Apache Cassandra vs. Redis: Choosing the Right Vector Database for Your AI Applications

Apache Cassandra and Redis are two popular options for handling vector data in AI applications. Both databases have evolved to include vector search capabilities, but they cater to different use cases and requirements. Cassandra is ideal for large-scale distributed data with strong consistency and fault tolerance across multiple data centers, while Redis excels in scenarios demanding high-speed, real-time vector operations, particularly for datasets that can fit in memory. The choice between these technologies ultimately depends on specific project requirements, such as dataset size, the need for real-time processing, scalability needs, and the complexity of your data model.