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SolarWinds Joins AWS ISV Workload Migration Program

SolarWinds has joined the Amazon Web Services® (AWS®) Independent Software Vendor (ISV) Workload Migration Program, which supports AWS Partner Network (APN) members with a software as a service (SaaS) offering on AWS, to drive and deliver workload migrations.

The AWS ISV Workload Migration Program provides SolarWinds with funding, technical support, and go-to-market support to help rapidly migrate customers. Customers will now be able to benefit from reduced migration timelines and costs as they transition their software to the cloud.

“Cloud migration can be a complex and high-stakes process for today’s enterprises,” said Barb Huelskamp, Vice President of Channel Sales at SolarWinds. “That’s why customers turn to SolarWinds Service Desk—not just for a smoother migration, but for our intuitive user experience, powerful automation, and seamless integration with AWS environments. Through the AWS ISV Workload Migration Program, we’re helping customers accelerate their cloud journey while maximizing ROI and driving innovation with confidence.”

SolarWinds has long been committed to helping its customers maximize their cloud assets through purpose-built observability SaaS offerings. Most recently, the company expanded its offerings with a new SaaS data center based in Sydney, Australia. The new data center brings observability solutions closer to customers in the Asia-Pacific and Japan regions, providing low latency, better responsiveness, and a choice of local data storage.

SolarWinds products are available in AWS Marketplace. In addition, SolarWinds has achieved the DevOps ISV Competency and Microsoft® Workloads ISV Competency. This means SolarWinds has been recognized by AWS for meeting high standards in specialization, deep AWS expertise, and proven customer success.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

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 ...

SolarWinds Joins AWS ISV Workload Migration Program

SolarWinds has joined the Amazon Web Services® (AWS®) Independent Software Vendor (ISV) Workload Migration Program, which supports AWS Partner Network (APN) members with a software as a service (SaaS) offering on AWS, to drive and deliver workload migrations.

The AWS ISV Workload Migration Program provides SolarWinds with funding, technical support, and go-to-market support to help rapidly migrate customers. Customers will now be able to benefit from reduced migration timelines and costs as they transition their software to the cloud.

“Cloud migration can be a complex and high-stakes process for today’s enterprises,” said Barb Huelskamp, Vice President of Channel Sales at SolarWinds. “That’s why customers turn to SolarWinds Service Desk—not just for a smoother migration, but for our intuitive user experience, powerful automation, and seamless integration with AWS environments. Through the AWS ISV Workload Migration Program, we’re helping customers accelerate their cloud journey while maximizing ROI and driving innovation with confidence.”

SolarWinds has long been committed to helping its customers maximize their cloud assets through purpose-built observability SaaS offerings. Most recently, the company expanded its offerings with a new SaaS data center based in Sydney, Australia. The new data center brings observability solutions closer to customers in the Asia-Pacific and Japan regions, providing low latency, better responsiveness, and a choice of local data storage.

SolarWinds products are available in AWS Marketplace. In addition, SolarWinds has achieved the DevOps ISV Competency and Microsoft® Workloads ISV Competency. This means SolarWinds has been recognized by AWS for meeting high standards in specialization, deep AWS expertise, and proven customer success.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

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 ...