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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 live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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.