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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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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

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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