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Building something real?

You'll need to co-locate vectors, metadata and content on the same item on the same node, run inference there to achieve scalable performance, and seamlessly scale this across nodes to handle any amount of data and traffic. Vespa does all this for you so you can focus on building your application.

What is Vespa used for?

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Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time. Together with Vespa's proven scaling and high availability, this empowers you to create production ready search applications at any scale, and with any combination of features. more

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Recommendation and personalization

Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. Vespa lets you build applications which does this online, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. This makes it possible to make recommendations specifically for each user or situation, using completely up to date information. more

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Conversational AI

Large language models are revolutionary, but their usefulness for real tasks is limited by their lack of memory, trustworthiness, specific knowledge, and ability to reason in many steps. To make more useful agents we must give the models the ability to store and search vector and text data in real time, and orchestrate many such operations to carry out a task. Vespa is the ideal platform for this, as it integrates all these building blocks in scalable form. more

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Semi-structured navigation

Applications such as e-commerce use a combination of structured data and text and need to provide structured navigation - grouping data dynamically for navigation and filtering - in combination with search and recommendation. Vespa provides all the features required for this with great performance, which makes it possible to realize functionally complete applications leveraging structured data on a unified architecture. more

How other companies use Vespa to build their business


When Spotify wanted to implement semantic search using vector embeddings, they turned to Vespa for its support for fast ANN search in combination with all the other needs of real applications, such as using ranking functions combining vector similarity with other signals. Read more.


Yahoo runs about 150 Vespa applications, including selecting the personalized content on all content pages in real time and the targeted ads of one of the worlds largest ad exchanges. In total these applications serve close to a billion users, at a rate of 600 000 queries per second.


OkCupid chose Vespa over Elasticsearch for their solution to find matches for users in real time, due to Vespa's automatic data management, flexible ranking, and support for adding new fields for filtering and sorting quickly without refeeding all data. Read more.

Get Started today

Vespa lets you do selection, organization and machine-learned model inference over billions of constantly changing data items, serving thousands of queries per second with latency below 100 milliseconds.

Run it yourself, or try the Vespa cloud service for free.

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