Takeaways
- Vector databases are optimized for storing and retrieving vector embeddings, which represent high-dimensional semantic data used by AI models.
- Unlike standalone vector indexes, vector databases support CRUD operations, metadata filtering, and real-time updates.
- Serverless vector databases solve key limitations of first-generation systems by decoupling compute and storage, improving scalability and freshness.
- Advanced indexing algorithms accelerate approximate nearest neighbor search without compromising speed or accuracy.
- Access control, fault tolerance, and monitoring are critical operational features that make vector databases suitable for production use.
Summary
Vector databases are purpose-built to store, index, and retrieve vector embeddings, which represent semantic information across many dimensions. Traditional scalar databases can't handle this complexity, making vector databases essential for AI tasks like semantic search, recommendations, and generative applications.
Unlike vector indexes such as FAISS, vector databases support full CRUD operations and metadata filtering. They allow real-time updates and backups, making them ideal for dynamic AI use cases. Modern versions often use serverless architectures that separate compute from storage for scalability and cost efficiency. Features like geometric partitioning and freshness layers ensure fast access to new data.
Vector search relies on Approximate Nearest Neighbor (ANN) algorithms like HNSW, Product Quantization, and Locality-Sensitive Hashing, which speed up similarity searches with acceptable accuracy trade-offs. Similarity is measured using cosine similarity, Euclidean distance, or dot product.
Operational features—like sharding, replication, monitoring, and SDKs—make these databases enterprise-ready. Platforms like Pinecone abstract infrastructure, letting developers focus on AI solutions.