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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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In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...