Deepgram deepens Amazon SageMaker AI observability with Enhanced Metrics | Amazon Web Services
Self-hosted speech AI has historically carried an observability trade-off. The service can tell you an endpoint is up and how many requests it served. The questions that actually drive capacity planning and cost management stay locked inside the vendor’s container: what you are billed for, which features your traffic uses, and what the inference engine is doing on each GPU.
If you run Deepgram’s speech-to-text (STT) and text-to-speech (TTS) models on SageMaker AI, audio and transcripts stay inside your own AWS account. This can help support your data residency and compliance efforts without giving up a managed control plane for deployment, scaling, and monitoring. Your specific obligations depend on your own controls and assessments, so consult your compliance team and review the AWS shared responsibility model.
Deepgram is closing the gap on billing, feature usage, and engine behavior with the following two innovations, available today on Deepgram SageMaker AI deployments.
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