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Why is APM Important?

Anand Akela

The following is an excerpt from: An Introduction to Application Performance Management (APM).

It probably seems obvious to you that APM is important, but you will likely need to answer the question of APM importance to someone like your boss or the company CFO that wants to know why she must pay for it. In order to qualify the importance of APM, let's consider the alternatives to adopting an APM solution and assess the impact in terms of resolution effort and elapsed downtime.

First let's consider how we detect problems. An APM solution alerts you to the abnormal application behavior, but if you don't have an APM solution then you have a few options:

■ Build synthetic transactions

■ Manual instrumentation

■ Wait for your users to call customer support!?

A synthetic transaction is a transaction that you execute against your application and with which you measure performance. Depending on the complexity of your application, it is not difficult to build a small program that calls a service and validates the response. But what do you do with that program? If it runs on your machine then what happens when you're out of the office?

Furthermore, if you do detect a functional or performance issue, what do you do with that information? Do you connect to an email server and send alerts? How do you know if this is a real problem or a normal slowdown for your application at this hour and day of the week?

Finally, detecting the problem is one thing, how do you find the root cause of the problem?

The next option is manually instrumenting your application, which means that you add performance monitoring code directly to your application and record it somewhere like a database or a file system. Some challenges in manual instrumentation include:

What parts of my code do I instrument?

How do I analyze it?

How do I determine normalcy?

How do I propagate those problems up to someone to analyze?

What contextual information is important?

... and so forth. Plus you have introduced a new problem: you have introduced performance monitoring code into your application that you need to maintain.

Furthermore, can you dynamically turn it on and off so that your performance monitoring code does not negatively affect the performance of your application?

If you learn more about your application and identify additional metrics you want to capture, do you need to rebuild your application and redeploy it to production?

What if your performance monitoring code has bugs?

There are other technical options, but what I find most often is that companies are alerted to performance problems when their custom service organization receives complaints from users. I don't think I need to go into details about why this is a bad idea!

Next let's consider how we identify the root cause of a performance problem without an APM solution. Most often I have seen companies do one of two things:

■ Review runtime logs

■ Attempt to reproduce the problem in a development/test environment

Log files are great sources of information and many times they can identify functional defects in your application (by capturing exception stack traces), but when experiencing performance issues that do not raise exceptions, they typically only introduce additional confusion.

You may have heard of, or been directly involved in, a production war room. These war rooms are characterized by finger pointing and attempts to indemnify one's own components so that the pressure to resolve the issue falls on someone else. The bottom line is that these meetings are not fun and not productive.

Alternatively, and usually in parallel, the development team is tasked with reproducing the problem in a test environment. The challenge here is that you usually do not have enough context for these attempts to be fruitful. Furthermore, if you are able to reproduce the problem in a test environment, that is only the first step, now you need to identify the root cause of the problem and resolve it!

So to summarize, APM is important to you so that you can understand the behavior of your application, detect problems before your users are impacted, and rapidly resolve those issues. In business terms, an APM solution is important because it reduces your Mean Time To Resolution (MTTR), which means that performance issues are resolved quicker and more efficiently so that the impact to your business bottom line is reduced.

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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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Why is APM Important?

Anand Akela

The following is an excerpt from: An Introduction to Application Performance Management (APM).

It probably seems obvious to you that APM is important, but you will likely need to answer the question of APM importance to someone like your boss or the company CFO that wants to know why she must pay for it. In order to qualify the importance of APM, let's consider the alternatives to adopting an APM solution and assess the impact in terms of resolution effort and elapsed downtime.

First let's consider how we detect problems. An APM solution alerts you to the abnormal application behavior, but if you don't have an APM solution then you have a few options:

■ Build synthetic transactions

■ Manual instrumentation

■ Wait for your users to call customer support!?

A synthetic transaction is a transaction that you execute against your application and with which you measure performance. Depending on the complexity of your application, it is not difficult to build a small program that calls a service and validates the response. But what do you do with that program? If it runs on your machine then what happens when you're out of the office?

Furthermore, if you do detect a functional or performance issue, what do you do with that information? Do you connect to an email server and send alerts? How do you know if this is a real problem or a normal slowdown for your application at this hour and day of the week?

Finally, detecting the problem is one thing, how do you find the root cause of the problem?

The next option is manually instrumenting your application, which means that you add performance monitoring code directly to your application and record it somewhere like a database or a file system. Some challenges in manual instrumentation include:

What parts of my code do I instrument?

How do I analyze it?

How do I determine normalcy?

How do I propagate those problems up to someone to analyze?

What contextual information is important?

... and so forth. Plus you have introduced a new problem: you have introduced performance monitoring code into your application that you need to maintain.

Furthermore, can you dynamically turn it on and off so that your performance monitoring code does not negatively affect the performance of your application?

If you learn more about your application and identify additional metrics you want to capture, do you need to rebuild your application and redeploy it to production?

What if your performance monitoring code has bugs?

There are other technical options, but what I find most often is that companies are alerted to performance problems when their custom service organization receives complaints from users. I don't think I need to go into details about why this is a bad idea!

Next let's consider how we identify the root cause of a performance problem without an APM solution. Most often I have seen companies do one of two things:

■ Review runtime logs

■ Attempt to reproduce the problem in a development/test environment

Log files are great sources of information and many times they can identify functional defects in your application (by capturing exception stack traces), but when experiencing performance issues that do not raise exceptions, they typically only introduce additional confusion.

You may have heard of, or been directly involved in, a production war room. These war rooms are characterized by finger pointing and attempts to indemnify one's own components so that the pressure to resolve the issue falls on someone else. The bottom line is that these meetings are not fun and not productive.

Alternatively, and usually in parallel, the development team is tasked with reproducing the problem in a test environment. The challenge here is that you usually do not have enough context for these attempts to be fruitful. Furthermore, if you are able to reproduce the problem in a test environment, that is only the first step, now you need to identify the root cause of the problem and resolve it!

So to summarize, APM is important to you so that you can understand the behavior of your application, detect problems before your users are impacted, and rapidly resolve those issues. In business terms, an APM solution is important because it reduces your Mean Time To Resolution (MTTR), which means that performance issues are resolved quicker and more efficiently so that the impact to your business bottom line is reduced.

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.