LanceDB is an open-source, AI-native multimodal lakehouse designed for billion-scale vector search. Built on the Lance columnar format, it combines embedded simplicity with cloud-scale performance. LanceDB's disk-based architecture with compute-storage separation enables up to 100x cost savings compared to memory-based solutions while supporting multimodal data (text, images, video, audio).

Redis provides vector similarity search through Redis Stack (RediSearch module), enabling low-latency semantic search and RAG applications. As an in-memory database, Redis excels at small-to-medium scale vector workloads requiring ultra-low latency. It integrates vector search with Redis's core data structures, making it ideal for real-time AI applications, semantic caching, and RAG systems.