Skip to main content

Dynatrace Announces New Integration with Azure Spring Cloud

Dynatrace, in partnership with Microsoft, announced a new integration that provides full application data transparency into applications deployed on Azure Spring Cloud.

Azure Spring Cloud makes it easy to deploy Spring Boot-based microservice applications to Azure with zero code changes. Azure Spring Cloud manages your application infrastructure so that you can focus on application code and business logic. While Azure Spring Cloud excels at removing much of the labor associated with managing containerized workloads, the challenge of monitoring and maintaining the performance and health of these applications, or of troubleshooting issues when they occur, can be daunting—especially as organizations deploy these applications at massive scale.

Dynatrace removes the complexity of dynamic microservice workloads by providing automatic and intelligent observability, without requiring any code changes. This all happens out-of-the-box with the Dynatrace OneAgent, which automatically discovers and maps all applications, microservices, and infrastructure as well as any dependencies in dynamic, hybrid, and multicloud environments, without configuration or scripting, and without having to know which apps or cloud platforms are running. This provides end-to-end visibility into the running of the application, saving time spent manually identifying any anomalies and allowing teams to get to the root cause of code-level issues quicker. This means full transparency into application data and more time to focus on developing feature-rich applications for your end-users.

The Azure Spring Cloud and Dynatrace integration brings freedom to application developers, allowing them to manage instances by abstracting the underlying infrastructure. With Dynatrace ingesting metrics for Azure Spring Cloud, teams can see metrics for each service instance, split metrics into multiple dimensions, and create custom charts they can pin to their dashboards. By automatically delivering metrics for each instance, the Dynatrace Platform enables developers to focus where their effort matters most, on innovation.

“At Microsoft, we are committed to helping our customers modernize their applications and innovate faster than ever before,” Julia Liuson, Corporate VP, Developer Division, Microsoft. “By integrating a software intelligence solution like Dynatrace with Azure Spring Cloud, we can enable our customers with easy implementation of end-to-end observability, including automatic and continuous root-cause analysis, for their Spring Boot applications.”

“The ability to scale is critical for today’s digital business, as organizations have made the shift to cloud-native workloads and microservices,” said Eric Horsman, Global Director of Strategic Alliances at Dynatrace. “While cloud-native technologies and microservices have tremendous advantages, dynamic environments bring complexity that makes it difficult to understand the relationships and dependencies across an organization’s hybrid, multicloud ecosystem. Through the Dynatrace integration with Azure Spring Cloud, we are enabling full visibility into Spring Boot applications, which means more time innovating and a better product for end-users.”

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 ...

Dynatrace Announces New Integration with Azure Spring Cloud

Dynatrace, in partnership with Microsoft, announced a new integration that provides full application data transparency into applications deployed on Azure Spring Cloud.

Azure Spring Cloud makes it easy to deploy Spring Boot-based microservice applications to Azure with zero code changes. Azure Spring Cloud manages your application infrastructure so that you can focus on application code and business logic. While Azure Spring Cloud excels at removing much of the labor associated with managing containerized workloads, the challenge of monitoring and maintaining the performance and health of these applications, or of troubleshooting issues when they occur, can be daunting—especially as organizations deploy these applications at massive scale.

Dynatrace removes the complexity of dynamic microservice workloads by providing automatic and intelligent observability, without requiring any code changes. This all happens out-of-the-box with the Dynatrace OneAgent, which automatically discovers and maps all applications, microservices, and infrastructure as well as any dependencies in dynamic, hybrid, and multicloud environments, without configuration or scripting, and without having to know which apps or cloud platforms are running. This provides end-to-end visibility into the running of the application, saving time spent manually identifying any anomalies and allowing teams to get to the root cause of code-level issues quicker. This means full transparency into application data and more time to focus on developing feature-rich applications for your end-users.

The Azure Spring Cloud and Dynatrace integration brings freedom to application developers, allowing them to manage instances by abstracting the underlying infrastructure. With Dynatrace ingesting metrics for Azure Spring Cloud, teams can see metrics for each service instance, split metrics into multiple dimensions, and create custom charts they can pin to their dashboards. By automatically delivering metrics for each instance, the Dynatrace Platform enables developers to focus where their effort matters most, on innovation.

“At Microsoft, we are committed to helping our customers modernize their applications and innovate faster than ever before,” Julia Liuson, Corporate VP, Developer Division, Microsoft. “By integrating a software intelligence solution like Dynatrace with Azure Spring Cloud, we can enable our customers with easy implementation of end-to-end observability, including automatic and continuous root-cause analysis, for their Spring Boot applications.”

“The ability to scale is critical for today’s digital business, as organizations have made the shift to cloud-native workloads and microservices,” said Eric Horsman, Global Director of Strategic Alliances at Dynatrace. “While cloud-native technologies and microservices have tremendous advantages, dynamic environments bring complexity that makes it difficult to understand the relationships and dependencies across an organization’s hybrid, multicloud ecosystem. Through the Dynatrace integration with Azure Spring Cloud, we are enabling full visibility into Spring Boot applications, which means more time innovating and a better product for end-users.”

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 ...