Skip to main content

SolarWinds Enhances AppOptics with New Service- and Trace-Level Root Cause Analysis

SolarWinds announced updates to its SaaS-based infrastructure and application performance monitoring (APM) solution, AppOptics, making application troubleshooting simpler, faster, and more accurate, with new service- and trace-level root cause analysis functionality and insights, further reducing the complexity of APM and lowering the barrier of adoption for technology professionals.

AppOptics delivers full-stack infrastructure and application performance monitoring for hybrid and cloud environments with precise metrics down to the transaction level. The new service- and trace-level root cause analysis functionality and dashboards with actionable insights differentiate AppOptics in the APM market by going beyond charts and data and telling tech pros exactly where and when application issues arise regardless of application architecture—traditional on-premises monolithic, N-tier service-oriented, and microservices-based—simplifying the complexity of APM and freeing up troubleshooting time, so they can quickly resolve performance issues and focus on innovating the end-user experience.

“The ability to decode APM data to troubleshoot an application has required skills that ultimately leave much of the analysis and problem-solving to individual interpretation that eats up a significant amount of time and resources,” said Jim Hansen, VP of Products, Application Management, SolarWinds. “With the AppOptics service- and trace-level root analysis updates, we’re removing that barrier by doing the heavy lifting of showing and explaining what the problem is within an easy-to-understand dashboard. SolarWinds is helping more tech pros, who may still be cultivating deeper APM expertise, implement the tenets of APM strategies—deepening our commitment to empower tech pros with easy to use, powerful, and affordable tools that they need today.”

Service-level root cause analysis helps simplify the process of application troubleshooting by surfacing and clearly communicating the relevant performance changes and anomalies in layman’s terms, such as flagging a specific service experienced a 20 percent slowdown at the database layer as compared to the week prior. This simplification reduces the number of steps and clicks required to identify the specific performance problem or issue and ultimately speed time to resolution.

For customers seeking deeper visibility into the performance of specific requests, the new AppOptics trace-level root cause analysis similarly provides the necessary clarity for tech pros to drill down into application metrics and have code-level visibility into performance issues with speed and accuracy.

For those starting their APM journey or looking to benefit from APM in development and pre-production environments, the service- and trace-level root analysis features are also available in the full-feature AppOptics Dev Edition free tool.

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 ...

SolarWinds Enhances AppOptics with New Service- and Trace-Level Root Cause Analysis

SolarWinds announced updates to its SaaS-based infrastructure and application performance monitoring (APM) solution, AppOptics, making application troubleshooting simpler, faster, and more accurate, with new service- and trace-level root cause analysis functionality and insights, further reducing the complexity of APM and lowering the barrier of adoption for technology professionals.

AppOptics delivers full-stack infrastructure and application performance monitoring for hybrid and cloud environments with precise metrics down to the transaction level. The new service- and trace-level root cause analysis functionality and dashboards with actionable insights differentiate AppOptics in the APM market by going beyond charts and data and telling tech pros exactly where and when application issues arise regardless of application architecture—traditional on-premises monolithic, N-tier service-oriented, and microservices-based—simplifying the complexity of APM and freeing up troubleshooting time, so they can quickly resolve performance issues and focus on innovating the end-user experience.

“The ability to decode APM data to troubleshoot an application has required skills that ultimately leave much of the analysis and problem-solving to individual interpretation that eats up a significant amount of time and resources,” said Jim Hansen, VP of Products, Application Management, SolarWinds. “With the AppOptics service- and trace-level root analysis updates, we’re removing that barrier by doing the heavy lifting of showing and explaining what the problem is within an easy-to-understand dashboard. SolarWinds is helping more tech pros, who may still be cultivating deeper APM expertise, implement the tenets of APM strategies—deepening our commitment to empower tech pros with easy to use, powerful, and affordable tools that they need today.”

Service-level root cause analysis helps simplify the process of application troubleshooting by surfacing and clearly communicating the relevant performance changes and anomalies in layman’s terms, such as flagging a specific service experienced a 20 percent slowdown at the database layer as compared to the week prior. This simplification reduces the number of steps and clicks required to identify the specific performance problem or issue and ultimately speed time to resolution.

For customers seeking deeper visibility into the performance of specific requests, the new AppOptics trace-level root cause analysis similarly provides the necessary clarity for tech pros to drill down into application metrics and have code-level visibility into performance issues with speed and accuracy.

For those starting their APM journey or looking to benefit from APM in development and pre-production environments, the service- and trace-level root analysis features are also available in the full-feature AppOptics Dev Edition free tool.

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 ...