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

Apache Cassandra and Aerospike are two popular distributed NoSQL databases that have evolved to include support for vector search capabilities, making them suitable for AI-driven applications requiring efficient handling of high-dimensional vector data. Both systems leverage their existing strengths while addressing the growing demand for efficient vector data storage and retrieval. Cassandra integrates vector search into its core database using Storage-Attached Indexes (SAI), allowing for flexible schema design with vector data stored alongside other attributes. Aerospike introduces a dedicated vector search layer (AVS) on top of its core database, focusing on low-latency, high-throughput operations. The choice between these two databases largely depends on specific use case requirements, such as data scale and complexity, performance needs, team expertise, and production timeline. Conducting proof-of-concept tests with specific datasets and query patterns is essential in making an informed decision. Additionally, using open-source benchmarking tools like VectorDBBench can assist in evaluating and comparing vector database performance based on actual results.