Vector data, which is generated through embedding models that convert raw data into vector embeddings, is becoming increasingly valuable due to its potential to enhance various applications such as search engines, recommendation systems, and chatbots. These vectors, akin to the neural representations used by the brain for interpretation, enable similarity searches that mimic human pattern recognition and contextual understanding. Despite their potential, vector data requires unique indexing and search algorithms involving geometric relationships, making it challenging to implement at scale. This complexity has limited widespread adoption to companies with significant resources like Google and Amazon. However, the emergence of new tools and solutions aimed at simplifying the deployment and management of vector data is making it more accessible to a broader range of companies, suggesting a growing impact of vector data in technology applications.