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Vespa in AdTech

Powering Real-Time AdTech with AI Search

Deliver relevant ad and content recommendations in milliseconds with Vespa’s scalable, high-performance platform for hybrid retrieval and ranking.

AdTech is Moving to Online, AI-Driven Decisioning

AdTech is shifting from batch pipelines, where candidate generation and ranking models are updated on fixed schedules, to online, data-driven personalization, where ad ranking and recommendations happen in milliseconds. As deep learning models become central to optimizing engagement and ROI, platforms need serving systems that can operationalize large volumes of behavioral, contextual, and vector data in real time. Delivering relevant results instantly while continuously adapting to new signals has become a key differentiator for performance, yield, and user experience. Vespa enables this with a scalable, high-performance ad serving platform for hybrid retrieval and ranking, unifying structured, unstructured, and vector data to power accurate, continuously updated recommendations that maximize engagement and revenue.

Ad Serving Challenges

Delivering on these demands introduces significant technical complexity. Data is distributed across multiple systems and formats, making it difficult to unify structured, unstructured, and vector representations within a single retrieval and ranking pipeline. For example, ad serving platforms must combine structured campaign and user data, unstructured publisher content and metadata, and vector embeddings, all of which update continuously at different rates. Traditional offline or batch processing approaches cannot maintain the freshness required for real-time targeting, where signals and inventory change thousands of times per second. Ranking has also grown more computationally intensive, combining multi-phase scoring, filtering, and neural model inference for CTR, CVR, and eCPM prediction—all under tight latency constraints. Efficiently embedding deep learning models into online serving, ensuring low-latency execution at scale, and keeping infrastructure costs manageable remain core challenges for AdTech engineering teams.

Beyond Vector Search

Vector databases alone don’t deliver scalable, production-grade search. While they handle nearest-neighbor search, real-world applications demand much more, including combining semantic, keyword, and metadata retrieval, applying machine-learned ranking, and managing constantly changing structured and unstructured data. Scaling this across billions of documents with sub-100ms latency and thousands of concurrent queries forces you to stitch together multiple systems, introducing complexity, performance risks, and escalating infrastructure costs.

Vespa removes this burden by unifying vector search, hybrid retrieval, and real-time ranking in a single AI Search Platform, purpose-built to handle massive workloads at production scale without the integration overhead.

While many teams start their AI journey with a vector database, Vespa goes further, combining approximate nearest neighbor (ANN) search with traditional search and ranking in one cohesive system. This capability has earned Vespa recognition as both a Leader and Fast Mover in the GigaOm Sonar for Vector Databases, highlighting its unique ability to go beyond pure vector search to deliver true hybrid retrieval and real-time ranking at scale.

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Why Vespa?

Hybrid Retrieval for Rich Candidate Generation

Combine keyword, structured, and vector search in one query to capture both semantic meaning and contextual signals—essential for matching users with the most relevant ads and content.

Online Updates for Fresh Data

Apply thousands of partial updates per second to keep user behavior, campaign metadata, and inventory features continuously up to date.

Multi-Phase Ranking and Optimization

Execute complex ranking pipelines that balance click-through rate, ROI, engagement, and diversity within a single query—without adding latency.

Integrated Tensor Support

Vespa’s built-in tensor framework allows you to represent, store, and compute on multi-dimensional data directly in ranking. This enables efficient use of vector embeddings, cross-feature interactions, and deep model outputs, without an external inference service.

Low-Latency Scale

Serve hundreds of thousands of queries per second with consistent sub-50 ms response times using Vespa’s distributed architecture and parallel execution across shards.

Proven Reliability in Production

Validated at internet scale by companies such as Yahoo, Taboola and AdMarketPlace, Vespa powers large-scale AdTech systems with predictable performance and cloud flexibility, deployable on the cloud or on-prem.

Vespa AI Search Platform Key Capabilities

  • Vespa provides all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).

  • Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.

  • Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.

  • Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.

  • Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.

  • Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.

  • Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.

  • Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.

  • Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.

  • Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.

Ready to Unlock the Power of AI?

The AI Search Platform behind Perplexity, Spotify, and Yahoo. Vespa.ai unifies search, personalization, and recommendations with the accuracy and performance needed for generative AI at scale.

Other Resources

The RAG Blueprint

Accelerate your path to production with a best-practice template that prioritizes retrieval quality, inference speed, and operational scale.

Delivering Search for Perplexity

With Vespa RAG, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.

GigaOm: Migrating to AI-Native Search and Data Serving Platforms

AI-driven applications are pushing conventional search infrastructure to its limits. This GigaOm Brief explains how traditional systems are becoming bottlenecks for real-time, high-volume workloads like RAG and semantic search.