Amazon CloudWatch Introduces OpenTelemetry Metrics Support in Preview
Summary
Amazon CloudWatch has launched a public preview of OpenTelemetry metrics support, enabling developers to send metrics directly to CloudWatch using the OpenTelemetry protocol. This update introduces a high-cardinality metrics store, supporting up to 150 labels per metric, and automatically enriches ingested metrics with AWS resource context like account ID, Region, and resource tags. CloudWatch now supports OpenTelemetry across all three observability pillars: traces, logs, and metrics, allowing a single protocol for telemetry ingestion. Teams can analyze these metrics using PromQL within the CloudWatch console, facilitating dashboard creation and alarm setup for applications across various environments, including Kubernetes and on-premises systems. The feature is available in five regions, including Northern Virginia and Ireland, and is free during the preview period. AWS also introduced OpenTelemetry-based Container Insights for Amazon EKS.
Key takeaway
For AI Architects and CTOs evaluating observability strategies, this CloudWatch update simplifies metric ingestion by natively supporting OpenTelemetry. You can consolidate infrastructure, container, and application metrics in one place, reducing the need for complex conversion pipelines. Be mindful of potential long-term costs for high-cardinality metrics once the preview period concludes, and plan your metric retention and cardinality carefully.
Key insights
CloudWatch now natively ingests OpenTelemetry metrics, completing its observability suite and simplifying metric collection.
Principles
- High-cardinality metrics are supported.
- AWS automatically enriches metrics with resource context.
- PromQL is supported for metric analysis.
In practice
- Send rich metrics directly to CloudWatch.
- Use PromQL for metric analysis in CloudWatch.
- Monitor Kubernetes and on-premises systems.
Topics
- Amazon CloudWatch
- OpenTelemetry Metrics
- PromQL
- High-Cardinality Metrics
- Observability
Best for: AI Architect, CTO, VP of Engineering/Data, MLOps Engineer, DevOps Engineer, Software Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.