AI Solutions
Deliver real-time, intelligent search and recommendation that engage users, increase conversions, and scale effortlessly—from retrieval to ranking, all in one platform.
Many engineering teams are finding that their production systems can’t keep pace with the growing ambition of AI-powered products. Vector databases hit scalability limits. Traditional search engines can’t blend vector, text, and structured signals. And fragmented stacks, where retrieval, ranking, and inference run in separate systems, introduce latency, increased costs, and operational risk.
These constraints slow innovation and make it harder to deliver the real-time, personalized, and context-aware experiences users now expect.
Vespa solves these challenges in a single, unified platform. It combines full-text, vector, and tensor search to support hybrid retrieval, advanced ranking, and on-node model inference at scale. The result: production systems that perform at internet scale, enabling faster iteration, richer user experiences, and measurable business impact.
Originally built to power Yahoo’s large-scale, user-facing systems, Vespa now runs more than 150 live applications serving nearly one billion users—processing over 800,000 queries per second with precision and reliability.
Daniel Doro,
Director of Engineering
In modern products, search is the experience. From recommendations and discovery to conversational AI and generative answers, every interaction depends on retrieval quality, ranking accuracy, and system speed. When these falter, engagement and revenue suffer.
Vespa powers applications where search is part of the product, including eCommerce, market intelligence, AdTech, generative AI assistants and more. Product teams use Vespa to deliver real-time, accurate, and personalized results that directly impact business outcomes.
Because Vespa unifies retrieval, ranking, and inference into a single platform, it eliminates the friction of integrating multiple systems. Teams can focus on innovation, personalization, and new features rather than on operational overhead to transform search into a durable competitive advantage.
Vespa enables search, recommendation, and Retrieval-Augmented Generation (RAG) within a single platform. Developers can combine keyword, semantic, and structured retrieval; apply multi-phase ranking; and run in-place model inference to deliver valuable customer experiences.
This unified design reduces latency, simplifies data pipelines, and makes real-time personalization viable at scale. Vespa bridges retrieval and reasoning, giving teams the infrastructure to deliver more intelligent, context-aware experiences powered by their own data.
Vespa’s distributed architecture is designed for continuous performance under massive load. It handles streaming data ingestion, real-time updates, and hybrid queries across billions of documents with consistently low latency.
With integrated tensor computation and native model execution, Vespa brings machine-learned ranking and inference to where the data lives, eliminating network bottlenecks and external dependencies. The result is an end-to-end AI search stack that delivers speed, accuracy, and scale for the most demanding production workloads.
Deliver relevant ad and content recommendations in milliseconds with Vespa’s scalable, high-performance platform for hybrid retrieval and ranking.
Increase revenue with fast and accurate AI-driven recommendation, personalization, and search.
Real-time decisions, personalized experiences, and fraud detection with AI. Integrated seamlessly, manage risks and deliver tailored insights for stronger customer relationships.
Harness all data across text, images, and research to enable real-time discovery, precise relevance, and scalable RAG for faster drug development, compliance, and scientific innovation.
Deliver GenAI-powered insights over massive proprietary datasets without the performance bottlenecks or cost spikes of scaling RAG.
Vespa provides all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).
Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.
Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.
Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.
Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.
Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.
Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.
Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.
Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.
Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.
By leveraging Vespa, Spotify users can find what they are looking for even if they don’t use specific keywords, making the discovery process more intuitive and personalized.
“Vespa is a battle-tested platform that allows us to integrate keyword and vector search seamlessly. It forms a key part of our AI research solution, guaranteeing both precision and rapidity in streamlining research processes. We highly recommend Vespa for its reliability and efficiency.”
Perplexity chose Vespa.ai as the foundation for its AI Search platform and AI-First Search API because Vespa uniquely integrates retrieval, ranking, and machine-learning inference at scale. Vespa delivers the completeness, freshness, and fine-grained control essential for high-quality RAG.
