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APM for Development - Unified Monitoring for IT Ops

Scott Hollis

Ensuring application performance is a never ending task that involves multiple products, features and best practices. There is no one process, feature, or product that does everything. A good place to start is pre-production and production monitoring with both an Application Performance Management (APM) tool and a Unified Monitoring tool.

The APM tool will trace/instrument your application and application server activity and often the end user experience via synthetic transactions. The development team and DevOps folks need this.

The Unified Monitoring tool will monitor the supporting infrastructure. The IT Ops team needs this. DevOps likes it too because it helps make IT Ops more effective, which in turn helps assure application delivery.

More Cost Effective

APM tools do not specialize in infrastructure monitoring like unified monitoring solutions do, and unified monitoring solutions do not provide application monitoring depth and diagnostics like the APM tools do. And on top of that, the different audiences need different information.

The best approach is to buy APM for the most critical applications. Most organizations use APM for 10% - 15% of their applications. It is too expensive to buy it for everything. Then for the second tier applications that need some monitoring, they use the unified monitoring solution. It is much less expensive and if you select one with synthetic transaction capability you can get "good enough" end user experience monitoring to know whether or not the application is performing well or not.

Service-Centric is Key

When it comes to unified monitoring, it is important to understand that most unified monitoring vendors provide endpoint monitoring. With endpoint monitoring alone, it is impossible to provide highly accurate root-cause isolation. And they don't identify which service, or application, is impacted. And they can't tell you the extent of the impact. Is it just at risk without impacting application delivery yet OR is it down OR is it somewhere in between?

Be sure the unified monitoring vendor is service-centric and models relationships between components, and that it identifies root-cause; the service or application impacted; and the extent of the impact. This can save hours when there is an outage.

Better yet, by identifying when services are at risk, this can help you to proactively identify and address issues before services/application delivery is impacted.

Scott Hollis is Director of Product Marketing for Zenoss.

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...

APM for Development - Unified Monitoring for IT Ops

Scott Hollis

Ensuring application performance is a never ending task that involves multiple products, features and best practices. There is no one process, feature, or product that does everything. A good place to start is pre-production and production monitoring with both an Application Performance Management (APM) tool and a Unified Monitoring tool.

The APM tool will trace/instrument your application and application server activity and often the end user experience via synthetic transactions. The development team and DevOps folks need this.

The Unified Monitoring tool will monitor the supporting infrastructure. The IT Ops team needs this. DevOps likes it too because it helps make IT Ops more effective, which in turn helps assure application delivery.

More Cost Effective

APM tools do not specialize in infrastructure monitoring like unified monitoring solutions do, and unified monitoring solutions do not provide application monitoring depth and diagnostics like the APM tools do. And on top of that, the different audiences need different information.

The best approach is to buy APM for the most critical applications. Most organizations use APM for 10% - 15% of their applications. It is too expensive to buy it for everything. Then for the second tier applications that need some monitoring, they use the unified monitoring solution. It is much less expensive and if you select one with synthetic transaction capability you can get "good enough" end user experience monitoring to know whether or not the application is performing well or not.

Service-Centric is Key

When it comes to unified monitoring, it is important to understand that most unified monitoring vendors provide endpoint monitoring. With endpoint monitoring alone, it is impossible to provide highly accurate root-cause isolation. And they don't identify which service, or application, is impacted. And they can't tell you the extent of the impact. Is it just at risk without impacting application delivery yet OR is it down OR is it somewhere in between?

Be sure the unified monitoring vendor is service-centric and models relationships between components, and that it identifies root-cause; the service or application impacted; and the extent of the impact. This can save hours when there is an outage.

Better yet, by identifying when services are at risk, this can help you to proactively identify and address issues before services/application delivery is impacted.

Scott Hollis is Director of Product Marketing for Zenoss.

Hot Topics

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...