Apache Cassandra vs Faiss: Choosing the Right Tool for Vector Search

Apache Cassandra and Faiss are two technologies that handle vector data differently. While both can perform vector searches, they approach the task from different angles. Apache Cassandra is a distributed NoSQL database designed to handle large-scale structured data across many servers, ensuring high availability and scalability. It can be extended for vector search through integrations with vector search libraries or custom plugins like the DataStax integration. Faiss (Facebook AI Similarity Search) is an open-source library that provides highly efficient tools for fast similarity search and clustering of dense vectors, designed for large-scale nearest neighbor search in high-dimensional vector spaces. Key differences between the two include their search methodology, data handling capabilities, scalability and performance, flexibility and customization, integration and ecosystem support, ease of use, cost considerations, and security features. Apache Cassandra is suitable when vector search is not the primary focus, while Faiss is a better fit for high-performance vector search tasks. For large-scale, high-performance, and production vector search tasks, specialized vector databases like Milvus and Zilliz Cloud are recommended.