"First, solve search, then use it to solve everything else."
Aravind Srinivas, Perplexity CEO
Perplexity at a Glance
780M+
Queries/month
20%+
Monthly query growth
170M+
Monthly website visits
About Perplexity
Building an AI Answer Engine Starts with Better Search
Perplexity is an AI-powered answer engine that combines web search with large language models to answer questions using current, cited information. Every response depends on retrieving the right information before generation begins.
Unlike traditional search engines, the goal isn't simply finding documents. The goal is finding the most relevant passages, ranking them effectively, and providing the language model with high-quality context to generate an accurate answer.
That retrieval layer is powered by Vespa.
The Challenge
Building an AI Answer Engine Starts with Better Search
Perplexity's goal wasn't simply to search the web. It was to generate accurate, grounded answers supported by citations.
To do that, every question had to retrieve the right information before generation could begin.
The team needed a retrieval platform capable of combining keyword and semantic search, filtering results, ranking individual passages, and continuously indexing new information—all within the latency expectations of a consumer search experience.
Rather than stitching together separate databases, vector stores, ranking services, and model-serving infrastructure, Perplexity looked for a single platform capable of performing retrieval and ranking as one integrated system.
The SOlution
Why Perplexity Chose Vespa
Building an AI answer engine requires more than similarity search. To deliver accurate, grounded answers at the scale and speed users expect, Perplexity needed an AI search platform capable of finding, ranking, and serving the best information before generation begins.
Perplexity built its search infrastructure on Vespa because Vespa unifies retrieval, ranking, filtering, real-time indexing, and machine learning inference in a single distributed serving engine. That gives Perplexity one platform for hybrid retrieval, passage-level ranking, real-time freshness, and integrated machine learning, without stitching together separate systems for each stage of the retrieval pipeline.
Why Perplexity Built AI Search on Vespa
- Search at web scale. Continuously index billions of documents while serving thousands of low-latency queries per second.
- Hybrid retrieval with structured filtering. Combine lexical, semantic, and structured signals within a unified retrieval and ranking pipeline.
- Passage-level retrieval and ranking. Retrieve and rank the most relevant passages within documents—not just entire pages—so the language model receives focused, grounded context.
- Multi-stage ranking. Narrow large candidate sets efficiently with lightweight ranking, then apply richer neural models to the most promising results.
- Real-time updates. Continuously update content, vectors, metadata, and behavioral signals without interrupting live search.
- Machine Learning in the serving layer. Execute ranking models and inference directly within the serving layer, eliminating external orchestration.
- Unified AI search architecture. Bring retrieval, ranking, machine learning inference, indexing, and serving together in a single distributed system.
Perplexity built its search infrastructure on Vespa because modern AI search requires more than vector search alone. It depends on an architecture that combines search at web scale, hybrid retrieval and ranking, fine-grained content understanding, real-time freshness, and integrated machine learning. The following sections explore these architectural principles and how Vespa brings them together in a single distributed system.
What Great AI Search Depends On
Perplexity's approach to AI search is built around three core principles. Together, they define the architecture needed to retrieve accurate, trustworthy information at production scale.
Why AI Search Needs More Than Vectors
Vector search transformed AI search by making semantic retrieval practical at scale. Today, it is a fundamental building block of modern AI search.
As AI search has matured, however, it has become clear that vectors alone are not enough. Perplexity concluded that neither traditional search engines nor vector databases could independently satisfy the requirements of high-quality AI search. Traditional search engines lacked sophisticated semantic retrieval and ranking, while vector databases often lacked the filtering, ranking, and retrieval capabilities required for production search applications.
Instead, Perplexity built a unified search architecture that combines lexical search, semantic retrieval, structured filtering, machine-learned ranking, and real-time updates within a single distributed system. The following sections explore how Vespa brings these capabilities together to power AI search at production scale.
Perplexity built its search infrastructure on Vespa because modern AI search requires more than language models and vector search alone. It depends on an architecture that combines web-scale search, hybrid retrieval and ranking, fine-grained content understanding, real-time freshness, and integrated machine learning. The following sections explain why Perplexity chose Vespa for AI Search.
More resources
Explore related guides, videos, case studies, and technical documentation in our resource library to learn more about AI search and retrieval with Vespa.
