Skip to main content

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

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 ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

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 ...