We Make AI Work at Perplexity

Delivering AI Search at Scale

How Perplexity uses Vespa.ai to power fast, accurate, and trusted answers for millions of users.

Perplexity: Where Knowledge Begins

Perplexity has quickly become one of the most innovative players in generative AI. By combining large language models (LLMs) with real-time retrieval, Perplexity delivers accurate, conversational answers that are cited and sourced at web scale. This experience stands apart from traditional search engines by giving users a transparent way to find trusted information.

By May 2025, Perplexity reported 22 million active users and 780 million monthly queries, driven by demand for fast and reliable answers across public and private data sources.

Why Retrieval Matters

Perplexity’s approach is based on a clear principle: the quality of AI answers depends on the quality of information retrieved. While many companies can access foundational LLMs, Perplexity differentiates by retrieving and ranking relevant content with precision so that responses are fluent, accurate, and grounded in fact.

“First, solve search, then use it to solve everything else,” as Perplexity CEO and Founder Srinivas puts it.

To achieve this, Perplexity built its retrieval layer on Vespa.ai, the only production-proven platform capable of powering real-time, large-scale Retrieval-Augmented Generation (RAG). By combining real-time indexing, hybrid retrieval, and advanced ranking, Perplexity delivers higher-quality and faster answers than conventional search systems.

Read Jon Bratseth, CEO & Founder, Vespa.ai Blog Perplexity builds AI Search at scale on Vespa.ai

Challenge

To deliver high-quality RAG results, Perplexity determined that a retrieval system must provide:

Completeness, freshness, and speed: comprehensive coverage, continuous updates, and low latency
Fine-grained content understanding: relevance scoring at the level of document sections, not just full pages
Hybrid retrieval and ranking: combining lexical and semantic signals to provide context that LLMs can trust

Neither traditional search engines nor vector databases met these needs. Vector databases struggled with filtering and ranking, while conventional search engines lacked the semantic precision and scalability required for AI-driven applications.

Why Vespa?

Perplexity selected Vespa.ai as the foundation for its AI Search platform and AI-First Search API because Vespa uniquely integrates retrieval, ranking, and machine learning inference at scale. Vespa provides the completeness, freshness, and fine-grained control that high-quality RAG depends on. Here’s how Vespa supports Perplexity:

+ High-Performance Query Execution

Vespa’s serving engine combines distributed retrieval, ranking, and ML inference in a single low-latency pipeline. It efficiently handles thousands of concurrent hybrid queries per second while maintaining sub-second response times through memory-resident indexes, parallel execution, and optimized C++ processing.
+ Fine-Grained Content Understanding

Vespa supports chunk-level retrieval, treating both documents and their internal sections as retrievable units. This allows Perplexity to supply LLMs with only the most relevant text spans, improving factual accuracy, reducing context length, and minimizing compute cost.
+ Advanced Hybrid Ranking

Vespa fuses lexical, vector, and metadata signals in a unified ranking pipeline. Early stages use lexical and embedding-based scorers to narrow candidates, while later stages apply learned models and cross-encoders to refine relevance. Structured and behavioral features are incorporated directly into ranking, enabling continuous optimization based on real-world signals.
+ Real-Time Indexing and Updates

Vespa continuously ingests data and updates both text and vector indexes in real time without interrupting queries. Its distributed architecture balances data and computation across nodes, co-locating content, indexes, and ranking logic to eliminate bottlenecks. Partial updates let Perplexity refresh metadata and behavioral signals at high frequency, ensuring the retrieval layer always reflects the latest state of the web.
+ Integrated ML and Operational Efficiency

Vespa runs ranking models and cross-encoders directly inside the serving layer, removing the need for external pipelines. Unified management of retrieval, ranking, and model execution allows Perplexity to iterate quickly, deploy new models seamlessly, and maintain performance with low operational overhead.

The RAG Blueprint

A Blueprint for Success

The RAG Blueprint is a modular application template for designing, deploying, and testing production-grade RAG systems for Vespa. It codifies best practices for building accurate and scalable retrieval pipelines using Vespa’s native support for hybrid search, phased ranking, and real-time inference. Designed for developers and architects, the Blueprint serves as a hands-on guide for production-ready implementations, helping teams move faster without compromising on quality or control.

Ready to Unlock the Power of Generative AI?

Generative AI only delivers real business value when it’s built on the right foundation. Vespa.ai is the world’s first AI Search Platform, unifying vector, keyword, and structured retrieval with machine-learned ranking and real-time inference. Trusted by leaders like Perplexity, Spotify, and Yahoo, Vespa powers search, personalization, and recommendation, and delivers the speed, scale, and accuracy required for deep research, agentic AI, and customer-facing generative applications.

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