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Build RAG at Scale

Built for Deep Research. Ready for Machine Speed.

Empower your AI applications to reason, recall, and refine insights across billions of documents in real time. Vespa unifies text, vector, and metadata search with machine-learned ranking so your enterprise RAG systems can think deeper, not slower.

Why Deep Research Breaks Traditional RAG Systems

Today’s AI agents issue hundreds of retrievals per session to investigate complex topics, ground insights with evidence, and generate reliable, contextual answers. This is beyond the capabilities of conventional retrieval stacks, which fall short in production because:

  • Rely on external services for ML reranking, slowing responses, and increasing cost
  • Break under real-time update requirements with no live indexing or ingestion
  • Can’t join structured and unstructured data on-the-fly
  • Suffer latency spikes and throughput drops under multi-hop retrieval

How Vespa Powers Deep Research

Deep research isn’t just search. It’s an iterative, multi-hop process where systems must understand, connect, and reason across vast, evolving datasets.

Vespa eliminates limits of traditional systems with one distributed system that co-locates storage, retrieval, ranking, and inference to remove data movement, keeping latency and costs low.

Why Vespa for Deep Research

Made for Real-World Complexity

Vespa unifies vector, text, and structured retrieval in one engine, enabling queries that mix semantics, filters, metadata, and business logic without stitching together multiple systems.

Accuracy without Latency

Vespa’s hybrid retrieval and multi-phase ranking pipelines deliver millisecond responses, even when agents chain hundreds of queries or reason over billions of documents.

Always Up to Date

Vespa ingests and serves new data in real time—no manual refreshes, rebuilds, or downtime. Researchers and agents always retrieve the latest insights as information evolves.

Machine Learning at the Core

Run embedding and re-ranking models directly inside the data cluster, leveraging metadata and domain-specific signals without external latency.

Unified Data Handling

From research papers to metadata and embeddings, Vespa lets you query everything together. No data silos, no manual joins, no preprocessing pipelines to maintain.

Built for Scale

Autoscaling, partitioning, and replication maintain low latency and high throughput as data and query volumes grow.

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

With Vespa RAG, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.

Ready to go Beyond Basic RAG?

With Vespa, you don’t just retrieve documents. You power intelligent systems that reason, rank, and scale. From research copilots to market intelligence platforms, Vespa enables deep research at machine speed.

Explore More

Layered Ranking for RAG Applications

Deep research requires multiple ranking phases to balance precision, latency, and cost. This post shows how Vespa’s layered ranking lets developers combine fast approximate retrieval with deeper model-based re-ranking, enabling RAG pipelines to scale to billions of documents without losing accuracy.

The RAG Blueprint

The RAG Blueprint is a modular application template for designing, deploying, and testing production-grade RAG systems. Built on the same core architecture that powers Perplexity, 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.

Advancing HNSW in Vespa

For large-scale vector and hybrid search, efficiency in approximate nearest-neighbor algorithms is key. This post explains Vespa’s enhancements to Hierarchical Navigable Small World (HNSW), showing how these techniques improve recall and latency trade-offs in production environments handling high-throughput AI workloads.

RAG Technical Guide

Learn how Vespa RAG allows language models to access up-to-date or specific domain knowledge beyond their training, improving performance in tasks such as question answering and dynamic content creation.