Vector databases are designed to efficiently handle high-dimensional data, making them ideal for anomaly detection tasks in various domains such as fraud detection, network security, quality control, and healthcare. They differ from traditional databases in their data structure, optimization, storage efficiency, and scalability. Vector databases excel in storing, managing, and querying high-dimensional vectors, which are essential in identifying anomalies in real-time. The curse of dimensionality, sparsity, and overfitting pose significant challenges to anomaly detection in high-dimensional data, but vector databases address these issues through efficient similarity searches, real-time analytics, scalability, geospatial applications, and data exploration and visualization capabilities. By leveraging vector databases like SingleStoreDB, organizations can unlock the full potential of their data and stay ahead in the era of data-driven decision-making.