Vespa.ai is a platform for developing and running real-time AI-driven applications that include search, recommendation, personalization and conversational AI. The Vespa platform provides retrieval-augmented generation (RAG), vector database, support for machine learning, large language models (LLM), and vision language models (VLM), efficiently managing data, inference, and logic to support applications with large data volumes and high concurrent query rates. Vespa supports precise hybrid search, ranking, and inference that combines data types, including multiple vectors, text search, and unstructured and structured data. Vespa is available as a managed service and open source. Designed for low latency and scalability–typically over 100K queries per second—it is the preferred solution for large-scale deployments like Perplexity, Spotify, Wix, and Yahoo. Vespa achieves high performance and scalability through its distributed architecture, efficient query processing, and advanced data management. By distributing data, queries, and machine learning models across multiple nodes, Vespa ensures scalability and fault tolerance, which are crucial for large-scale deployments. It supports horizontal and vertical scaling, allowing additional nodes to be added to increase capacity and performance. Vespa's optimized low-latency query execution, real-time data updates, and advanced ranking algorithms enable sophisticated and efficient searches. Computation is performed where the data is stored, eliminating expensive data transfer costs, increasing performance through local execution and aligning with corporate privacy and security policies.