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Application Stability Management vs. Application Performance Management - Who Needs Them and Why

Leon Adato

For IT teams, catching errors in applications before they become detrimental to a project is critical. Not only can it ensure that teams are not spending time going back and course correcting errors like transaction bottlenecks or application failures, but it can also save significant amounts of money if the problem becomes too far gone for it to be resolved quickly and efficiently. And wouldn't it be nice if there was someone standing over your shoulder, letting you know exactly when, where, and what the issue is so you can correct it immediately? Luckily, there are both application performance management (APM) and application stability management (ASM) solutions available that can do this for you, flagging errors in both the deployment and development stages of applications, before they can create larger issues down the line.

How Does It Work?

Tech teams often go back and forth on which solution makes the most sense for them to deploy, but the real answer is that because they monitor different aspects of an application, tech teams really stand to benefit from having both. It isn't a question of whether a team wants to invest in APM over ASM or vice versa — it's both, not an either or.

Applications are the oil that keeps the IT machine moving, so it's imperative that they are working their best at all times. And for the most part, organizations use APM to alert users about how their applications are performing in real time, as they are being used. One of the benefits of APM is it can be used to send alerts and flag errors to IT teams to let them know when an application may fail, allowing tech pros to fix applications before they can disrupt business. But ASM has a different functionality altogether and can assist developers when they are building applications to avoid errors in the development phase.

The development cycle can be unpredictable and full of surprises. With changes coming at any possible moment, developers rely on ASM to help them throughout the coding process and ensure there are no gaps in the code they are designing. With ASM, engineers aren't just coding the best they can and hoping for the best, but designing an application that has an almost flawless backbone so that APM solutions do not have to catch all of the problems. ASM can flag and trap new errors as they appear and allows developers to graph out the quality and severity of errors as they're produced. But even when an application is built with ASM, businesses need APM to ensure it's always performing optimally.

Unlike ASM that only catches errors in the development phase, APM can monitor and flag problems after an application has been executed and is in use. But it's worth noting that APM is never going to catch a problem that no one uses, making ASM all the more critical. Because ASM monitors code development, it can find problems that a user may never stumble across.

Making the Most of What You Have

The challenge with deciding when to use APM or ASM is that each option is catered for different teams. On the development side, APM doesn't provide the information they need to know about their code. Whereas for DevOps teams, monitoring engineers and more an APM solution provides the mature and complete overview to allow to know exactly what they are supposed to be getting.

For example, think about how most organizations have engineers on call. Some business leaders find this unnecessary, believing that if there were a problem you could simply kill the instance and reload a better version of the application. But from an engineer's perspective who primarily deals with containerized applications, they kill containers, and if there's a problem in their code, they automatically pull the previous known good version.

But when it comes to most tech pros, there's no real way to kill a router and then revert it from code if the router crashed, something that most engineers don't consider. Even though ASM and APM are definitely for coders and programmers, it's important to see how they are interconnected into the whole IT infrastructure and can impact the performance of what is happening beyond the applications.

But perhaps the most important to remember is that all of these pieces matter holistically and should be interconnected with each other. If you have an APM or ASM tool that stands alone and can't incorporate the different metrics and data, then the tools will only ever be used as a point solution. For the most impactful information, they should operate together.

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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 ...

Application Stability Management vs. Application Performance Management - Who Needs Them and Why

Leon Adato

For IT teams, catching errors in applications before they become detrimental to a project is critical. Not only can it ensure that teams are not spending time going back and course correcting errors like transaction bottlenecks or application failures, but it can also save significant amounts of money if the problem becomes too far gone for it to be resolved quickly and efficiently. And wouldn't it be nice if there was someone standing over your shoulder, letting you know exactly when, where, and what the issue is so you can correct it immediately? Luckily, there are both application performance management (APM) and application stability management (ASM) solutions available that can do this for you, flagging errors in both the deployment and development stages of applications, before they can create larger issues down the line.

How Does It Work?

Tech teams often go back and forth on which solution makes the most sense for them to deploy, but the real answer is that because they monitor different aspects of an application, tech teams really stand to benefit from having both. It isn't a question of whether a team wants to invest in APM over ASM or vice versa — it's both, not an either or.

Applications are the oil that keeps the IT machine moving, so it's imperative that they are working their best at all times. And for the most part, organizations use APM to alert users about how their applications are performing in real time, as they are being used. One of the benefits of APM is it can be used to send alerts and flag errors to IT teams to let them know when an application may fail, allowing tech pros to fix applications before they can disrupt business. But ASM has a different functionality altogether and can assist developers when they are building applications to avoid errors in the development phase.

The development cycle can be unpredictable and full of surprises. With changes coming at any possible moment, developers rely on ASM to help them throughout the coding process and ensure there are no gaps in the code they are designing. With ASM, engineers aren't just coding the best they can and hoping for the best, but designing an application that has an almost flawless backbone so that APM solutions do not have to catch all of the problems. ASM can flag and trap new errors as they appear and allows developers to graph out the quality and severity of errors as they're produced. But even when an application is built with ASM, businesses need APM to ensure it's always performing optimally.

Unlike ASM that only catches errors in the development phase, APM can monitor and flag problems after an application has been executed and is in use. But it's worth noting that APM is never going to catch a problem that no one uses, making ASM all the more critical. Because ASM monitors code development, it can find problems that a user may never stumble across.

Making the Most of What You Have

The challenge with deciding when to use APM or ASM is that each option is catered for different teams. On the development side, APM doesn't provide the information they need to know about their code. Whereas for DevOps teams, monitoring engineers and more an APM solution provides the mature and complete overview to allow to know exactly what they are supposed to be getting.

For example, think about how most organizations have engineers on call. Some business leaders find this unnecessary, believing that if there were a problem you could simply kill the instance and reload a better version of the application. But from an engineer's perspective who primarily deals with containerized applications, they kill containers, and if there's a problem in their code, they automatically pull the previous known good version.

But when it comes to most tech pros, there's no real way to kill a router and then revert it from code if the router crashed, something that most engineers don't consider. Even though ASM and APM are definitely for coders and programmers, it's important to see how they are interconnected into the whole IT infrastructure and can impact the performance of what is happening beyond the applications.

But perhaps the most important to remember is that all of these pieces matter holistically and should be interconnected with each other. If you have an APM or ASM tool that stands alone and can't incorporate the different metrics and data, then the tools will only ever be used as a point solution. For the most impactful information, they should operate together.

Hot Topics

The Latest

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...