One platform for your entire AI team to build, deploy, and scale smarter retrieval.

How Vespa Supports the AI Team

Vespa.ai empowers your entire AI team to build and scale high-performance retrieval systems for RAG, search, personalization, and recommendation — all within a single, unified platform. Data scientists experiment with advanced ranking models using tensors, vectors, and rich data signals. AI engineers benefit from precise, scalable retrieval pipelines that integrate seamlessly with generative systems. Search engineers gain full control over hybrid ranking strategies and production-grade performance. And technical leaders can trust Vespa to handle all data types and current retrieval methods — from millions to hundreds of billions of documents — with the flexibility and reliability to support evolving use cases and future AI innovations. By bringing the whole team onto one platform, Vespa also fosters closer collaboration, faster iteration, and a shared path from experimentation to production.

For Search Engineers: Apply Advanced Information Retrieval at Scale

Vespa gives Search Engineers a powerful, scalable platform for building advanced retrieval systems that support RAG, search, personalization, and recommendation use cases — all in one engine. Its flexibility and performance let you go beyond simple search to deliver highly relevant, context-aware, and individualized results at scale. Key capabilities include:

• Hybrid retrieval – Combine full-text search (BM25) with dense vector similarity and structured filters to maximize recall and precision. Read the Hybrid Text Search Tutorial.
• Flexible ranking framework – Express any ranking logic using mathematical functions, tensors, or ONNX models across all ranking phases. Learn more.
• Multi-signal integration – Incorporate metadata, behavioral signals, and domain-specific features alongside textual and vector relevance.
• Machine-learned ranking (MLR) – Easily integrate trained models into your ranking pipeline, with support for ONNX inference at query time. Learn more. 
• Real-time personalization – Inject per-user context and preferences into the retrieval and ranking process for dynamic, tailored results. Learn more.
• Recommendation support – Blend collaborative signals, item metadata, embeddings, and interaction data to power content or product recommendations. Learn more.
• Massive scalability – Proven distributed architecture enables low-latency, high-throughput performance on large datasets and under heavy query load. Read Balancing Performance and Cost: A Guide to Optimizing Node Size in Vespa and Vespa Serving Scaling Guide.

For Data Scientists: Build and Experiment with Advanced Ranking Models

Vespa enables Data Scientists to express, evaluate, and deploy sophisticated retrieval and ranking models using the full range of available data signals — all within a scalable, production-ready platform. Key benefits include:

• Multi-modal data support – Work with vector embeddings, full-text content, and structured metadata in a unified schema. Learn more.
• Tensor computation – Use Vespa’s native tensor engine to implement state-of-the-art ranking functions and interaction models. Learn more.
• Hybrid retrieval – Combine dense and sparse signals for improved relevance, including keyword and semantic matching. Read the Hybrid Search Tutorial. 
• Custom ranking expressions – Define ranking logic using math, domain rules, or machine-learned features — no custom code required. Learn more.
• Machine-learned ranking integration – Deploy models trained offline, with ONNX support and real-time evaluation during queries. Read about Deploying Remote Models and Ranking with ONNX Models.
• Fast iteration – Collect training data, A/B test ranking strategies, and move seamlessly from experimentation to production. Learn more.
• Scalable by design – Built-in distributed infrastructure ensures your experiments run efficiently on large datasets. Read Vespa Serving Scaling Guide.

For AI Engineers: Power Accurate, Scalable Retrieval for RAG and Beyond

Vespa equips AI Engineers with the tools to build high-accuracy RAG systems and intelligent applications that scale — with full control over how data is retrieved, ranked, and delivered. Key benefits include:

• Advanced retrieval capabilities – Combine vector search, keyword relevance, and structured filters for more accurate grounding in RAG. Learn more.
• Multi-signal scoring – Blend multiple relevance signals — including embeddings, metadata, and behavioral data — in one pipeline. Read the Hybrid Text Search Tutorial.
• Support for multi-vector models – Implement ColBERT-style architectures with late interaction and distributed tensor operations. Learn more.
• ONNX model inference – Run trained neural ranking or classification models at query time, fully integrated into the retrieval layer. Learn more.
• Customizable ranking logic – Express any scoring function with math, tensors, or model outputs — no black-box limitations. Learn more.
• Unified platform – Use the same system for retrieval, reranking, and inference, reducing system complexity. Learn more.
• Production-grade scale – Vespa handles billions of documents and high query volumes with low latency and high reliability. Read Scaling Smarter: Vespa’s Approach to High-Performance Data Management and Vespa Serving Scaling Guide.