How the AI Search Platform works

This page explains how the Vespa AI Search Platform works. It introduces the platform's core architectural concepts and links to the technical deep dives that explore each capability in more detail.

Vespa is designed around a simple architectural principle: execute search and AI inference where the data lives. Rather than moving data between specialized systems for retrieval, ranking, inference, and serving, Vespa performs these operations within a unified distributed engine, reducing network overhead while improving latency, scalability, and operational simplicity.

Vespa is a distributed serving engine that unifies retrieval, ranking, machine learning inference, and real-time serving within a single architecture. Rather than stitching together specialized databases, retrieval engines, inference services, and serving infrastructure, Vespa executes search, ranking, inference, and serving close to the data, delivering high throughput, predictable latency, and operational simplicity at scale.

A unified architecture for retrieval, ranking, machine learning inference, and real-time serving.

platform architecture

Architecture components

Understand the distributed architecture that executes the retrieval workflow. Here you will find the core platform components that power retrieval, ranking, machine learning inference, and real-time serving.

  • Unified data model

    Bring every data type together.

    Model structured data, full text, vectors, and tensors in a single schema. Retrieve, filter, rank, and run machine learning over the same data without stitching together multiple systems.

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  • Distributed serving

    Scale without adding complexity.

    Execute retrieval, ranking, and machine learning where the data already resides. A shared-nothing architecture delivers predictable latency, high throughput, and linear scalability for AI applications.

  • Retrieval pipeline

    Build retrieval workflows that combine multiple search techniques.

    Combine lexical search, vector search, metadata filtering, structured retrieval, and passage search within a single query pipeline to improve relevance while keeping latency low.

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  • Multi-phase ranking

    Optimize relevance at every stage.

    Apply progressively more sophisticated ranking models—from lightweight signals to deep learning rerankers—so expensive computation is focused only where it matters most.

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  • Machine learning inference

    Run AI models directly inside search.

    Execute ONNX, XGBoost, cross-encoders, and custom ranking functions alongside retrieval and ranking, eliminating separate model-serving infrastructure.

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  • Real-time updates

    Keep results continuously fresh.

    Index new content and apply partial document updates almost immediately, ensuring search and AI applications always operate on the latest information.

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Deployment

Deployment & operations

Understand how Vespa applications are deployed and operated in production. Here you'll find the deployment models, infrastructure options, and scaling capabilities needed to run retrieval systems at any scale.

  • Application deployment

    Deploy your entire AI application as one unit.

    Package document schemas, ranking logic, machine learning models, and configuration into a single deployable application. The same application model works across development, testing, and production, simplifying upgrades and ensuring consistent behavior.

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  • Multi-cluster architecture

    Scale datasets independently within one application.

    Run multiple content clusters inside a single Vespa application, allowing datasets and workloads to be indexed, scaled, and optimized independently while remaining part of one retrieval platform.

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  • Multi-cloud deployment

    Deploy anywhere without changing your application.

    Run the same Vespa application across public cloud, private infrastructure, or hybrid environments. A consistent deployment model simplifies migration, disaster recovery, and global expansion while avoiding infrastructure lock-in.

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  • Horizontal Scaling

    Grow without rearchitecting.

    Scale storage, indexing, and query throughput by adding nodes. Vespa's distributed architecture lets applications grow from millions to billions of documents without fundamental changes to the application.

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RElated content

Solution guides

The Technical Deep Dives explain how the platform is built. The resources below show how Vespa's architectural capabilities can be applied to common AI search patterns and implementation challenges.

  • The RAG Blueprint

    A modular application template for designing, deploying, and testing production-grade RAG systems.

    Read more

  • Visual retrieval

    Enhance multimodal search by combining image and text queries for more comprehensive results.

    Read more

Learn with Vespa

Learn how to build search, recommendation, and RAG applications with Vespa through a free, self-paced course that combines hands-on exercises with links to the documentation.

Start the free course

Ready to build your AI search workflow?

Whether you're evaluating Vespa Cloud or planning a self-managed deployment, we'd be happy to discuss your architecture, answer technical questions, and help you get started.

Talk to the Vespa team