The use case: how DeviantArt deploys Vespa
DeviantArt, a Wix subsidiary and online art community, uses Vespa as a single serving layer for search, recommendations and personalized content discovery. It combines machine learning ranking, custom query processing and document enrichment over a very large, constantly changing catalog of artworks.
More reported results
Figures published in Vespa’s case study. They are not independently measured by usedby.
with an average query latency of 12 milliseconds
In the customer’s words
At DeviantArt, discovery is fundamental to the user experience. With hundreds of millions of artworks, rapidly changing content, and highly diverse user interests, we needed a platform capable of combining machine learning, personalization, and real-time serving performance at a very large scale. Vespa gives us the flexibility to integrate custom ranking models, user context, and domain-specific processing within a single high-performance system, which is critical for delivering responsive and relevant experiences across our global community.Vespa users in Media & Publishing
Companies in the same sector, with public evidence.
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Go straight to the source
Based on Vespa’s published case study. We check the public evidence; the results are reported by the vendor.
vespa.ai- Source published
- Aug 5, 2026
- Last checked
- Oct 7, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 1 billionChecked
The figure is on the page; its label is our wording.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of DeviantArt.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- DeviantArt uses VespaChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Today, the deployment manages more than 1 billion documents and serves nearly 4,000 queries per second, with an average query latency of 12 milliseconds.