TiDB and Deep Lake are two vector databases designed for efficient similarity searches in high-dimensional vectors, which encode complex information such as text, images, or product attributes. TiDB is a distributed SQL database with hybrid transactional and analytical processing capabilities, MySQL compatibility, and supports vector search through external libraries and plugins. In contrast, Deep Lake is a specialized database built for handling unstructured data like images, audio, video, and other multimedia types, optimized for high-speed querying of large-scale embeddings using the Hierarchical Navigable Small World (HNSW) index. Key differences between TiDB and Deep Lake include search methodology, data type support, scalability, flexibility, integration with AI frameworks, ease of use, pricing, security, and suitability for specific use cases such as hybrid workloads, unstructured data, and machine learning applications. Ultimately, the choice between TiDB and Deep Lake depends on the project's core needs, including the type of data, performance requirements, and desired level of customization.