Skip to main content

10 APM Capabilities Every IT Manager Should Have

One of the common questions that every IT manager asks on a regular basis is, “Why is my application so slow today when everything was fine yesterday?” Application Performance Management (APM) is the only way to truly answer that question, and it is one of the must-have tools for every IT manager.

With this APM imperative in mind, the following are 10 capabilities every IT manager should look for when choosing an APM solution:

1. Real-time monitoring

Real-time monitoring is a must. When digging into a problem, tracking events in real-time as they occur is by far more effective than doing so via “post-mortem” analysis. There are many APM vendors that claim to provide real-time monitoring but sometimes they really mean “near real-time”, with delays from 30 seconds to five minutes, typically. This restricts your ability to analyze and react to events in real-time. Make sure real-time is truly real-time. Real-time monitoring should provide you with important metrics such as: Who is doing what, how much resources are being taken, and who is affecting who right now?

2. Rich data repository

Sometimes you get lucky and witness a problem in real-time. But in most cases, this doesn’t happen. This is why a good APM solution must be able to collect all transaction activity and performance metrics into a rich, but light-weight repository.

3. “Single anomaly” granularity

Some APM vendors store the statistics they gather but they aggregate it to save disk space or because they just can’t handle too much data in a reasonable amount of time. Analyzing performance incidents based on aggregated data is similar to assessing a book by reading only its rear cover. You get the general idea but you have no ability to understand what really happened. That’s why good APM solutions must give you all of the granular information including individual transactions and their characteristics, resource consumption, traffic order (chain of events) etc.

4. Measuring Quality of Service (QoS) and Service Level Agreements (SLAs)

APM solutions are designed to improve the end user experience. Improving user experience starts by measuring it and identifying QoS and SLA anomalies. Only then can you make informed decisions and take action. You should also have the ability to compare user experience before and after a change is applied to your systems.

5. Performance proactivity – enforcing QoS and SLA

Some APM solutions enable users to analyze performance data and identify root problems retroactively, but do nothing to enable real-time resolution of performance issues. Because these solutions are fundamentally passive by nature, you have no choice but to wait for application performance to nosedive before corrective action can be taken. And in these cases, the wait time from issue identification to resolution can be hours or even days. Avoiding QoS problems can be achieved only if you take proactive steps. Proactive APM solution can turn this: “I got a text message at 2:00AM from our APM tool that indicated that we had a QoS problem so I logged into the system and solved it,” into: “I got a text message at 8:00 AM from our APM tool letting me know that at 1:50 AM a QoS problem was about to occur and it took care of it automatically.” Being proactivite can be achieved in many ways: by activating automatic scripts, managing system resources, and triggering third party tools, etc.

6. Detecting bottlenecks and root cause analysis

If an APM tool only notifies you that you ran out of system resources because of job X, then you don’t really have root cause analysis capabilities. Root cause analysis is when your APM tool tells you that this job usually runs at 8:00 PM but because of problem on a secondary system, it has started 1 hour later and collided with another job that was scheduled to run at the same time. APM tools must do the hard work of correlating many little pieces of data so that you can get to the source of the problem. Otherwise you will find yourself trying to assemble a 1,000 piece puzzle while your CEO knocks on your door every 5 minutes looking for answers.

7. Chain reaction analysis

Analyzing a problem can take many shapes. The conventional way is by digging into the top-10 hit lists. But those top-10 lists always miss something - the chain of events. Who came first, who came after, “it was all fine until this transaction came in”, etc. Analyzing the chain of events before the system crashed is crucial if you wish to avoid this problem in the future. An APM tool should give you the ability to travel back in time and look into the granular metrics second by second as if you were watching a movie in slow motion. This is possible only if the APM tool collects data at a very high level of granularity and does not lose it over time (i.e. it retains the raw collected metrics).

8. Performance comparisons

There are two main performance troubleshooting approaches that an APM tool should support. Performance drill downs to a specific period of time, and performance comparison. If you have a performance problem now, but all was fine yesterday, you must assume that something has changed. Hunting for those changes will lead you to the root cause much quicker than a conventional drill down into the current problem's performance metrics. You should have the ability to answer questions like these in seconds: “Is this new storage system I just implemented faster than the old one we had?” and “why is it working very well in QA but not in production?” If your APM tool collects and stores raw performance metrics, by comparing those metrics you can easily answer all these questions and dramatically shorten your mean time to recovery.

9. Business Intelligence-like dashboard

When an APM tool stores millions of pieces of raw (and aggregated) data, it should also deliver a convenient way to slice and dice this data. Some APM tools will decide for you the best way to process this data by providing a pre-defined set of graph and report templates. A good APM tool will let you decide how you want to slice and dice this data by giving you a flexible and easy to use BI-like dashboard where you can drag and drop dimensions and drill down by double clicking in order to answer questions like, “What user consumed most of my CPU and what is the top program he/she has been using that caused the most impact?”

10.Charge back capability

Bad performance usually starts with bad design or bad coding and very rarely stems from hardware faults. If a developer writes a poor piece of code, the IT division needs to spend more money on hardware or software licenses to deal with it. This is why it’s becoming popular in many organizations to turn this dynamic upside down - here the annual budgets are distributed between the application development divisions that use this money to buy IT services from their IT division. If they write poor code they ultimately need to pay more. This is workable only if the IT department has an APM tool that can measure and enforce resources usage by ‘tenant’. This approach has proven to be effective in helping companies reduce their IT budget quite significantly.

ABOUT Irad Deutsch

Irad Deutsch is a CTO at Veracity group, an international software infrastructure integrator. Irad is also the CTO of MORE IT Resources - MoreVRP, a provider of application and database performance optimization solutions.

Related Links:

www.morevrp.com

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

10 APM Capabilities Every IT Manager Should Have

One of the common questions that every IT manager asks on a regular basis is, “Why is my application so slow today when everything was fine yesterday?” Application Performance Management (APM) is the only way to truly answer that question, and it is one of the must-have tools for every IT manager.

With this APM imperative in mind, the following are 10 capabilities every IT manager should look for when choosing an APM solution:

1. Real-time monitoring

Real-time monitoring is a must. When digging into a problem, tracking events in real-time as they occur is by far more effective than doing so via “post-mortem” analysis. There are many APM vendors that claim to provide real-time monitoring but sometimes they really mean “near real-time”, with delays from 30 seconds to five minutes, typically. This restricts your ability to analyze and react to events in real-time. Make sure real-time is truly real-time. Real-time monitoring should provide you with important metrics such as: Who is doing what, how much resources are being taken, and who is affecting who right now?

2. Rich data repository

Sometimes you get lucky and witness a problem in real-time. But in most cases, this doesn’t happen. This is why a good APM solution must be able to collect all transaction activity and performance metrics into a rich, but light-weight repository.

3. “Single anomaly” granularity

Some APM vendors store the statistics they gather but they aggregate it to save disk space or because they just can’t handle too much data in a reasonable amount of time. Analyzing performance incidents based on aggregated data is similar to assessing a book by reading only its rear cover. You get the general idea but you have no ability to understand what really happened. That’s why good APM solutions must give you all of the granular information including individual transactions and their characteristics, resource consumption, traffic order (chain of events) etc.

4. Measuring Quality of Service (QoS) and Service Level Agreements (SLAs)

APM solutions are designed to improve the end user experience. Improving user experience starts by measuring it and identifying QoS and SLA anomalies. Only then can you make informed decisions and take action. You should also have the ability to compare user experience before and after a change is applied to your systems.

5. Performance proactivity – enforcing QoS and SLA

Some APM solutions enable users to analyze performance data and identify root problems retroactively, but do nothing to enable real-time resolution of performance issues. Because these solutions are fundamentally passive by nature, you have no choice but to wait for application performance to nosedive before corrective action can be taken. And in these cases, the wait time from issue identification to resolution can be hours or even days. Avoiding QoS problems can be achieved only if you take proactive steps. Proactive APM solution can turn this: “I got a text message at 2:00AM from our APM tool that indicated that we had a QoS problem so I logged into the system and solved it,” into: “I got a text message at 8:00 AM from our APM tool letting me know that at 1:50 AM a QoS problem was about to occur and it took care of it automatically.” Being proactivite can be achieved in many ways: by activating automatic scripts, managing system resources, and triggering third party tools, etc.

6. Detecting bottlenecks and root cause analysis

If an APM tool only notifies you that you ran out of system resources because of job X, then you don’t really have root cause analysis capabilities. Root cause analysis is when your APM tool tells you that this job usually runs at 8:00 PM but because of problem on a secondary system, it has started 1 hour later and collided with another job that was scheduled to run at the same time. APM tools must do the hard work of correlating many little pieces of data so that you can get to the source of the problem. Otherwise you will find yourself trying to assemble a 1,000 piece puzzle while your CEO knocks on your door every 5 minutes looking for answers.

7. Chain reaction analysis

Analyzing a problem can take many shapes. The conventional way is by digging into the top-10 hit lists. But those top-10 lists always miss something - the chain of events. Who came first, who came after, “it was all fine until this transaction came in”, etc. Analyzing the chain of events before the system crashed is crucial if you wish to avoid this problem in the future. An APM tool should give you the ability to travel back in time and look into the granular metrics second by second as if you were watching a movie in slow motion. This is possible only if the APM tool collects data at a very high level of granularity and does not lose it over time (i.e. it retains the raw collected metrics).

8. Performance comparisons

There are two main performance troubleshooting approaches that an APM tool should support. Performance drill downs to a specific period of time, and performance comparison. If you have a performance problem now, but all was fine yesterday, you must assume that something has changed. Hunting for those changes will lead you to the root cause much quicker than a conventional drill down into the current problem's performance metrics. You should have the ability to answer questions like these in seconds: “Is this new storage system I just implemented faster than the old one we had?” and “why is it working very well in QA but not in production?” If your APM tool collects and stores raw performance metrics, by comparing those metrics you can easily answer all these questions and dramatically shorten your mean time to recovery.

9. Business Intelligence-like dashboard

When an APM tool stores millions of pieces of raw (and aggregated) data, it should also deliver a convenient way to slice and dice this data. Some APM tools will decide for you the best way to process this data by providing a pre-defined set of graph and report templates. A good APM tool will let you decide how you want to slice and dice this data by giving you a flexible and easy to use BI-like dashboard where you can drag and drop dimensions and drill down by double clicking in order to answer questions like, “What user consumed most of my CPU and what is the top program he/she has been using that caused the most impact?”

10.Charge back capability

Bad performance usually starts with bad design or bad coding and very rarely stems from hardware faults. If a developer writes a poor piece of code, the IT division needs to spend more money on hardware or software licenses to deal with it. This is why it’s becoming popular in many organizations to turn this dynamic upside down - here the annual budgets are distributed between the application development divisions that use this money to buy IT services from their IT division. If they write poor code they ultimately need to pay more. This is workable only if the IT department has an APM tool that can measure and enforce resources usage by ‘tenant’. This approach has proven to be effective in helping companies reduce their IT budget quite significantly.

ABOUT Irad Deutsch

Irad Deutsch is a CTO at Veracity group, an international software infrastructure integrator. Irad is also the CTO of MORE IT Resources - MoreVRP, a provider of application and database performance optimization solutions.

Related Links:

www.morevrp.com

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...