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Dynatrace Announces Enhanced Observability for All AWS Services

Dynatrace announced the extension of its Software Intelligence Platform to support all services from Amazon Web Services (AWS) that publish metrics to Amazon CloudWatch, a fully managed AWS service that provides monitoring and observability of AWS resources and applications on AWS and on-premises environments.

Combining Amazon CloudWatch metrics with the data already captured by the Dynatrace platform provides customers with richer context and more precise answers for their dynamic AWS and hybrid-cloud environments, helping to drive faster cloud adoption and accelerate their digital transformation.

With these enhancements, Dynatrace will automatically identify and collect metrics from the 95 AWS services currently supported by Amazon CloudWatch. This enriches Dynatrace’s AI-powered answers with the latest services from AWS, including Amazon MSK, Amazon Route 53, Amazon Sagemaker, Amazon Neptune, and Amazon MQ. Metrics from these and all services that publish metrics to CloudWatch are automatically combined with the distributed tracing, log, user experience, and other observability data already processed by the Dynatrace platform. As a result, Dynatrace-AWS customers not only get all CloudWatch metrics streamed to dashboards automatically, they also gain automatic, self-adjusting baselining, immediate anomaly detection, and precise root-cause determination prioritized by business impact across their entire AWS and hybrid/multicloud environment. This combination of CloudWatch metrics, additional observability data, automation, and AI-powered assistance saves digital teams considerable time and resources, allowing them to focus on innovative, high-value tasks that drive better business outcomes.

“As organizations increasingly invest in cloud-native development using AWS, as well as microservices and Kubernetes architectures, complete visibility into these dynamic environments is critical,” said Bob Wilkinson, GM Monitoring and Observability Services, Amazon Web Services, Inc. “Organizations need the right capabilities to achieve that level of visibility, and Dynatrace can be a key part of the solution along with Amazon CloudWatch. I am excited the Dynatrace Software Intelligence Platform now supports any AWS service that publishes metrics to CloudWatch. This enables even greater automation and observability for customers as they migrate architectures to AWS.”

“Our customers are accelerating their digital transformation, and many are adopting AWS to help them on that journey. We built our Software Intelligence Platform purposefully for dynamic environments such as AWS, with AI-assistance and continuous automation at the core,” said Steve Tack, SVP Product Management, Dynatrace. “We’ve always delivered distributed tracing and code-level insights for applications and microservices running on AWS. This enhanced AWS integration allows us to provide rapid support as Amazon introduces new services for observability into any layer or service in their cloud stack. We are proud of our relationship with AWS and the enhanced value we continue to deliver to joint customers.”

Dynatrace’s ability to ingest metrics from the 95 AWS CloudWatch services will be available within the next 60 days.

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Dynatrace Announces Enhanced Observability for All AWS Services

Dynatrace announced the extension of its Software Intelligence Platform to support all services from Amazon Web Services (AWS) that publish metrics to Amazon CloudWatch, a fully managed AWS service that provides monitoring and observability of AWS resources and applications on AWS and on-premises environments.

Combining Amazon CloudWatch metrics with the data already captured by the Dynatrace platform provides customers with richer context and more precise answers for their dynamic AWS and hybrid-cloud environments, helping to drive faster cloud adoption and accelerate their digital transformation.

With these enhancements, Dynatrace will automatically identify and collect metrics from the 95 AWS services currently supported by Amazon CloudWatch. This enriches Dynatrace’s AI-powered answers with the latest services from AWS, including Amazon MSK, Amazon Route 53, Amazon Sagemaker, Amazon Neptune, and Amazon MQ. Metrics from these and all services that publish metrics to CloudWatch are automatically combined with the distributed tracing, log, user experience, and other observability data already processed by the Dynatrace platform. As a result, Dynatrace-AWS customers not only get all CloudWatch metrics streamed to dashboards automatically, they also gain automatic, self-adjusting baselining, immediate anomaly detection, and precise root-cause determination prioritized by business impact across their entire AWS and hybrid/multicloud environment. This combination of CloudWatch metrics, additional observability data, automation, and AI-powered assistance saves digital teams considerable time and resources, allowing them to focus on innovative, high-value tasks that drive better business outcomes.

“As organizations increasingly invest in cloud-native development using AWS, as well as microservices and Kubernetes architectures, complete visibility into these dynamic environments is critical,” said Bob Wilkinson, GM Monitoring and Observability Services, Amazon Web Services, Inc. “Organizations need the right capabilities to achieve that level of visibility, and Dynatrace can be a key part of the solution along with Amazon CloudWatch. I am excited the Dynatrace Software Intelligence Platform now supports any AWS service that publishes metrics to CloudWatch. This enables even greater automation and observability for customers as they migrate architectures to AWS.”

“Our customers are accelerating their digital transformation, and many are adopting AWS to help them on that journey. We built our Software Intelligence Platform purposefully for dynamic environments such as AWS, with AI-assistance and continuous automation at the core,” said Steve Tack, SVP Product Management, Dynatrace. “We’ve always delivered distributed tracing and code-level insights for applications and microservices running on AWS. This enhanced AWS integration allows us to provide rapid support as Amazon introduces new services for observability into any layer or service in their cloud stack. We are proud of our relationship with AWS and the enhanced value we continue to deliver to joint customers.”

Dynatrace’s ability to ingest metrics from the 95 AWS CloudWatch services will be available within the next 60 days.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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