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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.

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

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

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...