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Q&A Part Two: CA Technologies Talks About APM

Pete Goldin
APMdigest

In Part Two of APMdigest's exclusive interview, Aruna Ravichandran, CA Technologies Vice President, Product and Solution Marketing, Application Performance Management & DevOps, discusses the benefits of APM and APM SaaS, and the differences between standard APM and APM for the cloud.

Start with Part One of the interview

APM: What are the top benefits of APM?

AR: APM is all about providing an outstanding customer or end-user experience. A positive customer experience improves customer satisfaction and brand perception thereby creating inspired users that directly impact business performance. It helps organizations reduce cost, increase brand loyalty, improve operational efficiency, and accelerate delivery of new business services to grow revenue.

APM: What do you see as the benefits of SaaS, in terms of APM?

AR: SaaS is all about ease of deployment, ease of use, and reduced cost. Looking at technology and business macro trends today, SaaS as a licensing and delivery model continues to show aggressive growth, and is pushing into the mainstream with enterprise IT organizations.

On the road to broad adoption, SaaS APM is following the path set in different markets (i.e. CRM), but is resolving its own unique maturity challenges. For example, what we have learned is that a SaaS APM solution has to be purposely designed to resolve specific APM problems, and with specific user personas in mind. A great example of a well-targeted product that is addressing a real need in the market is CA APM Cloud Monitor. This solution is used by many of our customers as a simple and easy to deploy End User Experience solution for Tier 2 and Tier 3 application, or to provide a complimentary synthetic monitoring component as part of a comprehensive CA APM solution for T1 applications.

Users expect an APM SaaS solution that delivers not only a cost optimized model that is easy to deploy with a quick time-to-value, but also one that is flexible and provides a persona-centric set of advanced capabilities that do not require a change in the platform or going on-premise. Not all of our customers' business and their applications are the same, but they all agree that SaaS APM should not just be some lightweight intro or “hook” to an APM solution that can completely resolve their problem. SaaS solutions need to be user centric, designed for specific personas, and have the ability to comprehensively resolve targeted APM challenges.

APM: What are the main differences between standard APM and APM for the cloud?

AR: Once again I will start answering with the customer needs in mind. The difference between APM and APM for Cloud solutions depends on the customer business, and the needs of their business models. For example, a Cloud providers' main focus is in exceptional customer experience. Performance of their applications is the cornerstone of the End User Experience and has direct impact on revenue streams. Their requirements are having a deep and scalable APM solution that will help them preemptively resolve issues, continue improving performance over time, and the ability to infinitely scale with their deployed solution. This is APM for the cloud.

A different example might include a business model where IT is bursting into the Public Cloud at times of peak load. They want to be able to make sure that transactions that are partially traversing applications in the Public Cloud will continue providing exceptional customer experience. In that case, they might deploy an APM solution that is running synthetic transactions to alert on any application issue.
This is just one simple use case, depicting the difference between customer needs that are driving APM and APM for Cloud requirements.

APM: With this in mind, what is the difference between CA APM vs. CA APM Cloud Monitor?

AR: When it comes to CA solutions, customers' needs are driving the solution and expected benefits. With CA APM you can ensure that every customer interaction with your applications is driving your business, by collecting on-premise information about applications, infrastructure, and end user experience and then taking action to optimize each user interaction with your applications. CA APM is an on-premise solution for datacenters, private clouds, and public clouds that require deep 20/20 vision of their applications.

CA APM Cloud Monitor is different in that it is a SaaS-based solution that runs synthetic transactions to emulate what a real-user might experience from over 96 monitoring stations across the globe. CA APM Cloud Monitor enables IT Operations teams to quickly identify and resolve performance issues and proactively manage the end-user experience of their applications around the world, even when there are no users on the system.

CA APM Cloud Monitor complements the on-premise CA APM solution for a more comprehensive solution and provides a SaaS-based option for applications that don't require full APM. This approach can help you optimize your organization's investments by employing the right level of synthetic monitoring so that you consistently deliver high service levels and an exceptional end-user experience.

Q&A Part Three: CA Technologies Talks About APM

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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 ...

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.

Q&A Part Two: CA Technologies Talks About APM

Pete Goldin
APMdigest

In Part Two of APMdigest's exclusive interview, Aruna Ravichandran, CA Technologies Vice President, Product and Solution Marketing, Application Performance Management & DevOps, discusses the benefits of APM and APM SaaS, and the differences between standard APM and APM for the cloud.

Start with Part One of the interview

APM: What are the top benefits of APM?

AR: APM is all about providing an outstanding customer or end-user experience. A positive customer experience improves customer satisfaction and brand perception thereby creating inspired users that directly impact business performance. It helps organizations reduce cost, increase brand loyalty, improve operational efficiency, and accelerate delivery of new business services to grow revenue.

APM: What do you see as the benefits of SaaS, in terms of APM?

AR: SaaS is all about ease of deployment, ease of use, and reduced cost. Looking at technology and business macro trends today, SaaS as a licensing and delivery model continues to show aggressive growth, and is pushing into the mainstream with enterprise IT organizations.

On the road to broad adoption, SaaS APM is following the path set in different markets (i.e. CRM), but is resolving its own unique maturity challenges. For example, what we have learned is that a SaaS APM solution has to be purposely designed to resolve specific APM problems, and with specific user personas in mind. A great example of a well-targeted product that is addressing a real need in the market is CA APM Cloud Monitor. This solution is used by many of our customers as a simple and easy to deploy End User Experience solution for Tier 2 and Tier 3 application, or to provide a complimentary synthetic monitoring component as part of a comprehensive CA APM solution for T1 applications.

Users expect an APM SaaS solution that delivers not only a cost optimized model that is easy to deploy with a quick time-to-value, but also one that is flexible and provides a persona-centric set of advanced capabilities that do not require a change in the platform or going on-premise. Not all of our customers' business and their applications are the same, but they all agree that SaaS APM should not just be some lightweight intro or “hook” to an APM solution that can completely resolve their problem. SaaS solutions need to be user centric, designed for specific personas, and have the ability to comprehensively resolve targeted APM challenges.

APM: What are the main differences between standard APM and APM for the cloud?

AR: Once again I will start answering with the customer needs in mind. The difference between APM and APM for Cloud solutions depends on the customer business, and the needs of their business models. For example, a Cloud providers' main focus is in exceptional customer experience. Performance of their applications is the cornerstone of the End User Experience and has direct impact on revenue streams. Their requirements are having a deep and scalable APM solution that will help them preemptively resolve issues, continue improving performance over time, and the ability to infinitely scale with their deployed solution. This is APM for the cloud.

A different example might include a business model where IT is bursting into the Public Cloud at times of peak load. They want to be able to make sure that transactions that are partially traversing applications in the Public Cloud will continue providing exceptional customer experience. In that case, they might deploy an APM solution that is running synthetic transactions to alert on any application issue.
This is just one simple use case, depicting the difference between customer needs that are driving APM and APM for Cloud requirements.

APM: With this in mind, what is the difference between CA APM vs. CA APM Cloud Monitor?

AR: When it comes to CA solutions, customers' needs are driving the solution and expected benefits. With CA APM you can ensure that every customer interaction with your applications is driving your business, by collecting on-premise information about applications, infrastructure, and end user experience and then taking action to optimize each user interaction with your applications. CA APM is an on-premise solution for datacenters, private clouds, and public clouds that require deep 20/20 vision of their applications.

CA APM Cloud Monitor is different in that it is a SaaS-based solution that runs synthetic transactions to emulate what a real-user might experience from over 96 monitoring stations across the globe. CA APM Cloud Monitor enables IT Operations teams to quickly identify and resolve performance issues and proactively manage the end-user experience of their applications around the world, even when there are no users on the system.

CA APM Cloud Monitor complements the on-premise CA APM solution for a more comprehensive solution and provides a SaaS-based option for applications that don't require full APM. This approach can help you optimize your organization's investments by employing the right level of synthetic monitoring so that you consistently deliver high service levels and an exceptional end-user experience.

Q&A Part Three: CA Technologies Talks About APM

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.