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APM and Observability: Cutting Through the Confusion — Part 8

Pete Goldin
APMdigest

So after all this discussion, what do the experts say about whether you need APM, observability or both?

Start with: APM and Observability - Cutting Through the Confusion - Part 7

“In today's complex digital landscape, organizations need both APM and Observability to not only react to issues but to anticipate and mitigate them proactively, ensuring robust performance and resilience,” says Gab Menachem, VP ITOM at ServiceNow.

"Organizations will absolutely continue to leverage both approaches, particularly as the number of solutions in the market that support both approaches increases," Bryan Cole, Director of Customer Engineering at Tricentis, agrees.

"Most organizations use a combination of tools," adds Gurjeet Arora, CEO and Co-Founder of Observo AI. "It's rare to find a single solution that serves every team's needs. DevOps may rely on telemetry data pipelines, while application teams use APM tools, and security teams use SIEM platforms. Ideally, these tools integrate with each other and share a common data layer, but in reality, priorities differ by role. What matters most is having a strategy that allows each team to extract value from telemetry without duplicating data or increasing costs unnecessarily."

One Tool Fits All

Why choose APM or Observability when you can have both? Modern tools increasingly combine both observability and APM, bringing together deep insights for legacy applications and observability for distributed, cloud-native systems, says Varma Kunaparaju, SVP and GM for Cloud Platform and OpsRamp Software at HPE.

"It's becoming increasingly clear that for many, a combined approach, leveraging both APM and observability functionalities within a single, well-integrated tool, offers the most comprehensive solution for their monitoring and diagnostic requirements," confirms Arun Balachandran, Senior Product Marketing Manager, ManageEngine APM Solutions.

Ideally, organizations should aim for an integrated platform that combines both APM and observability, advises Menachem from ServiceNow. The objective isn't to accumulate more tools but to achieve deeper insights.

Andreas Grabner, Fellow DevRel and CNCF Ambassador, Dynatrace, agrees, "Ideally, organizations wouldn't need to manage separate tools. The most effective approach combines APM and observability into a single platform that provides unified data, context-rich insights, and automation. Fragmented tooling leads to data silos and slower issue resolution, while a converged platform improves visibility, collaboration, and time-to-value."

A unified platform serves as a single source of truth, streamlining resolution processes, enhancing accountability, and empowering informed decision-making across the board, Menachem from ServiceNow adds.

Integrated solutions can offer convenience, optimized operations and more robust performance for complex systems, Kunaparaju of HPE adds.

"Organizations increasingly recognize that they need unified platforms rather than point solutions," Rakesh Gupta, Head of Product Management at Observe points out. "The market is clearly moving toward consolidation, with 'single pane of glass' becoming a standard requirement from IT leadership."

There are tools that do both, but just as with any other tool purchase, you must weigh the pros and cons out there, warns Chrystal Taylor, Tech Evangelist at SolarWinds. There may be tools developed specifically for APM that have the benefit of greater maturity and feature sets that haven't been worked into observability tools out there. There may also be ways to integrate that data into your observability tools to get the best of both worlds. There are many options, so it's important to find the one best suited to your organization's needs and budget.

The trend is toward unified platforms that incorporate both capabilities, but organizations should evaluate their specific requirements before assuming one solution fits all, Paul Appleby, CEO of Virtana, concludes.

Observability with a Side of APM

Many of the experts recommend the tool combination in the form of a comprehensive Observability platform that includes APM among multiple other capabilities — and this concept further supports the view that APM is a subset of Observability, discussed in Part 3 of this series.

While some organizations choose to use both APM and Observability, it isn't a necessity, according to Emily Nakashima, VP of Engineering at Honeycomb. "Since APM is a subset of observability, organizations with a well-crafted observability strategy can utilize an observability tool to get everything APM offers and more."

"APM is just one part of the observability picture," explains Bahubali Shetti, Senior Director, Product Marketing, Elastic. "While APM focuses on identifying what's wrong within a specific service or set of services, observability helps you understand why by connecting signals across your entire environment using AI and machine learning. Full observability enables correlation of metrics, logs, and traces to uncover root causes, analyze patterns, and generate context-aware recommendations."

Shetti adds that with features like an AI Assistant, users can ask questions like, "Why is my checkout latency spiking?" and receive insights that automatically connect traces, logs, infrastructure metrics, and even internal knowledge like GitHub issues or support tickets. Observability also brings advanced capabilities like anomaly detection, pattern recognition, event categorization, and predictive analytics, allowing teams to move from reactive troubleshooting to proactive optimization. When combined with APM data, these capabilities deliver the end-to-end visibility and intelligence users need to resolve issues faster and scale systems more effectively.

"Both can co-exist, but for cost and complexity reasons, it is likely that organizations will move to the more encompassing observability approach," says Sven Delmas, VP of Research at Mezmo. "Note that an observability solution may or may not be just one tool."

Today organizations can expect to have APM in their observability tool but not necessarily vice versa, adds Hugo Kaczmarek, Director of Product, APM Suite at Datadog.

Keeping It Separate

Even though the experts recommend combining tools to get the best of both worlds, some organizations still rely on separate tools — whether due to legacy systems, departmental preferences, or unique functional requirements, says Balachandran from ManageEngine.

"And even the solutions that are a blend of both worlds — usually the result of acquired companies and merged toolsets — end up focusing on more of one than the other, with the lesser of the two simply there to provide context to the other or, worse, to be little more than box-checkers to pass an RFP," adds Leon Adato, Principal Technology Advocate, Catchpoint. "So companies probably will need both. More specifically, different teams within an organization will need the capabilities of one more than the other."

Unified Visibility and Actionable Insights

"What's most important is that organizations take a close look at what different tools and platforms offer and ensure that everything they require regarding APM/observability capabilities are met — whether that be through one tool, two tools, or five," says Cole from Tricentis.

Ultimately, the most important factor is ensuring that whichever tools are in place deliver unified visibility and actionable insights across the entire technology stack, from the application layer all the way down to the underlying infrastructure, recommends Balachandran from ManageEngine.

Start with: APM and Observability - Cutting Through the Confusion - Part 9, covering open source's impact on the evolution Observability.

Pete Goldin is Editor and Publisher of APMdigest

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

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If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

APM and Observability: Cutting Through the Confusion — Part 8

Pete Goldin
APMdigest

So after all this discussion, what do the experts say about whether you need APM, observability or both?

Start with: APM and Observability - Cutting Through the Confusion - Part 7

“In today's complex digital landscape, organizations need both APM and Observability to not only react to issues but to anticipate and mitigate them proactively, ensuring robust performance and resilience,” says Gab Menachem, VP ITOM at ServiceNow.

"Organizations will absolutely continue to leverage both approaches, particularly as the number of solutions in the market that support both approaches increases," Bryan Cole, Director of Customer Engineering at Tricentis, agrees.

"Most organizations use a combination of tools," adds Gurjeet Arora, CEO and Co-Founder of Observo AI. "It's rare to find a single solution that serves every team's needs. DevOps may rely on telemetry data pipelines, while application teams use APM tools, and security teams use SIEM platforms. Ideally, these tools integrate with each other and share a common data layer, but in reality, priorities differ by role. What matters most is having a strategy that allows each team to extract value from telemetry without duplicating data or increasing costs unnecessarily."

One Tool Fits All

Why choose APM or Observability when you can have both? Modern tools increasingly combine both observability and APM, bringing together deep insights for legacy applications and observability for distributed, cloud-native systems, says Varma Kunaparaju, SVP and GM for Cloud Platform and OpsRamp Software at HPE.

"It's becoming increasingly clear that for many, a combined approach, leveraging both APM and observability functionalities within a single, well-integrated tool, offers the most comprehensive solution for their monitoring and diagnostic requirements," confirms Arun Balachandran, Senior Product Marketing Manager, ManageEngine APM Solutions.

Ideally, organizations should aim for an integrated platform that combines both APM and observability, advises Menachem from ServiceNow. The objective isn't to accumulate more tools but to achieve deeper insights.

Andreas Grabner, Fellow DevRel and CNCF Ambassador, Dynatrace, agrees, "Ideally, organizations wouldn't need to manage separate tools. The most effective approach combines APM and observability into a single platform that provides unified data, context-rich insights, and automation. Fragmented tooling leads to data silos and slower issue resolution, while a converged platform improves visibility, collaboration, and time-to-value."

A unified platform serves as a single source of truth, streamlining resolution processes, enhancing accountability, and empowering informed decision-making across the board, Menachem from ServiceNow adds.

Integrated solutions can offer convenience, optimized operations and more robust performance for complex systems, Kunaparaju of HPE adds.

"Organizations increasingly recognize that they need unified platforms rather than point solutions," Rakesh Gupta, Head of Product Management at Observe points out. "The market is clearly moving toward consolidation, with 'single pane of glass' becoming a standard requirement from IT leadership."

There are tools that do both, but just as with any other tool purchase, you must weigh the pros and cons out there, warns Chrystal Taylor, Tech Evangelist at SolarWinds. There may be tools developed specifically for APM that have the benefit of greater maturity and feature sets that haven't been worked into observability tools out there. There may also be ways to integrate that data into your observability tools to get the best of both worlds. There are many options, so it's important to find the one best suited to your organization's needs and budget.

The trend is toward unified platforms that incorporate both capabilities, but organizations should evaluate their specific requirements before assuming one solution fits all, Paul Appleby, CEO of Virtana, concludes.

Observability with a Side of APM

Many of the experts recommend the tool combination in the form of a comprehensive Observability platform that includes APM among multiple other capabilities — and this concept further supports the view that APM is a subset of Observability, discussed in Part 3 of this series.

While some organizations choose to use both APM and Observability, it isn't a necessity, according to Emily Nakashima, VP of Engineering at Honeycomb. "Since APM is a subset of observability, organizations with a well-crafted observability strategy can utilize an observability tool to get everything APM offers and more."

"APM is just one part of the observability picture," explains Bahubali Shetti, Senior Director, Product Marketing, Elastic. "While APM focuses on identifying what's wrong within a specific service or set of services, observability helps you understand why by connecting signals across your entire environment using AI and machine learning. Full observability enables correlation of metrics, logs, and traces to uncover root causes, analyze patterns, and generate context-aware recommendations."

Shetti adds that with features like an AI Assistant, users can ask questions like, "Why is my checkout latency spiking?" and receive insights that automatically connect traces, logs, infrastructure metrics, and even internal knowledge like GitHub issues or support tickets. Observability also brings advanced capabilities like anomaly detection, pattern recognition, event categorization, and predictive analytics, allowing teams to move from reactive troubleshooting to proactive optimization. When combined with APM data, these capabilities deliver the end-to-end visibility and intelligence users need to resolve issues faster and scale systems more effectively.

"Both can co-exist, but for cost and complexity reasons, it is likely that organizations will move to the more encompassing observability approach," says Sven Delmas, VP of Research at Mezmo. "Note that an observability solution may or may not be just one tool."

Today organizations can expect to have APM in their observability tool but not necessarily vice versa, adds Hugo Kaczmarek, Director of Product, APM Suite at Datadog.

Keeping It Separate

Even though the experts recommend combining tools to get the best of both worlds, some organizations still rely on separate tools — whether due to legacy systems, departmental preferences, or unique functional requirements, says Balachandran from ManageEngine.

"And even the solutions that are a blend of both worlds — usually the result of acquired companies and merged toolsets — end up focusing on more of one than the other, with the lesser of the two simply there to provide context to the other or, worse, to be little more than box-checkers to pass an RFP," adds Leon Adato, Principal Technology Advocate, Catchpoint. "So companies probably will need both. More specifically, different teams within an organization will need the capabilities of one more than the other."

Unified Visibility and Actionable Insights

"What's most important is that organizations take a close look at what different tools and platforms offer and ensure that everything they require regarding APM/observability capabilities are met — whether that be through one tool, two tools, or five," says Cole from Tricentis.

Ultimately, the most important factor is ensuring that whichever tools are in place deliver unified visibility and actionable insights across the entire technology stack, from the application layer all the way down to the underlying infrastructure, recommends Balachandran from ManageEngine.

Start with: APM and Observability - Cutting Through the Confusion - Part 9, covering open source's impact on the evolution Observability.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...