SingleStore and Qdrant are two different vector databases that cater to distinct use cases. SingleStore is an all-in-one database that embeds vector search with SQL, making it suitable for complex enterprise workloads that require a mix of transactional and analytical capabilities. Its distributed architecture allows it to handle large datasets and mixed data types, making it ideal for high concurrency applications. Qdrant, on the other hand, is specifically designed for similarity search and machine learning applications, offering flexible data modeling, robust security features, and strong integrations with popular ML frameworks. It's better suited for AI-driven workflows that require high-performance search and filtering. The choice between SingleStore and Qdrant depends on the specific use case, data types, and scalability requirements of the application. Thorough benchmarking with actual datasets and query patterns is crucial to make an informed decision.