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More on SolarWinds Prediction for APM in 2017

You Keep Using That Word
Leon Adato

As an IT professional, I'm used to words that mean different things to different people. For example, "log monitoring" could mean anything from simple text files to logfile aggregation systems. "Uptime" is also notoriously hard to nail down. Heck, even the word "monitoring" itself can be obscure.

To illustrate this phenomenon, I often bring up the (completely unrelated) classical Chinese poem Lion-Eating Poet in the Stone Den. Spoken out loud, every word is a version of the sound "shi." But as you can see, aside from the pronunciation, each word has extremely different meanings.

This is why I'm not surprised that application performance monitoring (APM) can mean so many different things depending on the context. But what is most confounding is that these usages are not mutually exclusive. There is overlap. This graphic demonstrates:


As you can see, there's code-centric APM (cAPM) where the focus is on code execution, transactions moving through the message queue, transforms, etc. This type of APM is often applied to custom developed code, or applications that are highly transactional in nature.

At the other end of the spectrum, there's operations-centric APM (oAPM). This type of APM is more concerned with what's often called "shrink-wrapped" software, which can be everything from single-purpose business utilities to enterprise class tools, such as Microsoft Exchange and even foundational things like the operating system itself. The point isn't that they are any less sophisticated than the programs that use code-centric APM, but the needs are different. More on this point in a moment.

There's also web-centric APM, or web performance monitoring (WPM), which, as the name implies, is focused on monitoring web applications. So it's less about the code execution or the stability of the underlying server application, and more about how the user of the web application is experiencing the service.

Finally, there's database-centric APM (dbAPM). In this iteration, it's all about the things that make your database go bump in the night: long running queries, locking, blocking, and wait states.

If you look at it closely, you can see the overlap. cAPM still cares that the application itself is healthy, and it can provide insight into things like services and processes, performance counters, and log messages. But that's not the primary focus. Similarly, oAPM has the ability to expose issues with transactions, but not to the level that cAPM does. Where it shines, however, is in operational metrics. And the same is true for WPM and dbAPM. 

This has all always been true, but it wasn't as clear until recently. The emergence (and convergence) of cloud, DevOps, hybrid IT, and everything-as-a-service (EaaS) has highlighted both the overlap and the differences. 

This is why I recently predicted that, "2017 will be the year of 'not just' in APM. As in 'not just agent-based transaction tracking' or 'not just for DevOps.' But most importantly, 'not just for home-grown code.' In the coming year, APM will fully embrace the words behind the acronym to include tools and techniques that allow management of all application types — from those developed in-house to customized-off-the-shelf ones, to pure shrink-wrap apps that enterprises purchase, install, and run as-is. Yes! Some of those really do still exist."

I'm looking forward to the time — in this coming year, if my prediction holds true — when IT professionals can say, "APM" and understand the nuances the same way students of Chinese literature understand that "shí shì shī shì shī shì" means, "A poet named Shi lived in a stone room."

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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 ...

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 ...

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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.

More on SolarWinds Prediction for APM in 2017

You Keep Using That Word
Leon Adato

As an IT professional, I'm used to words that mean different things to different people. For example, "log monitoring" could mean anything from simple text files to logfile aggregation systems. "Uptime" is also notoriously hard to nail down. Heck, even the word "monitoring" itself can be obscure.

To illustrate this phenomenon, I often bring up the (completely unrelated) classical Chinese poem Lion-Eating Poet in the Stone Den. Spoken out loud, every word is a version of the sound "shi." But as you can see, aside from the pronunciation, each word has extremely different meanings.

This is why I'm not surprised that application performance monitoring (APM) can mean so many different things depending on the context. But what is most confounding is that these usages are not mutually exclusive. There is overlap. This graphic demonstrates:


As you can see, there's code-centric APM (cAPM) where the focus is on code execution, transactions moving through the message queue, transforms, etc. This type of APM is often applied to custom developed code, or applications that are highly transactional in nature.

At the other end of the spectrum, there's operations-centric APM (oAPM). This type of APM is more concerned with what's often called "shrink-wrapped" software, which can be everything from single-purpose business utilities to enterprise class tools, such as Microsoft Exchange and even foundational things like the operating system itself. The point isn't that they are any less sophisticated than the programs that use code-centric APM, but the needs are different. More on this point in a moment.

There's also web-centric APM, or web performance monitoring (WPM), which, as the name implies, is focused on monitoring web applications. So it's less about the code execution or the stability of the underlying server application, and more about how the user of the web application is experiencing the service.

Finally, there's database-centric APM (dbAPM). In this iteration, it's all about the things that make your database go bump in the night: long running queries, locking, blocking, and wait states.

If you look at it closely, you can see the overlap. cAPM still cares that the application itself is healthy, and it can provide insight into things like services and processes, performance counters, and log messages. But that's not the primary focus. Similarly, oAPM has the ability to expose issues with transactions, but not to the level that cAPM does. Where it shines, however, is in operational metrics. And the same is true for WPM and dbAPM. 

This has all always been true, but it wasn't as clear until recently. The emergence (and convergence) of cloud, DevOps, hybrid IT, and everything-as-a-service (EaaS) has highlighted both the overlap and the differences. 

This is why I recently predicted that, "2017 will be the year of 'not just' in APM. As in 'not just agent-based transaction tracking' or 'not just for DevOps.' But most importantly, 'not just for home-grown code.' In the coming year, APM will fully embrace the words behind the acronym to include tools and techniques that allow management of all application types — from those developed in-house to customized-off-the-shelf ones, to pure shrink-wrap apps that enterprises purchase, install, and run as-is. Yes! Some of those really do still exist."

I'm looking forward to the time — in this coming year, if my prediction holds true — when IT professionals can say, "APM" and understand the nuances the same way students of Chinese literature understand that "shí shì shī shì shī shì" means, "A poet named Shi lived in a stone room."

Hot Topics

The Latest

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 ...

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.