LanceDB and Deep Lake are two vector databases designed to store, manage, query, and retrieve high-dimensional vectors, which encode complex information such as semantic meaning of text or product attributes. LanceDB uses IVF_PQ search algorithm for efficient similarity searches, while Deep Lake employs HNSW index based on the Hnswlib package for fast querying over 35 million embeddings in under one second. Both databases offer hybrid search capabilities that combine vector similarity searches with metadata filtering. LanceDB is known for its simplicity, cost-effectiveness, and flexible deployment options, making it suitable for lightweight vector search with strong hybrid capabilities. In contrast, Deep Lake excels in managing large multimedia datasets with version control and is particularly strong for RAG applications. The choice between these two powerful but different approaches to vector search depends on specific use cases, such as the need for metadata filtering or the size of the dataset. VectorDBBench, an open-source benchmarking tool, can help users evaluate and compare different vector database systems based on their own datasets and query patterns.