Vespa Cloud on AWS
Build fast, accurate, scalable customer-facing applications with unified retrieval, ranking, and inference.
Vespa powers real-time search, RAG, recommendations, and personalization on AWS by unifying structured, unstructured, vector, and tensor data in a single system. By combining hybrid search and machine-learned ranking in one query pipeline, Vespa delivers low-latency, high-throughput performance for applications where relevance and speed directly impact engagement, conversion, and revenue.
Developers building customer-facing search, RAG, and recommendation systems face a core challenge: delivering relevant results in real time from fragmented data sources. Content is spread across formats such as PDFs, free text, and semi-structured data, making it difficult to index, retrieve, and serve efficiently. Without the right infrastructure, applications become slow, complex, and costly to scale. Vespa addresses this by unifying structured, unstructured, vector, and tensor data in a single system, enabling efficient, real-time retrieval and ranking.
Built for customer-facing applications on AWS, Vespa powers search, RAG, recommendations, and personalization with low latency and high throughput. By combining full-text search, vector search, and machine-learned ranking in a single query pipeline, Vespa delivers consistent, high-quality results across every interaction.
Its tensor-based architecture enables multiple relevance signals to be evaluated simultaneously, including semantic meaning, behavioral data, and real-time context. Ranking and inference run directly within the engine, allowing results to continuously adapt to user intent and business priorities without external pipelines.
Running on AWS, Vespa provides elastic scalability, high availability, and a fully managed experience through Vespa Cloud. Automated provisioning, scaling, and monitoring reduce operational overhead while supporting demanding, real-time workloads. Vespa is trusted in production by organizations including Perplexity, Spotify, and Yahoo to power large-scale AI applications that enhance customer experience, improve conversion, and drive measurable business outcomes.
Vespa Architecture for real-time search, RAG, and recommendation on AWS, combining hybrid search, vector search, and ML ranking for low-latency, scalable AI applications.
Reduce latency and network overhead with co-located data and computation, enabling fast, resource-efficient retrieval at any scale.
Deliver accurate, contextual results using hybrid search and distributed machine-learned ranking across structured, unstructured, and vector data.
Scale clusters up or down in real time while maintaining low latency, high throughput, and consistent uptime for production workloads.
The report provides a detailed comparison of 17 leading open source and commercial solutions, examining their strengths across hybrid search, semantic retrieval, RAG, and large-scale AI workloads. It also highlights how vendors are integrating vectors, tensors, and other numerical representations to power next-generation AI applications.
Vespa.ai is a member of the AWS ISV Accelerate Program, a co-sell initiative connecting leading software providers with AWS to help customers adopt modern, cloud-native solutions. This partnership reflects Vespa’s alignment with AWS infrastructure and commitment to delivering scalable, high-performance AI search. Through the program, Vespa collaborates with AWS to optimize deployments, adopt new technologies such as Graviton processors, and support customers via AWS Marketplace—enabling faster deployment and time to value.
Accelerate your path to production with a best-practice template that prioritizes retrieval quality, inference speed, and operational scale.
