Recognized through the AWS AI Competency, Vespa unifies retrieval, ranking, machine learning inference, and real-time serving within a single AI Search Platform, helping organizations build production AI on AWS with lower infrastructure complexity and 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. The AWS AI Competency recognizes Vespa's proven ability to help customers build these production AI applications on AWS.
Organizations building AI applications on AWS need more than a vector database.
They need to:
The Vespa AI Search Platform unifies these capabilities within a single distributed platform running 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.
The Vespa AI Search Platform addresses this challenge by unifying retrieval, ranking, machine learning inference, and real-time serving within a single distributed platform on AWS. By eliminating unnecessary data movement and system handoffs, Vespa reduces operational complexity while delivering the low latency and infrastructure efficiency required for production AI.
Building AI agents on AWS requires more than connecting an LLM to your data. Agents must continuously retrieve, rank, assemble trusted context, invoke tools, and adapt to changing information in real time before they can reason and act.
Vespa unifies hybrid retrieval, intelligent ranking, machine learning inference, and real-time serving within a single AI Search Platform on AWS, reducing infrastructure complexity while delivering trusted context for production AI.
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
Recognized as an AWS AI Competency Partner, Vespa is built for organizations running production AI on AWS. Available as a fully managed service through Vespa Cloud, it combines elastic scalability, high availability, and Kubernetes-native operations to simplify deployment and reduce operational overhead.
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 the scalable foundation for AI search, generative AI, and AI agents.
From managed operations to multi-region deployment, Vespa provides the scalability, resilience, and operational simplicity required to run AI search, generative AI, and AI agents confidently 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.
