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Dynatrace Introduces New Infrastructure Monitoring Module

Dynatrace announced the next generation of its Infrastructure Monitoring module in its all-in-one Software Intelligence Platform.

The latest upgrades, including enhanced AI, expanded out-of-the-box observability, and the ability to create custom metrics from log events, provide Dynatrace customers with greater efficiency, simplicity and speed as they undergo digital transformation.

“With our new enhancements, our radically different approach to infrastructure observability just got a whole lot better,” said Steve Tack, SVP of Product Management at Dynatrace. “From the beginning we built Dynatrace to deliver full-stack observability to dynamic, cloud-first environments. Our latest infrastructure monitoring module leverages the answers-first approach delivered by the AI and advanced automation capabilities at the core of our all-in-one Software Intelligence Platform. As a result, Dynatrace enables IT teams to keep up with growing complexity and removes the need for multiple tools and manual do-it-yourself approaches. This helps these teams lead their organizations through digital transformation with speed, simplicity, and confidence.”

New enhancements to the Dynatrace Infrastructure Monitoring module include:

- Extended out-of-the-box observability for cloud-native environments – Dynatrace now automatically ingests data from additional sources, including new AWS and Azure services, Kubernetes-native events, Prometheus OpenMetrics, and Spring Micrometer metrics. This provides out-of-the-box, comprehensive observability at scale, plus more precise answers to enable faster problem resolution, improved productivity, and rapid innovation in multi-cloud environments.

- Custom metrics and events from log monitoring – The Dynatrace platform can now create custom metrics and events based on log data so organizations can extend infrastructure observability to any application, script, or process that writes to a log file. This facilitates tool consolidation, and reduces the cost and effort involved in manual administration.

- Smarter infrastructure monitoring – The Dynatrace Davis AI engine now automatically provides thresholds and baselining algorithms for all infrastructure performance and reliability metrics, extending root-cause analysis and enabling organizations to easily scale infrastructure monitoring without manual configuration in dynamic cloud environments. As a result, organizations gain access to precise answers in real time, supporting faster innovation while ensuring infrastructure performance and availability.

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Dynatrace Introduces New Infrastructure Monitoring Module

Dynatrace announced the next generation of its Infrastructure Monitoring module in its all-in-one Software Intelligence Platform.

The latest upgrades, including enhanced AI, expanded out-of-the-box observability, and the ability to create custom metrics from log events, provide Dynatrace customers with greater efficiency, simplicity and speed as they undergo digital transformation.

“With our new enhancements, our radically different approach to infrastructure observability just got a whole lot better,” said Steve Tack, SVP of Product Management at Dynatrace. “From the beginning we built Dynatrace to deliver full-stack observability to dynamic, cloud-first environments. Our latest infrastructure monitoring module leverages the answers-first approach delivered by the AI and advanced automation capabilities at the core of our all-in-one Software Intelligence Platform. As a result, Dynatrace enables IT teams to keep up with growing complexity and removes the need for multiple tools and manual do-it-yourself approaches. This helps these teams lead their organizations through digital transformation with speed, simplicity, and confidence.”

New enhancements to the Dynatrace Infrastructure Monitoring module include:

- Extended out-of-the-box observability for cloud-native environments – Dynatrace now automatically ingests data from additional sources, including new AWS and Azure services, Kubernetes-native events, Prometheus OpenMetrics, and Spring Micrometer metrics. This provides out-of-the-box, comprehensive observability at scale, plus more precise answers to enable faster problem resolution, improved productivity, and rapid innovation in multi-cloud environments.

- Custom metrics and events from log monitoring – The Dynatrace platform can now create custom metrics and events based on log data so organizations can extend infrastructure observability to any application, script, or process that writes to a log file. This facilitates tool consolidation, and reduces the cost and effort involved in manual administration.

- Smarter infrastructure monitoring – The Dynatrace Davis AI engine now automatically provides thresholds and baselining algorithms for all infrastructure performance and reliability metrics, extending root-cause analysis and enabling organizations to easily scale infrastructure monitoring without manual configuration in dynamic cloud environments. As a result, organizations gain access to precise answers in real time, supporting faster innovation while ensuring infrastructure performance and availability.

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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 ...