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We Make AI Work at Perplexity

Delivering AI-Powered 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 rapidly become one of the most innovative players in AI-driven search. By combining large language models (LLMs) with real-time retrieval, Perplexity delivers accurate, conversational answers—cited and sourced—at web scale. This intuitive experience stands apart from traditional search engines, offering users a smarter, more transparent way to find trusted information.

And the growth speaks for itself: by March 2024, Perplexity had surpassed 15 million monthly active users, fueled by demand for fast, trustworthy answers across public and private data sources.

Why Retrieval Matters

At the heart of Perplexity’s approach is a simple insight: the quality of AI answers depends on the quality of information fed into the model. While many companies have access to foundational LLMs, Perplexity differentiates by retrieving and ranking relevant content with precision—ensuring that responses are fluent, accurate, timely, and grounded in fact.

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

That’s why Perplexity chose Vespa.ai—the only battle-tested platform capable of powering real-time, production-grade Retrieval-Augmented Generation (RAG) at scale.

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

By building on Vespa’s platform, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.

What Is RAG?

Retrieval-Augmented Generation (RAG) is a method for improving the accuracy and transparency of LLM outputs. Instead of relying solely on pre-trained knowledge, RAG works in three key steps:

  • Retrieval – The system searches a curated content store to find the most relevant and up-to-date information for the user’s query.
  • Augmentation – Retrieved content is passed to the LLM to generate a grounded, context-aware response.
  • Generation – The LLM produces an answer based on this real-time context, often including links to original sources.

This approach not only improves accuracy and freshness but also allows responses to be cited—critical for enterprise use cases where trust, auditability, and compliance are non-negotiable.

Why Vespa?

Executing RAG at the scale and speed Perplexity requires is a significant engineering challenge. Vespa makes it possible by combining real-time search, vector similarity, structured filtering, and machine-learned ranking in one unified platform. Here’s how Vespa supports Perplexity:

  • + Massive Scale

    Indexes and searches billions of documents across the public web and private user files, continuously updated in real time.

  • + Multimodal Retrieval

    Supports keyword, vector, and metadata filtering in a single query pipeline to maximize relevance.

  • + Signal-Rich Ranking

    Combines embeddings, structured signals, and learned models to refine the final set of documents passed to the LLM.

  • + High Performance

    Handles thousands of requests per second, with response times in the hundreds of milliseconds.

  • + Experimentation at Speed

    Enables fast iteration with new retrieval strategies and ranking models.

What This Means for Enterprises

Generative AI pilots have proven the opportunity of AI, but scaling those pilots into enterprise-grade systems is hard. Latency, cost, accuracy, and adaptability are all real barriers.

Vespa helps you clear them.

Perplexity’s success proves that Vespa can deliver reliable, high-speed RAG in even the most demanding environments. Whether you’re building internal copilots, customer-facing assistants, or vertical search engines, Vespa provides the core infrastructure to support your growth—from pilot to production.

Learn More

To explore how Vespa can help your team build scalable, real-time AI applications, contact us or get started with Vespa Cloud free trial.

 

Vespa Platform Key Capabilities

  • Vespa provides all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).

  • Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.

  • Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.

  • Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.

  • Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.

  • Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.

  • Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.

  • Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.

  • Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.

  • Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.

More Reading

Vespa RAG Solutions Page

Proving the value of RAG in the lab is one thing, but scaling it across an entire enterprise introduces numerous challenges. Vespa drives relevant, accurate, and real-time answers from all of your data, with unbeatable performance.

Vespa RAG Product Features

Vespa delivers accurate results for large language models, recommendation systems, and personalization engines—driving better business outcomes. It combines structured, full-text, and vector search with real-time ranking and filtering using machine learning expressed as tensors.

Enabling GenAI in the Enterprise RAG

This management guide outlines how businesses can deploy generative AI effectively, focusing on RAG to integrate private data for tailored, context-rich responses.

Analyst Report: Why and How RAG Improves GenAI Outcomes

Choosing the right RAG solution means balancing scalability, performance, security, and cost. This research note from BARC helps organizations navigate the key considerations.

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