Deepgram enhances Amazon SageMaker AI support with AWS IAM Temporary Delegation

<p>Enterprises running self-hosted speech AI need fast, auditable support without giving partners long-lived access to their accounts. When a model returns unexpected results or an endpoint misbehaves, the engineer best positioned to diagnose it is often on the partner side. But provisioning cross-account AWS Identity and Access Management (IAM) roles for every support engagement is operationally expensive and a recurring audit conversation. Amazon SageMaker AI delivers a managed deployment option for self-hosted Deepgram speech AI models. It provides AWS Marketplace listings for Nova, Flux, and Aura-2, along with validated reference architectures and Terraform modules that layer onto existing AWS infrastructure. To bring the same operational maturity to day-two support, Deepgram has integrated with <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies-temporary-delegation.html" target="_blank" rel="noopener">IAM temporary delegation</a>, a new IAM capability that grants partners scoped, time-limited, customer-approved access to specific resources, with no long-lived credentials, no cross-account roles to provision, and no shared secrets.</p> <p>In this post, we cover why Deepgram built on IAM temporary delegation, how the integration works end-to-end, and what it unlocks for customers running Deepgram speech models on SageMaker AI. With this integration, Deepgram has reduced the time for initial investigation on a SageMaker AI support ticket from days to minutes. Previously, initial investigation required scheduling a screen-share across customer and partner calendars. Now a customer approves a request in their own IAM console, which also reduces long-standing cross-account access.</p> <h2 id="amazon-sagemaker-ai-delivers-a-managed-control-plane-for-self-hosted-deepgram">Amazon SageMaker AI delivers a managed control plane for self-hosted Deepgram</h2> <p>Enterprise customers choose to self-host Deepgram for the usual reasons: data residency, network isolation, and regulatory compliance. But they don’t want to trade away the operational posture of a managed cloud service to get there. SageMaker AI is how we close that gap: a single AWS native control plane for deploying, scaling, and observing Deepgram speech models inside the customer’s own account.</p> <p>Deepgram has invested in SageMaker AI as a first-class deployment target across the axes that matter most to enterprise platform teams:</p> <ul> <li>AWS Marketplace listings for Deepgram’s Nova, Flux, and Aura spanning speech-to-text (STT) and text-to-speech (TTS) speech models, with one-click subscription and consolidated AWS billing.</li> <li>Validated reference architectures covering Amazon Virtual Private Cloud (Amazon VPC) isolation, AWS PrivateLink, auto scaling, and observability.</li> <li>Terraform modules that platform teams can layer onto their existing AWS infrastructure instead of building the deployment from scratch.</li> </ul> <p>The result is a self-hosted experience that no longer means “you’re on your own.”</p> <h2 id="the-support-access-problem">The support access problem</h2> <p>Production deployments eventually need support. A model is returning unexpected results, an endpoint is autoscaling slower than expected, graphics processing unit (GPU) utilization looks off. The engineer best positioned to diagnose it is on the Deepgram team. The challenge they face is how to investigate a workload running inside a customer Amazon VPC they don’t have access to.</p> <p>The traditional options all have drawbacks. Long-lived cross-account IAM roles work, but customers don’t want to provision, audit, and remember to revoke them, and platform teams don’t want to be the ones answering for them at the next audit. Shared screens and copy-pasted logs are slow, error-prone, and a poor fit for regulated environments where every command needs to be attributable. Asking the customer to run commands on Deepgram’s behalf works for trivial is