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Vespa is the world’s leading open text search engine and the world’s most capable vector database. In combination with Vespa’s integrated distributed machine-learned model inference for relevance this lets you create search applications with a quality you simply cannot achieve in any other way.

GenAI applications are only as good as the data we surface for them to work with; they need great search relevance. This takes much more than vector similarity—hybrid search, relevance models, and multi-vector representations. Vespa is the only platform which lets you deploy such techniques with no limitations and at any scale.

Recommendation, personalization and ad targeting systems combine retrieval of eligible content with machine-learned model evaluation to select the best data items. Vespa lets you easily build applications that do this at any scale and complexity.

Applications like e-commerce use a combination of structured data and text+images, and need to combine search and recommendation seamlessly with structured navigation. Vespa provides all the features required to do this with great performance, at any scale.

In applications working with personal data, any query will only access a small fraction of the total data, and building indexes would be wasteful – especially with vectors. Vespa provides a special mode – streaming search – which delivers all the industry-leading features of Vespa for personal/private search 20x cheaper than with indexing.