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
Case Study: E-commerce Product Search
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