A vector database is a specialized type of database designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. Vector databases play a crucial role in AI applications by enabling efficient similarity searches, allowing for more advanced data analysis and retrieval. They are commonly used in e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, natural language processing (NLP) tasks, and Retrieval Augmented Generation (RAG). There are various types of vector databases available, including purpose-built vector databases like Milvus and Zilliz Cloud, vector search libraries such as Faiss and Annoy, lightweight vector databases like Chroma and Milvus Lite, and traditional databases with vector search add-ons. Two popular vector database options are SingleStore and Vespa. SingleStore is a distributed, relational SQL database management system with vector search as an add-on, allowing developers to build complex AI applications using standard SQL syntax while maintaining performance and scale. Vespa is a powerful search engine and vector database that can handle multiple types of searches all at once, making it suitable for unified vector, text, and structured data search. The key differences between SingleStore and Vespa lie in their search methodologies, data handling and storage, scalability, and integration and usage capabilities. SingleStore excels in SQL compatibility and exact vector search, while Vespa is better suited for unified search and auto-scaling. Ultimately, the choice between SingleStore and Vespa depends on the specific technical requirements and organization of the user.