Elastic's zero-shot search offers rapid deployment and cost-effectiveness through its Elasticsearch Relevance Engine (ESRE) powered by the Elastic Learned Sparse Encoder (ELSER) model. This approach is suitable for general-purpose data and applications requiring immediate enhancements, but it has limitations such as lack of domain specificity, inability to customize, potential for non-deterministic responses and risk of hallucinations. SingleStore, on the other hand, provides a flexible and agnostic platform that allows organizations to choose their favorite public or private AI model, connect it easily and efficiently through an API call, and tailor their search functionality precisely to their requirements. This approach excels in scenarios requiring customization and complex analytics, offering scalability, adaptability, and advanced analytics capabilities. While SingleStore demands more initial investment, the long-term benefits of customized, highly relevant search capabilities can significantly impact efficiency, user satisfaction, and competitive advantage.