pgvector vs Clickhouse: Choosing the Right Vector Database for Your AI Apps

A vector database is a type of database specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data such as text, images, or product attributes. They enable efficient similarity searches and play a crucial role in AI applications like e-commerce recommendations, content discovery platforms, cybersecurity anomaly detection, medical image analysis, and natural language processing (NLP) tasks. pgvector is an extension for PostgreSQL that adds support for vector operations, allowing users to store and query vector embeddings directly within their PostgreSQL database. It supports exact and approximate nearest neighbor search with HNSW and IVFFlat indexing methods. ClickHouse is an open-source OLAP database for real-time analytics with full SQL support and fast query processing. It has vector search functionality through SQL functions, including exact matching with parallel processing and experimental Approximate Nearest Neighbour (ANN) indices. When choosing between pgvector and ClickHouse for vector search, consider factors such as search methodology, scalability and performance, flexibility and customization, integration and ecosystem, ease of use, cost, and security. Use pgvector when you're already using PostgreSQL and want to add vector search to your existing relational database setup, while ClickHouse is better for very large vector datasets with high-performance analytical processing and vector search needs.