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SolarWinds Enhances Partner Program

SolarWinds unveils the next phase of enhancements to the SolarWinds Partner Program. 

These updates focus on three key areas: increasing partner profitability, partner capabilities, and providing partners with the tools needed to elevate together.

"At SolarWinds, we are dedicated to creating lasting value for partners through collaboration, shared growth, and a unified vision," said Andre Cuenin, Chief Revenue Officer at SolarWinds. "This year's Partner Program theme, 'Elevating Together,' aims to empower partners to adapt to emerging technologies and evolving customer demands, focusing on channel success, growth, profitability, and exceptional customer satisfaction for a resilient and innovative future."

SolarWinds expanded the partner purchase tiers through a new three-tier model with enhanced revenue segmentation and margin control to deliver even greater value and boost partner profitability in 2025. These changes will go into effect in the coming months.

Along with that, this year’s program includes growth incentives, which reward partners for growth in SolarWinds' Observability (Self-Hosted and SaaS), Database, and IT Service Management (ITSM) solutions.

SolarWinds Services Certification Program (SCCP) is a new certification program designed to certify services partners to sell and deliver SolarWinds Premium Support Add-On services to customers. To become a certified services partner, partners’ team must include a SolarWinds Certified Professional (SCP) and a SolarWinds Certified Instructor (SCI).

SolarWinds also enhanced its partner portal experience to provide users with innovative and powerful new tools and a better user experience. These updates will improve partner business planning, partner marketing automation tools, integrated Google Ads activation, and updated lead-sharing capabilities, among other benefits.

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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 Enhances Partner Program

SolarWinds unveils the next phase of enhancements to the SolarWinds Partner Program. 

These updates focus on three key areas: increasing partner profitability, partner capabilities, and providing partners with the tools needed to elevate together.

"At SolarWinds, we are dedicated to creating lasting value for partners through collaboration, shared growth, and a unified vision," said Andre Cuenin, Chief Revenue Officer at SolarWinds. "This year's Partner Program theme, 'Elevating Together,' aims to empower partners to adapt to emerging technologies and evolving customer demands, focusing on channel success, growth, profitability, and exceptional customer satisfaction for a resilient and innovative future."

SolarWinds expanded the partner purchase tiers through a new three-tier model with enhanced revenue segmentation and margin control to deliver even greater value and boost partner profitability in 2025. These changes will go into effect in the coming months.

Along with that, this year’s program includes growth incentives, which reward partners for growth in SolarWinds' Observability (Self-Hosted and SaaS), Database, and IT Service Management (ITSM) solutions.

SolarWinds Services Certification Program (SCCP) is a new certification program designed to certify services partners to sell and deliver SolarWinds Premium Support Add-On services to customers. To become a certified services partner, partners’ team must include a SolarWinds Certified Professional (SCP) and a SolarWinds Certified Instructor (SCI).

SolarWinds also enhanced its partner portal experience to provide users with innovative and powerful new tools and a better user experience. These updates will improve partner business planning, partner marketing automation tools, integrated Google Ads activation, and updated lead-sharing capabilities, among other benefits.

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 ...