Vector similarity is a critical component in AI applications, enabling the transformation of unstructured data into numerical vectors to capture semantics, which facilitates the retrieval of relevant information across various domains such as e-commerce, music, and text analysis. This technique measures how closely related data points are in a high-dimensional vector space, crucial for applications like semantic search, recommendation systems, and image and text analysis. Despite its utility, implementing vector similarity at scale presents challenges such as the curse of dimensionality, scalability, efficiency, and security, which are addressed by specialized systems like Qdrant. Developed in Rust, Qdrant offers advanced features including horizontal scaling, sharding, and role-based authentication to handle high-dimensional data efficiently, making it suitable for large-scale AI applications. The continuous evolution of vector similarity technology promises future advancements in indexing, real-time search, and privacy-preserving techniques, further expanding its capabilities in AI.