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Q&A Part Three: Gartner Talks About Application Performance Management

Pete Goldin
APMdigest

In Part Three of APMdigest's exclusive interview,
Jonah Kowall, Research Director in Gartner's IT Operations Research group, discusses additional new requirements for the Magic Quadrant for APM, as well as the challenges of application performance management.

Start with Q&A Part One: Gartner Talks About APM Cool Vendors

Start with Q&A Part Two: Gartner Talks About SaaS APM

APM: You mentioned that Gartner is requiring companies in the Magic Quadrant on APM will now be required to offer a SaaS option. In addition, you are also requiring vendors to offer all of Gartner's Five Dimensions of APM, to be included in the Magic Quadrant for APM, correct?

JK: Yes. We previously required 3 out of 5 dimensions, but this year to be included in the Magic Quadrant a vendor must have a full-featured APM product.

APM: Do you expect this requirement to cut out many of the smaller players in the market?

JK: The new requirement will not cut out that many of the smaller players because most products are moving toward inclusion of all five dimensions. Vendors that did not offer deep dive capability, for example, are adding that in order to appeal to those other buyers. And vendors that did not have good end-user experience monitoring are adding JavaScript injection capabilities into their products to be able to fulfill that type of use case.

So we are starting to see many of these small vendors with one product and one agent be able to do all five dimensions. Since the last Magic Quadrant, about a year ago, we have seen many vendors that had four dimensions move to five dimensions, and those are all unified products.

APM: In your experience what are the main reasons why APM deployments fail to deliver?

JK: The number one issue that I see is a vendor will convince the customer that they should buy APM for all of their infrastructure and then they should work on deploying that and have a services engagement to do so. The issue is that when you buy hundreds of thousands of licenses of APM products, implementing those licenses across potentially hundreds of thousands of applications is not feasible in any reasonable amount of time.

Taking a much more balanced approach to identifying and implementing the critical components that should have APM on them tends to work better. Although the vendors do not appreciate it when you do smaller transactions with them, that tends to be the most effective use, and the reason why APM does not often deliver the value it is supposed to deliver.

The other challenge is that a company may not have the expertise in a tool that is potentially complex to implement and use. That is being combated by bringing APM downmarket and making it easier to use. Then the product can be leveraged not just by the third line developers but also the first and second line developers, in terms of being able to understand an application issue, and some type of diagnosis as to what the issue is, and who the issue should be escalated to. I think a lot of that is being driven by the push for simplicity versus the push for comprehensive solutions.

APM: How do you perceive Big Data impacting APM in the near future?

JK: There are two different ways to look at Big Data and APM. There is the Big Data that APM tools generate, and there is the ability to monitor and manage the applications that house Big Data infrastructure.

First, instrumenting and monitoring every transaction generates huge amounts of data. So we are seeing many APM vendors starting to implement Big Data stores themselves, and we are seeing that penetrate more and more of the market. And there are a few vendors currently undergoing re-architectures, moving from a traditional data store to a hybrid data store with a relational database on the front and Big Data behind.

Second is monitoring Big Data infrastructure. Most of the technologies are written in Java, which makes them a natural fit for an APM product. We have seen a couple of vendors start to message and market towards those who are running the data infrastructure, about ensuring performance. That is going to continue, and we will see more and more vendors position themselves as being able to monitor the data infrastructure.

The one area that has not been addressed by the market – which is not necessarily APM but is part of it – is data information assurance. How do you ensure that the data you are writing to your Big Data store is actually writing effectively? So that market is going to have to evolve as well. The Big Data administrator, or BDA, is going to need toolsets to be able to manage and monitor how the Big Data infrastructure is operating. That is something that has not really occurred at this point in the market. There has not been the same type of tools available for monitoring data infrastructure as there are for databases themselves.

APM: It seems like everyone is talking about Big Data this year. What do you see as the driver behind this interest?

JK: I think more and more companies are experimenting with Big Data infrastructure. There is obviously an influx of money that is going into that market, as far as the venture capitalists go. So I think part of it is being driven by vendors releasing products and venture capitalists funding those vendors.

And the other part is being driven by companies actually starting to experiment. There are use cases of critical transactional systems that are being run on the systems, which is kind of scary, in my opinion. A lot of the banks are starting to run pretty important transactions through these Big Data stores without actually having the proper tooling around them to understand data consistency. It is interesting what is happening and how the economics are changing the market.

APM: Do you have any predictions on how APM will evolve?

JK: We will have one or two predictions in the Magic Quadrant but nothing that I want to give away yet.

ABOUT Jonah Kowall

Jonah Kowall is a research director in Gartner's IT Operations Research group. He focuses on application performance monitoring (APM), event correlation and analysis (ECA), network management systems (NMSs), network performance management (NPM), network configuration and change management (NCCM), and general system and infrastructure monitoring technologies. Previously Kowall managed a global team of engineers and managers for MFG.com, and was responsible for monitoring and enterprise management software and architecture at Thomson Reuters.

Related Links:

Q&A Part One: Gartner Talks About APM Cool Vendors

Q&A Part Two: Gartner Talks About SaaS

Gartner Analyst Will Cappelli Talks about APM

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Q&A Part Three: Gartner Talks About Application Performance Management

Pete Goldin
APMdigest

In Part Three of APMdigest's exclusive interview,
Jonah Kowall, Research Director in Gartner's IT Operations Research group, discusses additional new requirements for the Magic Quadrant for APM, as well as the challenges of application performance management.

Start with Q&A Part One: Gartner Talks About APM Cool Vendors

Start with Q&A Part Two: Gartner Talks About SaaS APM

APM: You mentioned that Gartner is requiring companies in the Magic Quadrant on APM will now be required to offer a SaaS option. In addition, you are also requiring vendors to offer all of Gartner's Five Dimensions of APM, to be included in the Magic Quadrant for APM, correct?

JK: Yes. We previously required 3 out of 5 dimensions, but this year to be included in the Magic Quadrant a vendor must have a full-featured APM product.

APM: Do you expect this requirement to cut out many of the smaller players in the market?

JK: The new requirement will not cut out that many of the smaller players because most products are moving toward inclusion of all five dimensions. Vendors that did not offer deep dive capability, for example, are adding that in order to appeal to those other buyers. And vendors that did not have good end-user experience monitoring are adding JavaScript injection capabilities into their products to be able to fulfill that type of use case.

So we are starting to see many of these small vendors with one product and one agent be able to do all five dimensions. Since the last Magic Quadrant, about a year ago, we have seen many vendors that had four dimensions move to five dimensions, and those are all unified products.

APM: In your experience what are the main reasons why APM deployments fail to deliver?

JK: The number one issue that I see is a vendor will convince the customer that they should buy APM for all of their infrastructure and then they should work on deploying that and have a services engagement to do so. The issue is that when you buy hundreds of thousands of licenses of APM products, implementing those licenses across potentially hundreds of thousands of applications is not feasible in any reasonable amount of time.

Taking a much more balanced approach to identifying and implementing the critical components that should have APM on them tends to work better. Although the vendors do not appreciate it when you do smaller transactions with them, that tends to be the most effective use, and the reason why APM does not often deliver the value it is supposed to deliver.

The other challenge is that a company may not have the expertise in a tool that is potentially complex to implement and use. That is being combated by bringing APM downmarket and making it easier to use. Then the product can be leveraged not just by the third line developers but also the first and second line developers, in terms of being able to understand an application issue, and some type of diagnosis as to what the issue is, and who the issue should be escalated to. I think a lot of that is being driven by the push for simplicity versus the push for comprehensive solutions.

APM: How do you perceive Big Data impacting APM in the near future?

JK: There are two different ways to look at Big Data and APM. There is the Big Data that APM tools generate, and there is the ability to monitor and manage the applications that house Big Data infrastructure.

First, instrumenting and monitoring every transaction generates huge amounts of data. So we are seeing many APM vendors starting to implement Big Data stores themselves, and we are seeing that penetrate more and more of the market. And there are a few vendors currently undergoing re-architectures, moving from a traditional data store to a hybrid data store with a relational database on the front and Big Data behind.

Second is monitoring Big Data infrastructure. Most of the technologies are written in Java, which makes them a natural fit for an APM product. We have seen a couple of vendors start to message and market towards those who are running the data infrastructure, about ensuring performance. That is going to continue, and we will see more and more vendors position themselves as being able to monitor the data infrastructure.

The one area that has not been addressed by the market – which is not necessarily APM but is part of it – is data information assurance. How do you ensure that the data you are writing to your Big Data store is actually writing effectively? So that market is going to have to evolve as well. The Big Data administrator, or BDA, is going to need toolsets to be able to manage and monitor how the Big Data infrastructure is operating. That is something that has not really occurred at this point in the market. There has not been the same type of tools available for monitoring data infrastructure as there are for databases themselves.

APM: It seems like everyone is talking about Big Data this year. What do you see as the driver behind this interest?

JK: I think more and more companies are experimenting with Big Data infrastructure. There is obviously an influx of money that is going into that market, as far as the venture capitalists go. So I think part of it is being driven by vendors releasing products and venture capitalists funding those vendors.

And the other part is being driven by companies actually starting to experiment. There are use cases of critical transactional systems that are being run on the systems, which is kind of scary, in my opinion. A lot of the banks are starting to run pretty important transactions through these Big Data stores without actually having the proper tooling around them to understand data consistency. It is interesting what is happening and how the economics are changing the market.

APM: Do you have any predictions on how APM will evolve?

JK: We will have one or two predictions in the Magic Quadrant but nothing that I want to give away yet.

ABOUT Jonah Kowall

Jonah Kowall is a research director in Gartner's IT Operations Research group. He focuses on application performance monitoring (APM), event correlation and analysis (ECA), network management systems (NMSs), network performance management (NPM), network configuration and change management (NCCM), and general system and infrastructure monitoring technologies. Previously Kowall managed a global team of engineers and managers for MFG.com, and was responsible for monitoring and enterprise management software and architecture at Thomson Reuters.

Related Links:

Q&A Part One: Gartner Talks About APM Cool Vendors

Q&A Part Two: Gartner Talks About SaaS

Gartner Analyst Will Cappelli Talks about APM

Hot Topic
The Latest
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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 ...