Apache Cassandra and Zilliz Cloud are two notable vector databases designed to handle complex data structures like vector embeddings essential for AI applications. Apache Cassandra is an open-source, distributed NoSQL database system with high scalability, fault tolerance, and ability to operate in distributed environments with minimal downtime or performance degradation. It has integrated vector search capabilities through Storage-Attached Indexes (SAI) that provide column-level indexing for vector data types and support both query and content indexing. On the other hand, Zilliz Cloud is a fully managed vector database service built on top of the open-source Milvus engine, offering automatic performance optimization, hybrid search across different data types in single queries, flexible consistency levels, and scalable infrastructure. When choosing between these two databases, consider factors such as infrastructure preference, existing tech stack, team expertise, and specific requirements for vector search and scaling. Thorough benchmarking with your own datasets and query patterns will be key to making a decision between these powerful but different approaches to vector search in distributed database systems.