Skip to main content

An Interview with Bill Karpovich from Zenoss

Pete Goldin
APMdigest

In BSMdigest’s exclusive interview, Zenoss CEO Bill Karpovich discusses Business Service Management in the cloud.

BSM: Are companies moving their mission-critical business applications to the cloud or are they testing it first, with less critical applications?

BK: The general answer is testing first, but it also depends on what kind of a company you are. If you’re a new business that is entirely web-centric, like say a fashion e-tailer driving buyers to flash sales events, then your best bet is to have an elastic cloud-based model built from the ground up to take advantage of the cloud.

Now, on the other hand, if you are a more traditional enterprise, your business critical applications will most likely only migrate to the cloud after the lesser critical applications have been successfully migrated. We’ve seen this phenomenon in virtualization whereby business critical apps only get virtualized after the low hanging fruit of non-critical apps have been virtualized.

BSM: Are companies that deploy private clouds having trouble getting the business side of their organizations to buy into or have confidence in the cloud?

BK: Confidence in the cloud from a business owner’s perspective means confidence in the ability to assure service levels (performance and availability) to their business critical applications. The cloud involves a supporting infrastructure shift from a one-application-one-server service delivery paradigm where service could actually be assured through over provisioning of resources to a shared, dynamic resource delivery. This makes insight, visibility and service delivery assurance much more complex. So while it is easy to perhaps understand the cost efficiencies associated with cloud computing, service risk is what drives a lack of confidence.

BSM: What are the greatest BSM challenges companies face when they move to a private cloud?

BK: Moving to the cloud, either private, public or hybrid, means that you have essentially adopted an IT-as-a-Service paradigm. Done well, it means that your IT operation is self-service driven off of a service catalog with appropriate chargeback in place. The challenges here are many including the evolution of the organization, tools and processes.

For example, from an organization perspective, there is the breaking down of traditional organo-technical silos with a clear line-of-sight towards service delivery to the customer. New skills will be required.

In the area of tools, a brand new set of tools that are service aware, real-time, unified, multi-tenant and automated are required to support visibility and control of the next generation dynamic cloud infrastructure.

BSM: Do you feel most monitoring and management solutions are unable to handle the cloud?

BK: Yes. Virtualization and cloud change everything that we know relative to service assurance monitoring. There are many layers that are interconnected in this new infrastructure model that need to come together to assure service. These include physical unified computing servers like UCS that contain policy, virtualization and storage layers and then cloud layers. Add to this the fact that the configuration of resources is changing dynamically then this of course points to a completely different set of needs whereby the manual CMDB update is a thing of the past. Legacy tools are siloed, fragmented, static and single tenant. They have no notion of a real-time service model.

BSM: What is a must-have for a monitoring solution to work in a private cloud?

BK: If I had to pick one thing it would be dynamic model-driven monitoring. There are too many moving parts to keep up with either in an operator’s head, or in Excel spreadsheets. What is required is a model that is able to automatically and continuously keep up-to-date with changes to the dynamic configuration and to provide real-time insight and control into the service dependency model. This, in my mind, is the must-have for monitoring highly dynamic cloud environments, whether private or public.

And a real-time service model isn’t just something you bolt onto the side of your legacy-monitoring tool. It needs to be architected and purpose built from the ground up with this application in mind.

BSM: Everyone seems to agree that most IT environments will be hybrid virtual and physical environments for the near future. Why is it important to have one monitoring solution that covers both environments?

BK: There are a few reasons. I like to call these the three C’s; context, consistency and completeness.

Let’s start with context. When troubleshooting or looking at a particular incident, if everyone is working from the same dataset that covers both physical and virtual within the same model and with the same timestamps, then you can avoid the silo wars.

Consistency and thus operational efficiency comes about when the operations team uses the same uniform toolset.

Completeness is important when troubleshooting a performance problem where for example you might use one tool to determine the performance of the virtualization server and then a different tool to determine the performance of the guest OS and then try to correlate these at a historical point-in-time. Swivel-chair monitoring between different toolsets should be a thing of the past for monitoring hybrid environments.

BSM: What are the differentiating capabilities of the new Zenoss 3.0 release?

BK: In July we released Zenoss Enterprise 3.0 and our Dynamic Service Assurance vision. This release was the culmination of a lot of UI work that now enables users to visualize, search and interact more productively with the Zenoss real-time service model.

We believe that we are the first to deliver the ability to provide single-pane-of-glass insight and service assurance across all of the various interconnected physical, virtual, network, storage, UCS and vCloud computing layers.

About Bill Karpovich

Bill Karpovich, CEO and Co-Founder of Zenoss, conceived the company's disruptive business strategy and has successfully guided the company from start-up to a category leader. As an IT management and cloud computing visionary, Bill has been featured on the cover of InformationWeek Magazine and is frequently consulted by the media and industry analysts for his insights on IT management and the broader open source software market.

Related Links:

www.zenoss.com

Hot Topic
The Latest
The Latest 10

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

An Interview with Bill Karpovich from Zenoss

Pete Goldin
APMdigest

In BSMdigest’s exclusive interview, Zenoss CEO Bill Karpovich discusses Business Service Management in the cloud.

BSM: Are companies moving their mission-critical business applications to the cloud or are they testing it first, with less critical applications?

BK: The general answer is testing first, but it also depends on what kind of a company you are. If you’re a new business that is entirely web-centric, like say a fashion e-tailer driving buyers to flash sales events, then your best bet is to have an elastic cloud-based model built from the ground up to take advantage of the cloud.

Now, on the other hand, if you are a more traditional enterprise, your business critical applications will most likely only migrate to the cloud after the lesser critical applications have been successfully migrated. We’ve seen this phenomenon in virtualization whereby business critical apps only get virtualized after the low hanging fruit of non-critical apps have been virtualized.

BSM: Are companies that deploy private clouds having trouble getting the business side of their organizations to buy into or have confidence in the cloud?

BK: Confidence in the cloud from a business owner’s perspective means confidence in the ability to assure service levels (performance and availability) to their business critical applications. The cloud involves a supporting infrastructure shift from a one-application-one-server service delivery paradigm where service could actually be assured through over provisioning of resources to a shared, dynamic resource delivery. This makes insight, visibility and service delivery assurance much more complex. So while it is easy to perhaps understand the cost efficiencies associated with cloud computing, service risk is what drives a lack of confidence.

BSM: What are the greatest BSM challenges companies face when they move to a private cloud?

BK: Moving to the cloud, either private, public or hybrid, means that you have essentially adopted an IT-as-a-Service paradigm. Done well, it means that your IT operation is self-service driven off of a service catalog with appropriate chargeback in place. The challenges here are many including the evolution of the organization, tools and processes.

For example, from an organization perspective, there is the breaking down of traditional organo-technical silos with a clear line-of-sight towards service delivery to the customer. New skills will be required.

In the area of tools, a brand new set of tools that are service aware, real-time, unified, multi-tenant and automated are required to support visibility and control of the next generation dynamic cloud infrastructure.

BSM: Do you feel most monitoring and management solutions are unable to handle the cloud?

BK: Yes. Virtualization and cloud change everything that we know relative to service assurance monitoring. There are many layers that are interconnected in this new infrastructure model that need to come together to assure service. These include physical unified computing servers like UCS that contain policy, virtualization and storage layers and then cloud layers. Add to this the fact that the configuration of resources is changing dynamically then this of course points to a completely different set of needs whereby the manual CMDB update is a thing of the past. Legacy tools are siloed, fragmented, static and single tenant. They have no notion of a real-time service model.

BSM: What is a must-have for a monitoring solution to work in a private cloud?

BK: If I had to pick one thing it would be dynamic model-driven monitoring. There are too many moving parts to keep up with either in an operator’s head, or in Excel spreadsheets. What is required is a model that is able to automatically and continuously keep up-to-date with changes to the dynamic configuration and to provide real-time insight and control into the service dependency model. This, in my mind, is the must-have for monitoring highly dynamic cloud environments, whether private or public.

And a real-time service model isn’t just something you bolt onto the side of your legacy-monitoring tool. It needs to be architected and purpose built from the ground up with this application in mind.

BSM: Everyone seems to agree that most IT environments will be hybrid virtual and physical environments for the near future. Why is it important to have one monitoring solution that covers both environments?

BK: There are a few reasons. I like to call these the three C’s; context, consistency and completeness.

Let’s start with context. When troubleshooting or looking at a particular incident, if everyone is working from the same dataset that covers both physical and virtual within the same model and with the same timestamps, then you can avoid the silo wars.

Consistency and thus operational efficiency comes about when the operations team uses the same uniform toolset.

Completeness is important when troubleshooting a performance problem where for example you might use one tool to determine the performance of the virtualization server and then a different tool to determine the performance of the guest OS and then try to correlate these at a historical point-in-time. Swivel-chair monitoring between different toolsets should be a thing of the past for monitoring hybrid environments.

BSM: What are the differentiating capabilities of the new Zenoss 3.0 release?

BK: In July we released Zenoss Enterprise 3.0 and our Dynamic Service Assurance vision. This release was the culmination of a lot of UI work that now enables users to visualize, search and interact more productively with the Zenoss real-time service model.

We believe that we are the first to deliver the ability to provide single-pane-of-glass insight and service assurance across all of the various interconnected physical, virtual, network, storage, UCS and vCloud computing layers.

About Bill Karpovich

Bill Karpovich, CEO and Co-Founder of Zenoss, conceived the company's disruptive business strategy and has successfully guided the company from start-up to a category leader. As an IT management and cloud computing visionary, Bill has been featured on the cover of InformationWeek Magazine and is frequently consulted by the media and industry analysts for his insights on IT management and the broader open source software market.

Related Links:

www.zenoss.com

Hot Topic
The Latest
The Latest 10

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...