Qdrant vs Rockset Choosing the Right Vector Database for Your AI Apps

Qdrant and Rockset are two vector databases designed to store and query high-dimensional vectors, which encode complex information such as the semantic meaning of text or product attributes. Qdrant is a purpose-built vector database optimized for performance and flexible data modeling, allowing it to handle high-dimensional vector data and combine vector similarity with metadata filtering. It uses the HNSW algorithm for indexing and supports complex queries like Facet API for aggregation and counting unique values in the data. Qdrant's query language works seamlessly with vector search and supports trade-offs between search precision and performance. Rockset is a real-time search and analytics database that supports structured and unstructured data, including vector embeddings, and has Converged Indexing built on mutable RocksDB for efficient updates of vectors and metadata. It can handle high velocity event streams and change data capture feeds with 1-2 second latency. Both databases have different strengths in vector search - Qdrant is great for pure vector search performance and AI-focused features, while Rockset excels in real-time processing and SQL-based analytics. Choosing between the two depends on technical requirements, such as data update frequency, query patterns, and the need for real-time analytics alongside vector search. Thorough benchmarking with actual datasets and query patterns is key to making a decision between these powerful but different approaches to vector search in distributed database systems.