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Q&A: Ovum Talks About APM

Pete Goldin
APMdigest

In APMdigest's exclusive interview, Michael Azoff, Principal Analyst at Ovum, discusses his discoveries while researching the Ovum Decision Matrix on APM 2014-15.

APM: What are the main changes in the APM market that you have seen since your last APM report?

MA: Log management continues to grow well as it meets demand for swift performance answers with light weight tools. The need for mobile support continues to grow as expected. Vendors are visible on the market that we have not seen at the top level before, offering reduced functionality compared with top solutions but with ease of use, competitive pricing, and enough functionality to keep users happy.

APM: What do you see as the main drivers behind the strong growth of the APM market?

MA: Applications and services are more complex. At the same time those that are consumer facing have users who will drop the app/service/website if it is slow to respond or worse. The competitive nature of the market is placing higher demands on producing high quality and high performance solutions.

APM: What are the most important new APM capabilities that a buyer should look for?

MA: For development support, it has to be source code line level detail if a crash occurs.

For operations, it needs to provide end to end transaction traceability.

Analytics is now a common feature in top end solutions. Some are more sophisticated than others, e.g. offering complex event processing, and this now extends to big data processing.

APM: What key APM capabilities do the vendors still need to work on?

MA: DevOps and continuous delivery will continue to grow in adoption and there is a role for APM to support these approaches. Also the mobile client and server side APM need to be joined up.

Some players are transitioning their solutions to meet the demands of cloud, web, and mobile, in face of new competition that has this built-in from start. The end users now want seamless cloud and on-premise solutions, as well as seamless mobile client and server side APM.

APM: In your report, you mention log management as an emerging category within APM, and you bring it up now as well. Do you see log management as a replacement for traditional APM, or as an additional capability to augment APM?

MA: Log management is sold as a complementary technology, though any organization that is budget constrained will explore log management as a minimal entry to APM. Where in-house development is conducted, then the possibility of adding purpose built logs for use by log management tools can lead to a form of DIY APM.

APM: Where does APM SaaS (Software-as-a-Service) stand currently?

MA: The pattern when speaking to vendors is that APM SaaS is growing in demand from customers and therefore needs to be available today, though there is a sizable on-premise demand from large enterprises and public sector.

APM: Traditionally BMC is seen as one of the "Big Four" leaders in APM. What caused you to put them in the challenger category instead?

MA: Our ranking of BMC is based on how well it performed against a series of metrics in three dimensions: technology, financial, and market execution. So yes, BMC has slipped measured against its peers.

All vendors make decisions about what they want or are capable of bringing to the market. This does make the solution horse race approach not always appropriate for individual customers, as lesser ranked solutions will make a better fit for some customers. This is our advice: consider our shortlist rankings but examine solutions based on your actual needs. We have a spreadsheet where our customers can weight features according to their needs and this will create a custom ranking.

APM: Any predictions about where APM is headed, and how this may impact your next report?

MA: Two vendors which just missed the boat in being included in our analysis are among the new breed solutions and we look forward to including them next time round: AppDynamics and New Relic.

It is good to see the field as active as it is, with new technologies appearing in analytics and new vendors becoming visible at the top end of market.

ABOUT Michael Azoff

Michael Azoff (PhD, MIEEE) has been working as an IT industry analyst since 2003, bringing over 20 years of experience in pure and applied research and consulting in the IT industry.

At Ovum he leads the software development and lifecycle management (SDLM) research and his current focus is on Agile practices in software development, including enterprise Agile transformation initiatives, DevOps, cloud related SDLM, rich Internet applications, and enterprise IT mobile development.

Ovum, an Informa business based in London, provides clients with independent and objective analysis that enables them to make better business and technology decisions. Ovum research draws upon over 400,000 interviews a year with business and technology, telecoms and sourcing decision-makers, giving Ovum and its clients unparalleled insight not only into business requirements but also the technology that organizations must support.

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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 ...

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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 ...

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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.

Q&A: Ovum Talks About APM

Pete Goldin
APMdigest

In APMdigest's exclusive interview, Michael Azoff, Principal Analyst at Ovum, discusses his discoveries while researching the Ovum Decision Matrix on APM 2014-15.

APM: What are the main changes in the APM market that you have seen since your last APM report?

MA: Log management continues to grow well as it meets demand for swift performance answers with light weight tools. The need for mobile support continues to grow as expected. Vendors are visible on the market that we have not seen at the top level before, offering reduced functionality compared with top solutions but with ease of use, competitive pricing, and enough functionality to keep users happy.

APM: What do you see as the main drivers behind the strong growth of the APM market?

MA: Applications and services are more complex. At the same time those that are consumer facing have users who will drop the app/service/website if it is slow to respond or worse. The competitive nature of the market is placing higher demands on producing high quality and high performance solutions.

APM: What are the most important new APM capabilities that a buyer should look for?

MA: For development support, it has to be source code line level detail if a crash occurs.

For operations, it needs to provide end to end transaction traceability.

Analytics is now a common feature in top end solutions. Some are more sophisticated than others, e.g. offering complex event processing, and this now extends to big data processing.

APM: What key APM capabilities do the vendors still need to work on?

MA: DevOps and continuous delivery will continue to grow in adoption and there is a role for APM to support these approaches. Also the mobile client and server side APM need to be joined up.

Some players are transitioning their solutions to meet the demands of cloud, web, and mobile, in face of new competition that has this built-in from start. The end users now want seamless cloud and on-premise solutions, as well as seamless mobile client and server side APM.

APM: In your report, you mention log management as an emerging category within APM, and you bring it up now as well. Do you see log management as a replacement for traditional APM, or as an additional capability to augment APM?

MA: Log management is sold as a complementary technology, though any organization that is budget constrained will explore log management as a minimal entry to APM. Where in-house development is conducted, then the possibility of adding purpose built logs for use by log management tools can lead to a form of DIY APM.

APM: Where does APM SaaS (Software-as-a-Service) stand currently?

MA: The pattern when speaking to vendors is that APM SaaS is growing in demand from customers and therefore needs to be available today, though there is a sizable on-premise demand from large enterprises and public sector.

APM: Traditionally BMC is seen as one of the "Big Four" leaders in APM. What caused you to put them in the challenger category instead?

MA: Our ranking of BMC is based on how well it performed against a series of metrics in three dimensions: technology, financial, and market execution. So yes, BMC has slipped measured against its peers.

All vendors make decisions about what they want or are capable of bringing to the market. This does make the solution horse race approach not always appropriate for individual customers, as lesser ranked solutions will make a better fit for some customers. This is our advice: consider our shortlist rankings but examine solutions based on your actual needs. We have a spreadsheet where our customers can weight features according to their needs and this will create a custom ranking.

APM: Any predictions about where APM is headed, and how this may impact your next report?

MA: Two vendors which just missed the boat in being included in our analysis are among the new breed solutions and we look forward to including them next time round: AppDynamics and New Relic.

It is good to see the field as active as it is, with new technologies appearing in analytics and new vendors becoming visible at the top end of market.

ABOUT Michael Azoff

Michael Azoff (PhD, MIEEE) has been working as an IT industry analyst since 2003, bringing over 20 years of experience in pure and applied research and consulting in the IT industry.

At Ovum he leads the software development and lifecycle management (SDLM) research and his current focus is on Agile practices in software development, including enterprise Agile transformation initiatives, DevOps, cloud related SDLM, rich Internet applications, and enterprise IT mobile development.

Ovum, an Informa business based in London, provides clients with independent and objective analysis that enables them to make better business and technology decisions. Ovum research draws upon over 400,000 interviews a year with business and technology, telecoms and sourcing decision-makers, giving Ovum and its clients unparalleled insight not only into business requirements but also the technology that organizations must support.

Hot Topic
The Latest
The Latest 10

The Latest

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 ...

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.