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
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...
For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...
Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...
Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...