AWS has published guidance on deploying Reflection AI’s Beam, an open-weight model built for agentic coding and long-horizon tasks, on its cloud infrastructure. The post outlines two deployment paths: starting with an Amazon SageMaker JumpStart real-time endpoint, or using Amazon SageMaker HyperPod Inference together with JumpStart, SGLang, or vLLM for Kubernetes-native control over distributed GPU infrastructure.
Why it matters
An open-weight model offers teams flexibility to run and manage the model within their own infrastructure. By documenting multiple deployment options, AWS gives practitioners a choice between a managed real-time endpoint and more granular control over distributed GPU resources.
Who should care
Developers and engineering teams working on agentic coding workflows, as well as those operating on AWS who need Kubernetes-native control over inference, are the primary audience.