Vector search, also known as semantic vector search, leverages deep learning and machine learning to improve the relevance and speed of search results by interpreting the meaning, intent, and context of data in text, audio, and images. This technology transforms search queries into numerical representations, or vectors, which are then compared to datasets to find the most relevant results, allowing for more accurate product recommendations and personalization. Companies like Spotify and Home Depot have implemented vector search to enhance user experience and drive engagement by providing faster and more relevant search outcomes. Elastic, a leader in search-powered technology, offers an integrated solution for deploying vector search, enabling businesses to customize search experiences to meet specific needs and improve performance. By combining semantic search techniques with algorithms such as approximate nearest neighbor, Elastic allows for efficient processing of large data volumes, thereby enhancing search experience and reducing costs. As customer expectations for rapid and relevant search results grow, vector search presents an opportunity for businesses to meet and exceed these demands, ultimately contributing to business success.