At Perform, its flagship annual user conference, Dynatrace announced expanded cloud-native integrations across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
As organizations scale multi-cloud architectures and adopt more AI-driven applications, operating reliable digital services has become increasingly complex. Teams must manage performance, resilience, and cost across multiple cloud platforms while also supporting dynamic, data-intensive AI workloads.
Dynatrace’s expanded integrations bring added visibility across AWS, Azure, and Google Cloud into one unified view, enabling teams to find and fix issues faster and reduce risks to end-user experience. These capabilities are powered by an industry-leading unified data lakehouse, Grail™, as well as the Dynatrace Smartscape real-time dependency graph, and Dynatrace Intelligence, which together help teams understand, automate, and operate through growing multi-cloud complexity – turning it into a strategic advantage rather than an operational burden.
Enhancements further strengthen Dynatrace’s Cloud Operations capabilities, giving enterprises more streamlined way to manage performance, reliability, and costs across multi-cloud and AI-enabled environments.
Key enhancements include:
- Comprehensive visibility: Expanded telemetry and metadata improve insight into AWS, Azure, and Google Cloud services, helping teams better understand the health and behavior of cloud-native environments.
- Automated issue prevention: Ready-made health indicators, warning signals, and customizable alerts surface emerging risks early across cloud-native workloads, including those running on Azure Kubernetes Service and Azure AI Foundry.
- Automated remediation: Built-in automation resolves issues as they occur, reducing manual effort and minimizing user impact, regardless of where workloads are running.
- Automated optimization: Continuous assessment of cloud resource usage supports improved performance and cost efficiency across multi-cloud environments.
“With the updated cloud solution capabilities from Dynatrace, we are achieving a new standard for cloud operations,” said Alexandre Demailly, Head of Cloud Architecture Squad at SBS Software. “With Dynatrace, we have complete visibility into our cloud environments, moving us closer to achieving fully autonomous operations. This allows us to innovate more with less, all while maintaining end-to-end understanding and control of our technology stack.”
“As organizations continue to expand their cloud environments, the day-to-day reality of keeping applications reliable has become far more complex,” said Jay Snyder, Senior Vice President of Partners and Alliances at Dynatrace. “Teams are expected to deliver great performance, control costs, and maintain resilience across multiple cloud platforms at the same time. By expanding our cloud automation capabilities across AWS, Azure, and Google Cloud, Dynatrace not only makes it easier for platform teams to see what’s happening across their environments, but also to prevent issues automatically, before they have an impact on customers.”
“Dynatrace and AWS have worked together for years to help customers run critical workloads with confidence,” said Chris Grusz, Managing Director of Technology Partnerships at AWS. “As customer workloads grow in scale and complexity, teams need visibility and tools to identify and resolve issues quickly before they impact the business. These expanded integrations help our joint customers operate more efficiently and confidently at scale.”
“As enterprises increasingly adopt multi-cloud strategies, maintaining visibility and control has become a critical operational challenge,” said Steve McDowell, Chief Analyst and Founder of NAND Research. “Dynatrace’s expanded cloud-native integrations provide unified observability across the three major cloud platforms, enabling IT teams to manage performance, cost, and reliability from a single pane of glass. This approach to multi-cloud observability is essential for enterprises seeking to optimize cloud investments while maintaining operational excellence across a diverse multi-cloud infrastructure.”
Support for AWS is now generally available.
Azure support is in preview.
Google Cloud Platform support is also in preview.
The Latest
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 ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.