ToolsAugust 27, 2026via AWS Machine Learning Blog
Deepgram deepens Amazon SageMaker AI observability with Enhanced Metrics
Why it matters
Self-hosted speech models have historically hidden resource usage and costs from operators. This integration gives practitioners real observability for capacity planning and cost control — lowering friction for production deployments.
Key signals
- Deepgram integrates with Amazon SageMaker AI
- New capabilities: per-GPU metrics, billing data, usage metrics exported to CloudWatch
- Targets self-hosted speech AI deployment visibility gap
- Solves cost and capacity planning opacity in vendor containers
- Deepgram integration with Amazon SageMaker AI
- Metrics exposed: billing, usage, per-GPU performance
- Destination: Amazon CloudWatch (native AWS observability)
- Use case: self-hosted speech AI observability
- Problem solved: vendor metrics trapped in containers now accessible to operators
The hook
Speech AI operators just got visibility into the black box: Deepgram + SageMaker now surface per-GPU metrics and billing data to CloudWatch.
Self-hosted speech AI carries an observability trade-off: the numbers that drive capacity planning and cost management stay locked inside the vendor container. Deepgram closes that gap on Amazon SageMaker AI with two capabilities that land billing, usage, and per-GPU metrics directly in your own Ama…