Why Vespa
Vespa unifies retrieval and machine-learned ranking in a single scalable platform, built to power the most demanding AI applications at speed and scale.
Modern enterprises don’t fail at AI because of models. They fail because their data infrastructure can’t keep up.
When data is fragmented across formats, and retrieval and ranking are spread across disconnected systems, the result becomes slow, brittle pipelines that can’t deliver relevant context in real time.
This friction shows up quickly when scaling use cases like RAG, recommendations, and intelligent search, where latency, freshness and relevance are critical.
This is where Vespa, the leading AI Search Platform, comes in.
Vespa is purpose-built for AI applications that demand both quality and scalability. It delivers AI search at scale across vectors, tensors, text, and structured data together. Multi-phased, machine-learned ranking and inference run directly on the data, eliminating fragmented pipelines.
Proven in production for over a decade, Vespa powers mission-critical systems at companies like Perplexity, Spotify, Yahoo, and Vinted, handling hundreds of thousands of queries per second across massive datasets.
Vespa is the only AI search platform that gives teams full control over their data models, ranking logic, and infrastructure without sacrificing performance or cost efficiency.
Unlike legacy search stacks or vector-only databases, Vespa was built to:
This is the foundation required to move AI from experimentation to speed and accuracy at scale.
Vespa co-locates data and computation, running ranking and inference directly on content nodes to avoid slowdowns as data and ranking complexity grow.
Relevance is not a black box in Vespa. Teams can fully configure query pipelines, define custom schemas, and inspect, tune, or explain ranking behavior in production, a key reason teams consider Vespa the best search platform for GenAI.
Vespa was initially developed at Yahoo to solve the challenge of applying machine-learned ranking and real-time personalization at internet scale. Today, it supports over 150 mission-critical applications, handles more than 800,000 queries per second, and serves nearly one billion users globally, powering one of the largest-scale deployments of real-time AI in the world.
Vespa is the engine behind Perplexity’s retrieval-augmented generation (RAG) system, delivering low-latency, contextually relevant answers across billions of documents. It enables dense and sparse retrieval, approximate nearest neighbor (ANN) search, and large-scale ranking using expressive tensor models—executed directly on stored data for maximum efficiency.
Learn how innovators like Perplexity, Spotify, and Yahoo are using Vespa to serve billions of queries and scale their AI systems efficiently.
Compare Vespa to Elasticsearch, Solr, and others. See how Vespa’s unified approach outperforms split-stack architectures in complex AI workflows.
Read what independent analysts are saying about Vespa’s unique position in the AI infrastructure ecosystem.
See how Vespa performs in real-world workloads, including latency, throughput, and cost efficiency at scale, benchmarked against Elasticsearch.
Discover trusted partners with Vespa expertise who can help deliver your AI search applications and support your journey from design to deployment.
The AI Search Platform behind Perplexity, Spotify, and Yahoo. Vespa.ai unifies search, personalization, and recommendations with the accuracy and performance needed for generative AI at scale.
