How Perplexity Built AI Search at Scale with Vespa

About Perplexity

Perplexity: Search-First AI

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.

Vespa has supported Perplexity’s development of AI search and remains part of its broader infrastructure.

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. What separates a trustworthy answer from a plausible-sounding one is the quality of the information retrieved before the model ever runs. Perplexity built its product on that principle, which meant owning retrieval instead of renting it.

Building search in-house set a hard bar. The retrieval layer had to index a large, high-quality slice of the web, stay current as that web changed, and return results fast enough to feel instant, all while serving traffic at consumer scale.

When Perplexity evaluated existing tools, neither category cleared that bar. Perplexity found that vector databases could not reliably support even simple boolean filters, while traditional search backends offered only primitive semantic ranking.

The AI ecosystem had split into lexical systems and semantic systems, each strong on one side and weak on the other. Perplexity needed both, in one engine, at scale.

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 a platform that could combine keyword and semantic retrieval, structured filtering, and sophisticated ranking at scale.

Vespa brought these capabilities together in a single distributed serving engine, with support for continuous indexing and machine learning inference. This gave the team a flexible foundation for building search experiences without integrating a separate system for every stage of retrieval and ranking.

An Evolving Search Architecture

Perplexity’s infrastructure has evolved as the company has grown. It has moved its web index to a purpose-built internal search system and continues to use Vespa for other use cases.

Its original selection of Vespa illustrates the value of combining retrieval, filtering, ranking, and machine learning in one platform. These capabilities help teams build AI applications with greater control over how information is selected and served.