Apache Cassandra and MongoDB are two popular NoSQL databases that excel at handling large amounts of data and scaling to meet demand, making them ideal for storing and querying the massive volumes of data needed for AI use cases. Cassandra employs a wide-column data model and is designed for high-throughput, globally distributed, write-heavy workloads where availability and scalability are critical, while MongoDB uses a document data model and is suitable for document-centric, flexible-schema use cases that benefit from developer agility and strong consistency. Both databases offer custom versions of SQL, but Cassandra's peer-to-peer design virtually eliminates downtime, while MongoDB's primary/secondary design provides a single point of failure. Cassandra excels in terms of concurrency and scalability, but compromises on consistency, whereas MongoDB offers broad programming support and is highly consistent by default. DataStax Astra DB, built on top of Cassandra, provides a serverless solution that offers better performance, lower cost, and ease of administration compared to traditional on-premises deployments or cloud-based services like MongoDB.