Search is Entering a New Era

Search applications have continuously evolved as new retrieval techniques emerged. Keyword retrieval was joined by machine-learned ranking, personalization, semantic retrieval, and recommendation systems. Many organizations adopted these capabilities by integrating specialized search engines, ranking services, and machine learning components into their existing architectures.

AI changes the demands placed on search.

Large language models (LLMs) and AI agents dramatically increase both the volume and sophistication of retrieval. What was once a manageable search architecture becomes a complex retrieval workflow in which every additional component increases latency, operational complexity, and infrastructure costs.

Traditional search could often tolerate fragmented architectures because humans compensated for imperfect retrieval by refining queries or selecting a better result. AI retrieval cannot. Every retrieval becomes part of an automated workflow, in which the quality of the final answer depends entirely on the retrieved context.

As search evolves to support both people and AI systems, the retrieval workflow becomes the defining engineering challenge.

Retrieval Engineering

Retrieval workflows have become the defining engineering challenge for modern AI applications. The challenge is no longer selecting the best vector database, reranker, or inference model—it is engineering a workflow that balances search quality, latency, infrastructure cost, freshness, and scalability.

As retrieval workflows become more sophisticated, teams must optimize the entire execution path rather than individual technologies. Those engineering decisions ultimately determine the quality of the application itself.

Choose Vespa When

  • AI answers directly influence customer experience and trust.
  • Search quality determines application quality.
  • Vector search alone is no longer enough.
  • Ranking has become as important as retrieval.
  • Fresh, real-time data is essential.
  • Scale is exposing the limits of fragmented architectures.
  • AI agents are dramatically increasing retrieval traffic.

Optimize the Entire Workflow

Modern AI applications depend on sophisticated retrieval workflows that combine keyword search, vector search, filtering, ranking, machine learning inference, and business logic. As more capabilities are added, fragmented architectures introduce latency, infrastructure cost, and operational complexity.

Vespa is designed to optimize the entire workflow. Multi-phase ranking progressively refines candidates, applying increasingly sophisticated ranking models—including machine-learning inference—only where they improve the final outcome. By executing retrieval, ranking, and inference within a single distributed serving engine, Vespa minimizes unnecessary data movement while maintaining high throughput and predictable latency. The result is better search quality, lower infrastructure costs, and more accurate context for language models and AI agents—all without sacrificing performance at scale.

GigaOm

According to GigaOm, consolidating fragmented AI search stacks onto a unified AI Search Platform can reduce infrastructure costs by up to 5× while simplifying operations.

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One Workflow

Modern AI applications combine keyword search, vector search, ranking, and machine learning. Fragmented architectures add latency, cost, and operational complexity.

Vespa unifies the entire retrieval workflow within a single distributed serving engine, delivering better search quality with fewer moving parts.

Always Fresh

Better applications start with current information. The quality of intelligent applications depends on fresh retrieval. Documents, embeddings, user signals, and business data should become searchable immediately—not after an index rebuild or scheduled refresh. Vespa continuously indexes and updates data while serving live traffic, keeping AI agents, answer engines, search, recommendations, and personalization synchronized with the latest information.

High Performance at Scale

Scale retrieval without compromise. As AI workloads grow, many architectures add retrieval stages, rerankers, and inference services, thereby increasing latency and operational complexity. Vespa scales retrieval, ranking, and machine learning together in a single distributed serving engine, maintaining high throughput and predictable latency as data volumes, users, and AI agents grow.

We made AI work at Perplexity

Perplexity combines large language models with real-time retrieval to deliver fast, cited answers across billions of documents. As retrieval quality increasingly determines answer quality, Perplexity relies on Vespa to retrieve, rank, and continuously update the context behind every response. By bringing hybrid retrieval, advanced ranking, machine learning inference, and real-time indexing together within a single distributed serving engine, Vespa enables Perplexity to deliver trustworthy answers with the speed and scalability demanded by millions of users.

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Explore the Architecture

Modern AI applications demand more than vector search. Learn how Vespa unifies retrieval, ranking, machine learning inference, and real-time serving within a single distributed architecture.

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