Skip to main content

SolarWinds New Capabilites for IT Operational Resiliency

SolarWinds announced new enhancements across the SolarWinds portfolio offering expanded capabilities across observability, incident response, service management, and AI-powered automation—empowering IT teams to navigate complex hybrid environments, accelerate issue resolution, and ensure business continuity in an increasingly complex hybrid IT landscape.

“One of the biggest concerns we hear from customers is how to stay resilient amid rapid technological advancements and economic pressures,” said Cullen Childress, Chief Product Officer at SolarWinds. “Every new wave of change—from digital transformation to generative AI—feels like a storm threatening their business. They need solutions that not only help them adapt but also strengthen their ability to thrive in the face of disruption.”

The SolarWinds integrated portfolio of observability, incident response, and service management, powered by SolarWinds® AI, correlates alerts, improves decision-making, and accelerates issue resolution. This unified approach enhances performance, availability, and control across complex hybrid IT ecosystems to deliver unmatched operational resilience.

Key Enhancements Across the SolarWinds Portfolio:

  • Squadcast Incident Response:  New to the SolarWinds portfolio, Squadcast Incident Response unites people, processes, and technology, providing a proactive, structured approach to incident response and resolution. Squadcast brings AI-powered alert isolation, on-call management, multi-source alert correlation, standardized runbooks, status pages, and Microsoft Teams® and Slack® integration for incident swarming, leading to faster issue identification so organizations can minimize downtime, reduce remediation time, and maintain operational resilience.
  • SolarWinds Observability

-Now supports expanded hybrid IT awareness with deeper and broader single-pane-of-glass visibility across major cloud vendors, including GCP, AWS®, Azure®, and on-premises environments. These expanded capabilities help ensure a unified and detailed view of your entire hybrid IT environment, enabling proactive management and optimization.
-The AI-powered Log Insights feature surfaces critical insights from large volumes of log data, identifying patterns, anomalies, and trends that might indicate potential issues. This aids in proactive problem resolution and improves operational resilience by detecting issues before they become major incidents.
-Root Cause Assist leverages SolarWinds AI to help identify the underlying causes of problems or issues by analyzing data and providing rich, contextual insights. This function automates and accelerates the analysis of application performance issues.

  • SolarWinds Database Observability: Entering Tech Preview, SolarWinds AI Query Assist improves database queries by automatically analyzing query patterns and suggesting optimal query rewrites. This provides more accurate and efficient query optimization, helping DBAs improve efficiency and lower production costs caused by excessively long-running queries.
  • SolarWinds Service Desk: SolarWinds AI Runbook generation automates the manual and time-consuming task of compiling and formatting pre-written operational guides into new runbooks with standardized resolution processes that enhance operational efficiency and improve incident response times.  Data masking improves an organization’s compliance with governance and industry regulations of PII, PCI, and sensitive data by masking sensitive information and preventing inadvertent sharing.

“Learning and adapting, core pillars of operational resilience, have been at the heart of success for SolarWinds over the past 25 years,” said Sudhakar Ramakrishna, CEO of SolarWinds. “Our mission is to share that knowledge with our customers, equipping them with solutions that help them navigate the IT operational resiliency challenges of today and tomorrow’s dynamic IT landscape.”

The new enhancements to the SolarWinds portfolio are available now, with deployment options tailored to meet the needs of organizations of all sizes.

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

SolarWinds New Capabilites for IT Operational Resiliency

SolarWinds announced new enhancements across the SolarWinds portfolio offering expanded capabilities across observability, incident response, service management, and AI-powered automation—empowering IT teams to navigate complex hybrid environments, accelerate issue resolution, and ensure business continuity in an increasingly complex hybrid IT landscape.

“One of the biggest concerns we hear from customers is how to stay resilient amid rapid technological advancements and economic pressures,” said Cullen Childress, Chief Product Officer at SolarWinds. “Every new wave of change—from digital transformation to generative AI—feels like a storm threatening their business. They need solutions that not only help them adapt but also strengthen their ability to thrive in the face of disruption.”

The SolarWinds integrated portfolio of observability, incident response, and service management, powered by SolarWinds® AI, correlates alerts, improves decision-making, and accelerates issue resolution. This unified approach enhances performance, availability, and control across complex hybrid IT ecosystems to deliver unmatched operational resilience.

Key Enhancements Across the SolarWinds Portfolio:

  • Squadcast Incident Response:  New to the SolarWinds portfolio, Squadcast Incident Response unites people, processes, and technology, providing a proactive, structured approach to incident response and resolution. Squadcast brings AI-powered alert isolation, on-call management, multi-source alert correlation, standardized runbooks, status pages, and Microsoft Teams® and Slack® integration for incident swarming, leading to faster issue identification so organizations can minimize downtime, reduce remediation time, and maintain operational resilience.
  • SolarWinds Observability

-Now supports expanded hybrid IT awareness with deeper and broader single-pane-of-glass visibility across major cloud vendors, including GCP, AWS®, Azure®, and on-premises environments. These expanded capabilities help ensure a unified and detailed view of your entire hybrid IT environment, enabling proactive management and optimization.
-The AI-powered Log Insights feature surfaces critical insights from large volumes of log data, identifying patterns, anomalies, and trends that might indicate potential issues. This aids in proactive problem resolution and improves operational resilience by detecting issues before they become major incidents.
-Root Cause Assist leverages SolarWinds AI to help identify the underlying causes of problems or issues by analyzing data and providing rich, contextual insights. This function automates and accelerates the analysis of application performance issues.

  • SolarWinds Database Observability: Entering Tech Preview, SolarWinds AI Query Assist improves database queries by automatically analyzing query patterns and suggesting optimal query rewrites. This provides more accurate and efficient query optimization, helping DBAs improve efficiency and lower production costs caused by excessively long-running queries.
  • SolarWinds Service Desk: SolarWinds AI Runbook generation automates the manual and time-consuming task of compiling and formatting pre-written operational guides into new runbooks with standardized resolution processes that enhance operational efficiency and improve incident response times.  Data masking improves an organization’s compliance with governance and industry regulations of PII, PCI, and sensitive data by masking sensitive information and preventing inadvertent sharing.

“Learning and adapting, core pillars of operational resilience, have been at the heart of success for SolarWinds over the past 25 years,” said Sudhakar Ramakrishna, CEO of SolarWinds. “Our mission is to share that knowledge with our customers, equipping them with solutions that help them navigate the IT operational resiliency challenges of today and tomorrow’s dynamic IT landscape.”

The new enhancements to the SolarWinds portfolio are available now, with deployment options tailored to meet the needs of organizations of all sizes.

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...