AI Retrieval for Generative AI

Build scalable RAG and AI retrieval applications with accurate, low-latency retrieval.

Explore the Perplexity case study

Why RAG is Difficult to Scale

Retrieval-Augmented Generation (RAG) is the standard for grounding large language models with external knowledge. Early RAG applications combined embeddings, a vector database, and an LLM. As these applications mature, however, the retrieval workflow becomes significantly more sophisticated.

Improving answer quality often means adding hybrid retrieval, structured filtering, reranking, business rules, real-time updates, and machine learning inference. What begins as a simple RAG application gradually becomes a complex retrieval workflow spanning multiple specialized systems, increasing infrastructure cost, latency, and operational complexity. The rise of agentic AI compounds this problem, dramatically increasing retrieval volume while placing even greater demands on latency, freshness, and ranking quality.

Where retrieval workflows power customer-facing AI applications, every retrieval decision directly affects answer quality, response time, and user trust. Unlike traditional search, where users can compensate for imperfect ranking by selecting a better result, the language model can only answer using the context it receives.

Why the Retrieval Workflow Matters

As retrieval workflows become more sophisticated, optimizing the workflow becomes the primary engineering challenge. Teams are forced to balance answer quality against latency, infrastructure cost, and operational complexity. Those engineering compromises ultimately become compromises in the user experience, determining the quality of the AI application itself.

RAG is one application of AI retrieval. The real engineering challenge is building and optimizing the retrieval workflow that assembles accurate context before it reaches the language model.

Vespa is the AI Search Platform built for large-scale AI retrieval, not just RAG. Instead of stitching together vector databases, search engines, rerankers, and inference services, Vespa brings retrieval, ranking, and machine learning together in a single distributed serving engine.

Choose Vespa When:

AI answers directly affect customer experience and trust.
Retrieval quality determines application quality.
Vector search is no longer enough to build accurate context.
Ranking has become as important as retrieval.
Fresh data is essential for trustworthy responses.
Scale is exposing the limits of your retrieval architecture.
Agentic AI is dramatically increasing retrieval traffic.

Optimized Retrieval Pipeline

High-quality AI retrieval requires multiple retrieval and ranking stages, but adding more components often increases latency, infrastructure cost, and operational complexity.

Vespa is designed to optimize the entire retrieval pipeline. Instead of applying expensive ranking models to every candidate, Vespa uses multi-phase ranking to progressively refine results. Fast retrieval techniques identify promising candidates, while increasingly sophisticated ranking models—including machine learning inference—are applied only where they improve the final outcome.

Because retrieval, ranking, and machine learning execute within a single distributed serving engine, Vespa minimizes unnecessary data movement while maintaining high throughput and predictable latency. The result is more accurate context for large language models, enabling AI applications to deliver better answers without sacrificing performance at scale.

Independent analysis from GigaOm highlights how integrated AI Search Platforms reduce infrastructure complexity, improve performance, and lower operational costs compared with fragmented retrieval architectures.

Read the GigaOm Decision Brief

One Engine

Optimize the entire retrieval pipeline.

Many RAG architectures move data between vector databases, search engines, rerankers, and inference services before building context for the language model. Every additional service adds latency, cost, and operational complexity. Vespa performs retrieval, filtering, ranking, and machine-learning inference in a single distributed serving engine, delivering more accurate context with fewer moving parts and <100 ms latency required for large-scale AI retrieval.

Always Fresh

Better answers start with current information.

The quality of AI-generated answers 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 RAG, agentic AI, search, and recommendation applications synchronized with the latest information.

High Performance at Scale

Keep AI retrieval fast as applications grow.

As AI workloads grow, many architectures add retrieval stages, rerankers, and inference services that increase 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 Make AI Work at Perplexity

Perplexity delivers millions of users accurate, cited answers by combining large language models with real-time AI retrieval. As retrieval quality becomes increasingly critical to answer quality, Perplexity relies on Vespa to retrieve, rank, and continuously update the context behind every response.

By combining hybrid retrieval, advanced ranking, machine learning inference, and real-time indexing in a single distributed serving engine, Vespa enables Perplexity to deliver fast, trustworthy answers at internet scale.

Explore the Perplexity case study

Unified Vector, Text, and Structured Retrieval

Vespa brings all retrieval methods into a single, cohesive engine. You can combine dense embeddings, keyword signals, and metadata filters into a single query, eliminating the need for multiple systems or external orchestration.
Built-in Ranking and ML Inference

Vespa lets you deploy ranking models directly inside the serving layer using ONNX, XGBoost, or custom functions. You can perform first-phase recall with embeddings, then re-rank with machine learning models to maximize accuracy and explainability.
Real-Time Indexing and Updates

Unlike immutable-segment vector systems, Vespa supports continuous data ingestion and updates without costly index rebuilds. Applications stay fresh and responsive even under high write throughput.
Advanced Filtering and Aggregation

Beyond similarity search, Vespa supports structured filters, geospatial queries, and aggregations directly on vector fields. This allows you to combine semantic and business logic seamlessly.
Tensor-Native Architecture

Vespa stores and computes on tensors, not just vectors. This enables multi-dimensional representations that capture relationships between modalities such as text, images, and numeric data, supporting both dense and sparse features for hybrid search.

Explore tensors
Multimodal and Multi-Vector Support

Vespa supports multiple embeddings per document, enabling use cases such as ColBERT or ColPali-style retrieval, image–text matching, and cross-modal search all within the same schema.

Build with The RAG Blueprint

The RAG Blueprint is Vespa's reference architecture for building production-ready AI retrieval applications. It brings together hybrid retrieval, multi-phase ranking, real-time indexing, and machine learning inference into a modular implementation that reflects the principles described on this page.Whether you're building a new RAG application or scaling an existing one, the Blueprint helps you move from architecture to implementation faster.

Explore The RAG Blueprint

How Does Vespa Compare?

Many RAG architectures stitch together a vector database, search engine, reranker, and machine learning services to build the retrieval workflow. While this approach can improve answer quality, it also increases latency, infrastructure cost, and operational complexity as applications scale.

Vespa takes a different approach. Retrieval, ranking, filtering, machine learning inference, and real-time indexing run together in a single distributed serving engine. Instead of optimizing individual components, Vespa optimizes the entire retrieval workflow, delivering more accurate context with lower latency and a simpler architecture for large-scale AI retrieval.

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Ready to optimize retrieval workflows

Whether you're building customer-facing RAG, agentic AI, or AI search applications, we'd be happy to discuss your architecture and show how Vespa brings retrieval, ranking, and machine learning together in a single AI Search Platform designed for large-scale AI retrieval.

More resources

Explore related guides, videos, case studies, and technical documentation in our resource library to learn more about AI search and retrieval with Vespa.