Apache Cassandra vs MongoDB: Choosing the Right Vector Database for AI Applications

Apache Cassandra and MongoDB are two leading NoSQL databases known for their scalability and flexibility, but they have fundamental differences that influence their suitability for different workloads. Both databases can handle vector search tasks, but specialized vector databases like Milvus and Zilliz Cloud offer better performance for large-scale, high-performance vector search tasks. Apache Cassandra is better for environments requiring high availability, fault tolerance, and massive scalability, particularly for write-heavy workloads. MongoDB offers more flexibility in handling unstructured data, real-time performance, and ease of use, making it a strong choice for AI applications that require similarity searches, recommendation engines, or NLP.