Recommendation engines have become critical in the digital era, enhancing user experiences and increasing revenue across various industries, from e-commerce to media streaming. These systems, which analyze user data to provide personalized suggestions, have evolved significantly since their early, rule-based iterations, now leveraging advanced machine learning and deep learning techniques. Businesses can implement robust recommendation systems using CockroachDB’s distributed SQL architecture, which offers strong consistency and native vector capabilities for real-time similarity searches and low-latency queries. By employing methods like content-based, collaborative, and hybrid filtering, companies can offer tailored recommendations, improving customer satisfaction and engagement. CockroachDB supports these functionalities through features like vector indexing and hierarchical K-means trees, facilitating efficient and scalable AI-driven applications in a highly distributed environment.