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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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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...