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Making Sense of APM and Ending the Agent/Agentless War

Antonio Piraino

Application Performance Management (APM) is a hot topic right now. Gartner defines APM as agent-based monitoring that sits inside the operating system and provides code-level performance, tracing, application mapping, and tracking. How exactly does APM help an organization, and when would a business choose to invest in this technology? When does APM make sense and when doesn’t it? And, more broadly, how does this tie into the changing needs of IT monitoring? Finally, why does the agent vs. agentless debate continue to rage on?

Simply put, enterprises that write their own code (Java, .NET, etc.) and leverage applications unique to the way they do business must have code-level application visibility. More specifically, those companies who place high importance on understanding how code executes and functions in a production environment, and what that means to business-critical, revenue generating, bespoke applications need APM.

That said, APM is not necessary for the vast majority of commercial applications not authored by the enterprise because code-level visibility is not necessary, for instance in the example of a CAD app purchased from a provider of an ERP solution. There is also the cost consideration. As a single APM agent typically runs somewhere between $150-$200 per month, from a cost perspective it simply doesn’t make sense. If your authentication service goes down, you’re not going to use an APM agent on that. In fact, most of your operators wouldn’t even know what to do with the deep code level data you’re getting back.

Today we’re seeing traditional IT infrastructure management vendors moving towards an application-centric view of the world and APM vendors attempting to get broader visibility of the entire IT infrastructure. As an enterprise, I need to understand how all of my infrastructure is working — what’s up, what’s down, what’s running well and what’s not, capacity planning, failure analysis, and keeping the lights on across my vast complicated set of IT technologies. Simultaneously, organizations need to know how their applications are doing. However, rather than handpicking one or two “important” ones for code level visibility, you’d really like the two different types of vendors to meet in the middle.

So most organizations are combining application-aware infrastructure monitoring for all apps and augmenting in spot places with APM for custom apps.

On to the war — agent-based versus agentless monitoring. For years now we’ve heard sniping back and forth as to which model is best suited for enterprise IT. Both approaches have their pros and cons. Agents can provide more granular performance metrics, while agentless monitoring platforms are often easier to manage. But to say you can only have one or the other is a canard. There are vendors that provide customers with the option to deploy both models simultaneously, depending on the customer’s need.

If there is one inalienable truth concerning IT, it’s that IT has and always will be heterogeneous in nature. The complexity of systems and IT infrastructure ecosystems demand it and IT will never converge on homogeneity. Enterprise IT should not look to choose between APM and application-aware infrastructure monitoring. Nor should they be forced to adopt a single approach to gathering performance metrics. That of course isn’t stopping vendors from yelling from the rooftops.

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Making Sense of APM and Ending the Agent/Agentless War

Antonio Piraino

Application Performance Management (APM) is a hot topic right now. Gartner defines APM as agent-based monitoring that sits inside the operating system and provides code-level performance, tracing, application mapping, and tracking. How exactly does APM help an organization, and when would a business choose to invest in this technology? When does APM make sense and when doesn’t it? And, more broadly, how does this tie into the changing needs of IT monitoring? Finally, why does the agent vs. agentless debate continue to rage on?

Simply put, enterprises that write their own code (Java, .NET, etc.) and leverage applications unique to the way they do business must have code-level application visibility. More specifically, those companies who place high importance on understanding how code executes and functions in a production environment, and what that means to business-critical, revenue generating, bespoke applications need APM.

That said, APM is not necessary for the vast majority of commercial applications not authored by the enterprise because code-level visibility is not necessary, for instance in the example of a CAD app purchased from a provider of an ERP solution. There is also the cost consideration. As a single APM agent typically runs somewhere between $150-$200 per month, from a cost perspective it simply doesn’t make sense. If your authentication service goes down, you’re not going to use an APM agent on that. In fact, most of your operators wouldn’t even know what to do with the deep code level data you’re getting back.

Today we’re seeing traditional IT infrastructure management vendors moving towards an application-centric view of the world and APM vendors attempting to get broader visibility of the entire IT infrastructure. As an enterprise, I need to understand how all of my infrastructure is working — what’s up, what’s down, what’s running well and what’s not, capacity planning, failure analysis, and keeping the lights on across my vast complicated set of IT technologies. Simultaneously, organizations need to know how their applications are doing. However, rather than handpicking one or two “important” ones for code level visibility, you’d really like the two different types of vendors to meet in the middle.

So most organizations are combining application-aware infrastructure monitoring for all apps and augmenting in spot places with APM for custom apps.

On to the war — agent-based versus agentless monitoring. For years now we’ve heard sniping back and forth as to which model is best suited for enterprise IT. Both approaches have their pros and cons. Agents can provide more granular performance metrics, while agentless monitoring platforms are often easier to manage. But to say you can only have one or the other is a canard. There are vendors that provide customers with the option to deploy both models simultaneously, depending on the customer’s need.

If there is one inalienable truth concerning IT, it’s that IT has and always will be heterogeneous in nature. The complexity of systems and IT infrastructure ecosystems demand it and IT will never converge on homogeneity. Enterprise IT should not look to choose between APM and application-aware infrastructure monitoring. Nor should they be forced to adopt a single approach to gathering performance metrics. That of course isn’t stopping vendors from yelling from the rooftops.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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