalable
    • Rich feature set
    • Steeper learning curve

  • Qdrant

    • Open source
    • Rust-based implementation
    • Good performance
    • Growing community

Common Combinations​

  • RAG (Retrieval Augmented Generation) systems
  • Recommendation engines
  • Image similarity search
  • Semantic search applications
  • Document retrieval systems

A large e-commerce platform implemented a vector database to power their product search and recommendation system:

Challenge​

  • 10M+ products
  • Need for semantic search
  • Real-time updates
  • Complex filtering requirements

Solution​

  • Implemented Pinecone vector database
  • Stored product embeddings generated from images and descriptions
  • Combined vector search with metadata filtering
  • Integrated with existing PostgreSQL catalog

Results​

  • 3x improvement in search relevance
  • 40% reduction in search latency
  • Easier maintenance compared to previous custom solution
  • Improved scalability for holiday season traffic