How does Vespa handle hybrid search?

Vespa natively supports combining dense vector similarity, sparse vector search, and traditional text matching in a single query, with a unified ranking pipeline to score all results.

Can I run my own ONNX or TensorFlow models for ranking?

Yes. Vespa has native support for executing ONNX and TensorFlow models as part of its phased ranking pipeline, allowing for complex ML inference at query time.

Is there a free tier for Vespa Cloud?

Vespa Cloud offers a free trial period for new users. Post-trial, pricing is based on storage, data processing, and query throughput with no upfront commitment.

How does Vespa achieve low latency at scale?

Through a combination of distributed indexing, in-memory data structures, efficient tensor operations, and phased ranking (cheaper filters first, expensive models last).

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Vespa

Q: What is the difference between Vespa Cloud and self-hosted Vespa?

Vespa Cloud is a fully managed service handling operations, scaling, and security. Self-hosted Vespa gives you full control but requires managing your own infrastructure, deployments, and upgrades.

Vespa is an open-source distributed serving engine for building AI-powered applications. It uniquely combines vector search, text search, structured data queries, and machine-learned model inference in a single, scalable system. Core capabilities include hybrid search (combining vector similarity with traditional text matching), advanced tensor operations for ranking, and automated scaling to billions of data points. Target users are developers and engineers building search, recommendation, and generative AI (RAG) systems. It's designed for business-critical applications where low-latency responses (<100ms) and high throughput (thousands of QPS) are non-negotiable. Deployable on your own infrastructure or as a fully managed cloud service (Vespa Cloud), it's trusted by companies like Spotify, Yahoo, and Perplexity for production workloads.

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Added on

Sep 22, 2026

Vector DBRAG PlatformTensor RankingAuto-scalingManaged CloudOpen Source Core

Product information

Everything worth knowing about Vespa

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Unified Search & AI Platform

A single platform that unifies vector search, text search, structured data queries, and machine-learned ranking for complex AI applications.

Scalable RAG & Generative AI

Build high-quality Retrieval-Augmented Generation (RAG) applications using hybrid search and multi-vector representations at any scale.

Real-time Recommendation & Personalization

Combine retrieval of eligible content with real-time model evaluation to power personalized recommendations and ad targeting systems.

Managed Cloud with Auto-scaling

Fully managed service on Vespa Cloud with infinite, automated scalability and continuous deployment for production workloads.

Technical capabilities

Distributed Tensor Operations

Execute complex ranking and inference using a proprietary tensor formalism within the distributed serving engine.

Hybrid Search with ANN

Supports both exact and approximate nearest neighbor (ANN) vector search, combined with traditional text matching and structured filters.

Integrated ML Model Inference

Run TensorFlow and ONNX models directly within the Vespa serving layer for low-latency, machine-learned ranking.

Must watch videos

Tensor-Native Retrieval and Ranking, Explained

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Tutorial1:55

Tensor-Native Retrieval and Ranking, Explained

Vectors help you retrieve. Tensors help you reason. See why tensor-native retrieval and ranking express every signal that flat vectors leave behind. Modern AI retrieval needs more than a single vector. Hybrid search, multi-vector retrieval, multimodal content, personalization, and machine-learned ranking all depend on structure that flat embeddings collapse. Vespa is vector- and tensor-native by design, so dense embeddings, sparse lexical features, structured metadata, and real-time signals combine in one retrieval and ranking pipeline. The result is hybrid relevance that vector-only systems can't express: precise, explainable ranking that adapts as your data, models, and business needs evolve. Learn more about Vespa's tensor formalism: https://vespa.ai/tensor-formalism Explore the AI Search Platform: https://vespa.ai/ai-search-platform

0:58

Tutorial0:58

Simplifying Your Chunking Strategy

The video discusses the complexities of data chunking in retrieval systems and how Vespa simplifies these processes.

One Document. Every Chunk. Ranked to the Answer

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Tutorial8:22

One Document. Every Chunk. Ranked to the Answer

Chunking for RAG isn’t just choosing a chunk size — it drives your storage model, metadata strategy, keyword scores, and ranking pipeline. Vespa gives you both: chunk-level retrieval precision and document-level architectural simplicity.

Who uses it

Real use cases, no hype

AI-Powered Search & RAG

Build high-quality hybrid search (vector + text) to feed retrieval-augmented generation systems with relevant context.

Real-Time Recommendations

Combine retrieval with complex ML model scoring for personalized recommendations and ad targeting.

E-commerce Navigation

Seamlessly blend text search, vector similarity, and structured filtering for product catalogs.

Enterprise Knowledge Management

Semi-structured search across vast internal documents, supporting legal, financial, and scientific research.

Media & Advertising

Serve personalized content and ads at scale, leveraging real-time user signals and content features.

AI Agent Infrastructure

Provide the retrieval and inference backbone for autonomous AI agents requiring fast, reliable data access.

What's great

Unified platform for vector, text, and structured search with integrated ML inference.
Scales to billions of documents with sub-100ms latencies at high QPS.
Fully managed cloud service with automated scaling and upgrades.
Proven production use at massive scale by Spotify, Perplexity, and Yahoo.

Technical strengths

Native support for advanced ranking with tensors, ONNX, and TensorFlow models.
Hybrid search combining dense vectors, sparse vectors, and text matching.

Where it falls short

Significant operational complexity for self-hosted deployments.
Pricing can be unpredictable for high-throughput, data-heavy workloads.
Steep learning curve for schema definition and ranking expression syntax.

Technical limitations

Requires JVM knowledge for deep customization via searcher components.
Document schema updates require application redeployment, not live changes.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Vespa Cloud is a fully managed service handling operations, scaling, and security. Self-hosted Vespa gives you full control but requires managing your own infrastructure, deployments, and upgrades.