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Dynatrace Announces Enhanced AI-Powered Observability for Microsoft Azure

Dynatrace announced an extension of its Software Intelligence Platform to automatically ingest metrics from all services supported by Microsoft Azure Monitor, Microsoft’s solution for collecting telemetry data from Azure environments.

Combining these metrics with the data already captured by the Dynatrace platform provides customers with even more precise insights into their Azure and multicloud environments, driving faster cloud adoption and more effective digital transformation.

With this extension, Dynatrace will ingest metrics from the complete set of over 80 Azure Monitor services that span application and microservices workloads, as well as infrastructure-related services. This means metrics from services such as Azure HDInsight for Apache Hadoop, Spark and Kafka, Azure Container Instances for easy container deployment, and Azure Kubernetes Service are now automatically combined with the distributed tracing, log, user experience, and other observability data already processed by the Dynatrace platform. With this richer data set from Azure, Dynatrace’s Smartcape which continuously maps a customer’s full-stack topology, and the Dynatrace AI engine, Davis, can instantly identify and prioritize a broader set of issues and anomalies in Azure environments, and provide even more precise answers, saving organizations time, money, and resources.

“Dynatrace is purpose-built for cloud environments such as Microsoft Azure, with automation and intelligence at the core,” said Steve Tack, SVP Product Management, Dynatrace. “While we have always provided distributed tracing and code-level insights for applications running on Azure, extending our platform to ingest all metrics from all Azure Monitor services enables Dynatrace and Azure customers to migrate more services to the cloud and transform even faster, with greater confidence and less risk.”

“The need for faster digital transformation is driving organizations to increase their investment in cloud-native development using Microsoft Azure,” said Casey McGee, VP, Global ISV Sales, Microsoft. “We are pleased that Dynatrace is extending its AI-powered Software Intelligence Platform to ingest metrics from all Azure Monitor services, enabling better automation and intelligence, both of which are critical for customers as they transform.”

Dynatrace’s ability to ingest metrics from all Azure Monitor services will be available within the next 90 days.

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Dynatrace Announces Enhanced AI-Powered Observability for Microsoft Azure

Dynatrace announced an extension of its Software Intelligence Platform to automatically ingest metrics from all services supported by Microsoft Azure Monitor, Microsoft’s solution for collecting telemetry data from Azure environments.

Combining these metrics with the data already captured by the Dynatrace platform provides customers with even more precise insights into their Azure and multicloud environments, driving faster cloud adoption and more effective digital transformation.

With this extension, Dynatrace will ingest metrics from the complete set of over 80 Azure Monitor services that span application and microservices workloads, as well as infrastructure-related services. This means metrics from services such as Azure HDInsight for Apache Hadoop, Spark and Kafka, Azure Container Instances for easy container deployment, and Azure Kubernetes Service are now automatically combined with the distributed tracing, log, user experience, and other observability data already processed by the Dynatrace platform. With this richer data set from Azure, Dynatrace’s Smartcape which continuously maps a customer’s full-stack topology, and the Dynatrace AI engine, Davis, can instantly identify and prioritize a broader set of issues and anomalies in Azure environments, and provide even more precise answers, saving organizations time, money, and resources.

“Dynatrace is purpose-built for cloud environments such as Microsoft Azure, with automation and intelligence at the core,” said Steve Tack, SVP Product Management, Dynatrace. “While we have always provided distributed tracing and code-level insights for applications running on Azure, extending our platform to ingest all metrics from all Azure Monitor services enables Dynatrace and Azure customers to migrate more services to the cloud and transform even faster, with greater confidence and less risk.”

“The need for faster digital transformation is driving organizations to increase their investment in cloud-native development using Microsoft Azure,” said Casey McGee, VP, Global ISV Sales, Microsoft. “We are pleased that Dynatrace is extending its AI-powered Software Intelligence Platform to ingest metrics from all Azure Monitor services, enabling better automation and intelligence, both of which are critical for customers as they transform.”

Dynatrace’s ability to ingest metrics from all Azure Monitor services will be available within the next 90 days.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...