
Dynatrace's fully-automated, AI-powered, full stack performance monitoring capabilities are available to customers of VMware Cloud on AWS.
Launched with initial availability in AWS US West region, VMware Cloud on AWS brings together VMware’s enterprise-class Software-Defined Data Center (SDDC) software and elastic, bare-metal infrastructure from Amazon Web Services (AWS) to give organizations consistent operating model and application mobility for private and public cloud. Dynatrace enables VMware Cloud on AWS customers to gain a deep understanding of their hyper-dynamic public, private and hybrid cloud environments.
Dynatrace’s AI-powered, full stack monitoring solution enables full transaction visibility. Its AI capabilities can continuously auto-detect dependencies, learn application behavior, detect anomalies and proactively pinpoint the root cause of performance problems. Preconfigured and automatically adjusted dashboards allow organizations to maintain visibility into their hyper-dynamic application environments during vMotion events with ease.
The automated capabilities of Dynatrace also allow users to baseline application performance and instantly identify any negative impact on resource consumption or user-experience during the migration of running virtual machines in hyper-dynamic hybrid environments. This enables them to migrate virtual machines between their private VMware vSphere-based data center and the AWS cloud with confidence, monitoring the whole vMotion event through a single pane of glass.
“Dynatrace’s longstanding collaboration with AWS provides a tried-and-tested monitoring solution for customers of VMware Cloud on AWS,” said Franz Karlsberger, Director, Strategic Partners at Dynatrace. “The AI capabilities in Dynatrace provide a way for organizations to automate the management and monitoring of these hyper-complex, hyper-dynamic environments. Its ability to auto-discover application dependencies and baseline performance, and to instantly pinpoint the root cause of performance problems before they impact the end-user will be invaluable in helping organizations maximize the benefits of VMware Cloud on AWS.”
VMware Cloud on AWS technology partners enable customers to deploy the same proven solutions seamlessly in both the public and private cloud. VMware simplifies the deployment and eliminates the need for partners to refactor solutions for VMware Cloud on AWS. If a partner solution works on-premises in a VMware vSphere environment, it will easily support VMware Cloud on AWS. VMware technology partners complement and enhance native VMware Cloud on AWS service and enable customers to realize new capabilities.
“VMware Cloud on AWS provides customers a seamlessly integrated hybrid cloud offering that gives customers the SDDC experience from the leader in private cloud, running on the leading public cloud provider, AWS,” said Mark Lohmeyer, VP Products, Cloud Platforms Business Unit, VMware. “Solutions such as Dynatrace enable IT teams to reduce cost, increase efficiency, and create operational consistency across cloud environments. We’re excited to work with partners such as Dynatrace to enhance native VMware Cloud on AWS capabilities and empower customers with flexibility and choice in solutions that can drive business value.”
The Latest
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 ...
