Recognized through the AWS AI Competency Program, Vespa helps organizations build production AI applications on AWS by unifying retrieval, ranking, machine-learning inference, and real-time serving within a single AI Search Platform, reducing infrastructure complexity while delivering predictable performance.
Why Vespa
AI applications on AWS are rapidly evolving beyond traditional search and retrieval. Organizations are now building generative AI applications, AI agents, conversational experiences, and intelligent applications that depend on retrieving trusted context in real time. Vespa has achieved AWS AI Competency status in the AI Software category, recognizing its demonstrated technical expertise and customer success on AWS.
The Vespa AI Search Platform brings these capabilities together in a single distributed platform running on AWS.
They need to:
The Vespa AI Search Platform brings these production AI capabilities together in a single distributed platform on AWS.
AI Retrieval
AI agents place far greater demands on AI infrastructure than traditional search or RAG. Every interaction requires multiple retrieval operations, ranking decisions, machine learning inference, and continuous access to fresh data. As usage grows, fragmented architectures become increasingly expensive to operate and difficult to scale.
Vespa eliminates the need to stitch together separate retrieval, ranking, inference, and serving systems, reducing data movement and operational complexity while delivering the low latency required for production AI.
Building AI agents on AWS requires more than connecting an LLM to enterprise data. Every interaction depends on a carefully engineered retrieval workflow that retrieves, ranks, personalizes, assembles context, invokes models, and adapts to continuously changing information.
Vespa provides this retrieval workflow in a single AI Search Platform.
According to GigaOm's CIO Decision Brief, organizations consolidating fragmented AI search stacks onto a unified AI Search Platform can achieve up to 5× lower infrastructure costs.
As AI agents become more capable, every interaction requires more retrieval operations, ranking decisions, machine learning inference, and data movement. Rather than scaling multiple specialist systems independently, Vespa unifies these capabilities within a single distributed platform on AWS, reducing operational complexity while keeping infrastructure costs predictable.
Retrieval Engineering
Prompt engineering influences how a model reasons. Retrieval Engineering determines what it has to reason about.
Modern AI applications have moved beyond vector search alone. They assemble trusted context by combining hybrid retrieval, intelligent ranking, machine learning inference, personalization, and real-time updates before a model generates a response.
Retrieval Engineering is the discipline of designing these retrieval workflows. Vespa unifies them within a single AI Search Platform on AWS.
AWS Reference Architecture
Available as a fully managed service through Vespa Cloud, Vespa combines elastic scalability, high availability, and Kubernetes-native operations to simplify deployment and reduce operational overhead for production AI on AWS.
Trusted in production by organizations including Perplexity, Spotify, and Yahoo, Vespa powers large-scale AI applications running on AWS that deliver trusted results with predictable latency and lower infrastructure complexity.
The Vespa AI Search Platform unifies retrieval, ranking, machine-learning inference, and real-time serving within a single architecture on AWS, providing a scalable foundation for AI search, generative AI, and AI agents.
From managed operations to multi-region deployments, Vespa provides the scalability, resilience, and operational simplicity required to confidently run AI search, generative AI, and AI agents on AWS.
Scale seamlessly from development to billions of documents and high query volumes as workloads grow.
Built for resilient, always-on AI applications with distributed serving and fault tolerance.
Designed for modern cloud environments with Kubernetes-native deployment, orchestration, and operations.
Deploy close to your users across AWS regions for lower latency and business continuity.
Power AI search, RAG, generative AI, and AI agents with predictable performance at production scale.
Accelerate your path to production with a best-practice template that prioritizes retrieval quality, inference speed, and operational scale.
