Inside the Vespa AI Search Platform

Introduction

Vespa is built from a set of integrated architectural components that work together to execute the complete AI retrieval workflow. Rather than treating each stage of the retrieval workflow as a separate system, the components are designed to operate within a unified distributed architecture.

The following sections introduce the platform's core building blocks and explain how they contribute to scalable, real-time AI retrieval. Each topic links to a technical deep dive for a more detailed architectural discussion.

Unified Data Model

Vespa brings structured data, full text, vectors, and tensors together into a single document model. Rather than storing different data types in separate systems and combining results later, applications can model, index, and retrieve them through a single schema and query pipeline.

This unified approach simplifies application architecture while enabling lexical search, semantic retrieval, filtering, ranking, and machine learning to work together over the same data.

Document Model & Index Structure

Vespa brings structured data, text, vectors, and tensors together in a single document model.

Distributed Serving

Vespa distributes data across independent content nodes and executes retrieval, ranking, and machine learning inference where the data already resides. Rather than moving large volumes of data across the network for processing, queries are executed locally before results are combined into a final response. This minimizes network overhead while delivering high throughput, predictable latency, and efficient resource utilization as applications scale.

This shared-nothing architecture enables parallel query execution, local model inference, and linear scalability, making Vespa well-suited for large-scale AI applications where retrieval quality, response time, and availability directly affect user experience and business outcomes.

Distributed Architecture & Scaling

Learn how Vespa distributes data and executes queries across independent content nodes.

Retrieval Pipeline

Vespa supports a flexible retrieval pipeline that combines multiple retrieval techniques within a single query. Rather than relying on a single search method, applications can combine lexical search, vector search, filtering, and candidate generation to retrieve the most relevant information for each query.

This unified pipeline provides the foundation for modern AI retrieval, enabling hybrid search, retrieval-augmented generation (RAG), recommendations, and personalized search without requiring separate retrieval systems. Queries can be progressively refined before passing candidates to the ranking stages, balancing retrieval quality with efficiency.

Hybrid Retrieval Pipeline

Combine lexical, vector, filtering, and structured retrieval within a single query.

Multi-Phase Ranking

Retrieval identifies candidate documents. Ranking determines which of those candidates are most relevant. Vespa uses a multi-phase ranking pipeline that progressively applies increasingly sophisticated ranking models, allowing applications to improve relevance while controlling computational cost and query latency.

Each ranking stage can combine lexical signals, semantic similarity, business rules, personalization, and machine learning models, ensuring that expensive computations are performed only on the most promising candidates.

Advanced Ranking & Relevance

Progressively improve relevance by combining lexical signals, semantic similarity, business rules, personalization, and machine learning within an efficient ranking pipeline.

Machine Learning Inference

Vespa executes machine learning models directly within the retrieval pipeline, allowing inference to occur alongside retrieval and ranking rather than through separate model-serving infrastructure. This enables applications to enrich queries, rerank results, and execute machine learning inference without introducing additional network hops.

Vespa supports industry-standard model formats such as ONNX, as well as XGBoost models, custom ranking functions, and transformer-based rerankers, including cross-encoders. Executing inference close to the data reduces latency while simplifying the overall AI architecture.

Machine Learning & Model Inference

Execute ONNX models, XGBoost, cross-encoders, and custom ranking functions directly within the retrieval pipeline for low-latency AI inference.

Real-Time Updates

Modern AI applications depend on continuously changing information, from new documents and inventory changes to user interactions and behavioral signals. Vespa is designed for real-time serving, making newly ingested content and updates searchable almost immediately while maintaining predictable low-latency query performance.

Support for partial document updates enables individual fields to be modified without rebuilding entire documents, while the distributed architecture efficiently handles high ingestion rates alongside query traffic. This allows applications to maintain fresh retrieval results even as data changes continuously.

Real-Time Indexing & Partial Updates

Keep search results continuously up to date with real-time indexing, partial document updates, and immediate query visibility.