AI Big Data Serving Engine
What can Vespa do for large-scale AI-powered applications?
Vespa.ai is an AI Search Platform for developing and operating large-scale applications that combine big data, vector search, machine-learned ranking, and real-time inference. With native tensor support for complex ranking and decisioning, Vespa enables real-time AI applications like RAG, recommendation, and intelligent search—at enterprise scale. Vespa lets you query, organize, and make inferences in vectors, tensors, text and structured data. Scale to billions of constantly changing data items, thousands of queries per second with latencies below 100 milliseconds.
What are Vespa's primary use cases?
- Search: Open text search and vector retrieval with integrated machine-learned ranking.
- Generative AI (RAG): Hybrid search, relevance models, and multi-vector representations for high-quality data surfacing.
- Recommendation and personalization: Combine retrieval with machine-learned model evaluation for real-time, scalable personalization.
- Semi-structured navigation: Mix of structured data with text+images for fast, scalable navigation in large catalogs.
- Personal/private search: Streaming search mode delivers Vespa’s features for private data at lower costs.
How does Vespa support Generative AI (GenAI) applications?
GenAI applications are only as good as the data surfaced for them. Vespa provides advanced data surfacing with hybrid search, relevance models, and multi-vector representations, enabling high-quality data retrieval for GenAI. It is designed to deploy such techniques with no limitations and at any scale.
How does Vespa support personalized recommendations?
Vespa empowers businesses to build recommendation and personalization systems by combining content retrieval with machine-learned model evaluation. This enables real-time, scalable recommendations at any volume, making it suitable for e-commerce, content platforms, and targeted advertising.
What data types does Vespa support for search, ranking, and inference?
Vespa lets you query, organize, and make inferences in vectors, tensors, text and structured data. This multi-modal data handling supports complex ranking and real-time inference at scale.
What is streaming search and how does it help with personal/private search?
Streaming search is a special mode that delivers all the industry-leading features of Vespa for personal/private search. It enables private search workloads at significantly lower cost—20x cheaper than indexing.
What makes Vespa's machine-learned ranking feature powerful and what models does it support?
Vespa provides a distributed machine-learned ranking engine that can run models from popular tools such as TensorFlow, LightGBM, XGBoost, and ONNX. This enables precise, scalable ranking of search results and other data-driven decisions.
Is Vespa available as a fully managed cloud service?
Yes. Vespa is fully managed, with strong security, and Vespa Cloud is the cloud offering that provides a managed experience for deploying Vespa-based applications.
How can I get started with Vespa?
You can start with a free trial to build your first Vespa-powered application. Begin your free trial on Vespa’s site to explore sample apps, documentation, and guidance.
Are there notable Vespa customers or case studies?
Yes. Vespa has case studies with Spotify, Yahoo, Farfetch, and Elicit, demonstrating real-world use of Vespa at scale. Spotify’s quote highlights Vespa’s reliability and scalability for enabling Search at scale.
