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SolarWinds Adds New AI-Powered Observability Capabilities

SolarWinds announced enhancements to its SaaS-based and self-hosted, on-premises observability solutions built to monitor and observe complex, distributed environments from anywhere.

The AI-powered enhancements enable teams to manage on-prem, hybrid, or cloud-native ecosystems with full-stack visibility across networks, infrastructure, databases, applications, user experiences, and security through a unified, integrated solution available either on-premises or in the cloud.

These transformative updates to the SolarWinds solutions come as organizations face increased monitoring and issue resolution challenges associated with their digital transformation efforts—from cloud migration to SD-WAN networks, modern application frameworks, and persistently hybrid workforces. Recent Gartner® research has suggested that through 2027, 50% of critical applications will reside outside of centralized public clouds. With proven SolarWinds AI-powered observability solutions, organizations can gain full, unified visibility into the entire technology stack through both a self-hosted solution and a born-in-the-cloud SaaS offering.

SolarWinds enables enterprises to integrate on-prem and cloud ecosystems into one holistic view—so they can improve the user experience and increase performance through proactive issue detection and accelerated problem-solving.

“The rapidly evolving technology landscape and organizations’ ongoing modernization journeys drive the digital complexity they face. When operating in a hybrid, multi-cloud, containerized microservices world, there’s one thing we know for sure: this complexity isn’t magically going away,” said Cullen Childress, SolarWinds SVP, Product. “At the same time, uptime and service level requirements continue to become more stringent while budgets remain stagnant. For nearly 25 years, we’ve given IT pros the tools they need to do their jobs more easily and with greater satisfaction. That’s why our message today is clear: with SolarWinds, you can see everything from anywhere.”

The new capabilities in SolarWinds observability solutions include further enhancements to its proven broad and deep network and infrastructure Observability, offering complete hybrid visibility across on-premises and cloud networks. This includes on-premises and cloud network devices, virtual machines, hypervisors, containers, Kubernetes, and infrastructure-as-a-service resources. SolarWinds further extends its world-class database observability capabilities by adding query explorer and visual explain plans. Additionally, AIOps-enabled pattern recognition and anomaly detection provide insights into correlated alerts and events to accelerate root cause analysis, allowing IT teams to be more productive as the organization scales.

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.

SolarWinds Adds New AI-Powered Observability Capabilities

SolarWinds announced enhancements to its SaaS-based and self-hosted, on-premises observability solutions built to monitor and observe complex, distributed environments from anywhere.

The AI-powered enhancements enable teams to manage on-prem, hybrid, or cloud-native ecosystems with full-stack visibility across networks, infrastructure, databases, applications, user experiences, and security through a unified, integrated solution available either on-premises or in the cloud.

These transformative updates to the SolarWinds solutions come as organizations face increased monitoring and issue resolution challenges associated with their digital transformation efforts—from cloud migration to SD-WAN networks, modern application frameworks, and persistently hybrid workforces. Recent Gartner® research has suggested that through 2027, 50% of critical applications will reside outside of centralized public clouds. With proven SolarWinds AI-powered observability solutions, organizations can gain full, unified visibility into the entire technology stack through both a self-hosted solution and a born-in-the-cloud SaaS offering.

SolarWinds enables enterprises to integrate on-prem and cloud ecosystems into one holistic view—so they can improve the user experience and increase performance through proactive issue detection and accelerated problem-solving.

“The rapidly evolving technology landscape and organizations’ ongoing modernization journeys drive the digital complexity they face. When operating in a hybrid, multi-cloud, containerized microservices world, there’s one thing we know for sure: this complexity isn’t magically going away,” said Cullen Childress, SolarWinds SVP, Product. “At the same time, uptime and service level requirements continue to become more stringent while budgets remain stagnant. For nearly 25 years, we’ve given IT pros the tools they need to do their jobs more easily and with greater satisfaction. That’s why our message today is clear: with SolarWinds, you can see everything from anywhere.”

The new capabilities in SolarWinds observability solutions include further enhancements to its proven broad and deep network and infrastructure Observability, offering complete hybrid visibility across on-premises and cloud networks. This includes on-premises and cloud network devices, virtual machines, hypervisors, containers, Kubernetes, and infrastructure-as-a-service resources. SolarWinds further extends its world-class database observability capabilities by adding query explorer and visual explain plans. Additionally, AIOps-enabled pattern recognition and anomaly detection provide insights into correlated alerts and events to accelerate root cause analysis, allowing IT teams to be more productive as the organization scales.

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.