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SolarWinds Integrates with Amazon Bedrock

SolarWinds announced expanded collaboration with Amazon Web Services (AWS) through new integrations with Amazon Bedrock, a fully managed service that offers access to high-performing foundation models from leading AI companies through a single API.

This relationship enhances AI innovation at SolarWinds on two fronts: first, using Amazon Bedrock powers generative AI capabilities within the SolarWinds® Platform and SolarWinds Service Desk, and second, enabling SolarWinds Observability to monitor Amazon Bedrock services directly, providing visibility into one of the world’s most advanced AI infrastructures.

SolarWinds leverages the Anthropic family of Claude models through Amazon Bedrock, delivering conversational, context-aware capabilities across its platform, including automated ticket summarization, incident correlation, and intelligent recommendations within SolarWinds Service Desk.

These new experiences are orchestrated through the SolarWinds LLM Gateway, a unified platform service built on AI by Design principles, which provides secure model abstraction across various models.

“Amazon Bedrock gives us a secure, scalable foundation to extend SolarWinds AI across our entire portfolio,” said Krishna Sai, CTO, SolarWinds. “It enables our teams to innovate faster and deliver real-time intelligence and downstream value to customers, while ensuring their data remains private, protected, and under their control.”

In parallel, SolarWinds Observability SaaS now monitors customer workloads on Amazon Bedrock as first-class monitored resources. IT teams can visualize Amazon Bedrock performance—including invocation rates, latency, token throughput, throttling, and overall health—through customizable dashboards and automated alerts powered by the SolarWinds Platform. This capability demonstrates how SolarWinds Observability SaaS can deliver end-to-end insight into even the most sophisticated AI workloads.

“With unified observability, organizations have confidence their AI systems perform reliably, securely, and at scale, accelerating their journey toward autonomous operational resilience,” said Cullen Childress, CPO, SolarWinds.

The SolarWinds AI Agent, announced earlier this fall, is built on Amazon Bedrock and serves as a digital teammate, summarizing incidents, identifying probable root causes, recommending fixes, and automating multi-step workflows through natural language interaction. These capabilities are complemented by recently announced features, such as Root Cause Assist and enhanced dynamic threshold capabilities, which together help IT professionals reduce noise, resolve issues more quickly, and focus on innovation rather than reactive issue management.

By embedding Anthropic Claude 4 and Claude 4.5 models through Amazon Bedrock, SolarWinds is extending AI Agent’s intelligence and automation across its observability, database, and IT service management products. The result is a unified, scalable foundation built on the company’s AI by Design and Secure by Design frameworks, ensuring every innovation is built with transparency, safety, and customer trust at its core.

Many SolarWinds AI features, powered by Amazon Bedrock, are already available in SolarWinds Service Desk. Additional features, such as the SolarWinds AI Agent, improved Root Cause Assist, and Database Query Assist, will soon be rolled out across other products, including SolarWinds Observability SaaS. 

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 ...

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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 Integrates with Amazon Bedrock

SolarWinds announced expanded collaboration with Amazon Web Services (AWS) through new integrations with Amazon Bedrock, a fully managed service that offers access to high-performing foundation models from leading AI companies through a single API.

This relationship enhances AI innovation at SolarWinds on two fronts: first, using Amazon Bedrock powers generative AI capabilities within the SolarWinds® Platform and SolarWinds Service Desk, and second, enabling SolarWinds Observability to monitor Amazon Bedrock services directly, providing visibility into one of the world’s most advanced AI infrastructures.

SolarWinds leverages the Anthropic family of Claude models through Amazon Bedrock, delivering conversational, context-aware capabilities across its platform, including automated ticket summarization, incident correlation, and intelligent recommendations within SolarWinds Service Desk.

These new experiences are orchestrated through the SolarWinds LLM Gateway, a unified platform service built on AI by Design principles, which provides secure model abstraction across various models.

“Amazon Bedrock gives us a secure, scalable foundation to extend SolarWinds AI across our entire portfolio,” said Krishna Sai, CTO, SolarWinds. “It enables our teams to innovate faster and deliver real-time intelligence and downstream value to customers, while ensuring their data remains private, protected, and under their control.”

In parallel, SolarWinds Observability SaaS now monitors customer workloads on Amazon Bedrock as first-class monitored resources. IT teams can visualize Amazon Bedrock performance—including invocation rates, latency, token throughput, throttling, and overall health—through customizable dashboards and automated alerts powered by the SolarWinds Platform. This capability demonstrates how SolarWinds Observability SaaS can deliver end-to-end insight into even the most sophisticated AI workloads.

“With unified observability, organizations have confidence their AI systems perform reliably, securely, and at scale, accelerating their journey toward autonomous operational resilience,” said Cullen Childress, CPO, SolarWinds.

The SolarWinds AI Agent, announced earlier this fall, is built on Amazon Bedrock and serves as a digital teammate, summarizing incidents, identifying probable root causes, recommending fixes, and automating multi-step workflows through natural language interaction. These capabilities are complemented by recently announced features, such as Root Cause Assist and enhanced dynamic threshold capabilities, which together help IT professionals reduce noise, resolve issues more quickly, and focus on innovation rather than reactive issue management.

By embedding Anthropic Claude 4 and Claude 4.5 models through Amazon Bedrock, SolarWinds is extending AI Agent’s intelligence and automation across its observability, database, and IT service management products. The result is a unified, scalable foundation built on the company’s AI by Design and Secure by Design frameworks, ensuring every innovation is built with transparency, safety, and customer trust at its core.

Many SolarWinds AI features, powered by Amazon Bedrock, are already available in SolarWinds Service Desk. Additional features, such as the SolarWinds AI Agent, improved Root Cause Assist, and Database Query Assist, will soon be rolled out across other products, including SolarWinds Observability SaaS. 

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 ...