SingleStore and Zilliz Cloud are two vector databases designed for storing and querying high-dimensional vectors, which encode complex information such as text semantic meaning, image visual features, or product attributes. Both offer efficient similarity searches, enabling advanced data analysis and retrieval in AI applications like e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, natural language processing tasks, and Retrieval Augmented Generation (RAG) for large language models. However, SingleStore has vector search built into its SQL database, supporting multiple search algorithms, while Zilliz Cloud uses AutoIndex to automatically choose the best indexing strategy for your data. SingleStore excels in scenarios that combine traditional SQL operations with vector similarity search, whereas Zilliz Cloud is well-suited for pure AI-driven applications requiring flexible vector search capabilities without extensive SQL integration. The choice between these technologies depends on factors like application architecture and requirements, existing technology stack, need for automated management, and specific performance requirements for the use case. Thorough benchmarking with actual datasets and query patterns will be key to making a decision between these two powerful approaches to vector search in distributed database systems.