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
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...