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APM and Observability: Cutting Through the Confusion — Part 5

Pete Goldin
APMdigest

Many of the experts see Observability as an evolution of APM, providing even greater visibility.

Start with: APM and Observability - Cutting Through the Confusion - Part 4

"APM remains a cornerstone in the toolkit for application performance management, crucial for pinpointing and resolving application-specific issues. Observability, however, is the evolution of this concept, expanding the scope to encompass distributed systems and cloud environments," explains Gab Menachem, VP ITOM at ServiceNow.

"Based on the current needs for complex, cloud-native systems, many APM tools have evolved into observability platforms," Ajay Khanna, CMO at Yugabyte, agrees. "This is evident in how the Gartner Magic Quadrant name has evolved over the years — from APM to APM & Observability to the current iteration of Observability Platforms."

Observability encompasses a broader spectrum of monitoring and diagnostic capabilities compared to traditional APM tools, Khanna observes. As systems grow in scale and complexity, the value of observability as something broader and more adaptable than APM is becoming clearer. Ultimately, while APM is useful for maintaining performance baselines and triggering alerts, observability provides the depth, flexibility, and adaptability required to manage modern, dynamic systems. It empowers teams not only to detect that something is wrong, but also to understand why it's happening — even in cases where the problem was previously unknown or poorly understood.

Sven Delmas, VP of Research at Mezmo adds, "The boundaries are fluid and changing in these kinds of dynamic systems — APM use morphs into observability; observability implementations draw from the more predefined capabilities/solutions of APM."

In fact, some experts believe that the confusion between APM and Observability is rooted in this evolution. Severin Neumann, Head of Community & Developer Relations at Causely, says, "There is confusion in the market about APM vs. observability, largely because the shift has been more evolutionary than revolutionary. Many observability concepts build on capabilities that APM tools have offered for years behind vendors' closed gardens, like code-level tracing and analytics, just with more flexibility, scale for cloud native systems and with open standards. This overlap blurs the lines, especially as both types of tools adopt similar language and features."

Bringing Everything Together

Some experts see Observability's role as bringing a range of capabilities, including APM, together. Mimi Shalash, Observability Advisor at Splunk, a Cisco Company, explains, "Observability brings once-separate monitoring domains (like application performance monitoring, infrastructure monitoring, digital experience monitoring, AIOps and log analytics) together in order to enable unified visibility and eliminate blind spots. This is especially critical with the shift to cloud native as technology environments become more distributed. A comprehensive observability practice should include all of these components mentioned above and leverage artificial intelligence (AI) and machine learning (ML) to drive earlier detection and faster investigation of business impacting incidents."

Andreas Grabner, Fellow DevRel and CNCF Ambassador, Dynatrace, adds, "Observability provides a broader, real-time view of system health by integrating signals from across the entire stack — not just the application layer. This includes infrastructure telemetry, cloud services, security events, and user behavior data. It enables proactive problem detection, faster root-cause isolation, and more effective collaboration across DevOps, SRE, and business teams."

Another way Observability has evolved from APM is the addition of standardized open source elements. Neumann, from Causely says, "Observability's shift from proprietary APM vendor agents to open, standardized data has significantly expanded the surface of applications that can be instrumented."

Observability Is Essential

Many of the experts see this evolution making Observability essential in today's dynamic distributed enterprise.

Carlos Casanova, Principal Analyst at Forrester, explains, "The features/functions of APM do not go away and are still very much needed. They are just added onto with the investigative capabilities of observability. I personally don't see why organizations would settle for just APM these days when there are so many options to do much more with observability. The tracing, alerting, analytics are all vital elements that observability needs in order to dig deeper and explore without the pre-instrumentation. Observability provides the high cardinality and multi-dimensional support for a system that you don't get with just APM."

"For modern, distributed architectures, observability has become essential," says Brian Douglas, Head of Ecosystem, Cloud Native Computing Foundation (CNCF). "It allows teams to understand relationships between components, identify emergent issues, and trace performance degradations across microservices and infrastructure layers."

CNCF's 2025 Tech Radar validates this shift, with OpenTelemetry and Cortex positioned in the "adopt" tier, reflecting their growing role in powering flexible, telemetry-driven operations.

"Observability is like having a superpower for IT operations," asserts Varma Kunaparaju, SVP and GM for Cloud Platform and OpsRamp Software at HPE. "It dives into logs, metrics, and traces, revealing hidden issues across distributed systems. Designed for microservices and cloud-native architectures, it provides end-to-end tracing and correlates data from multiple sources, offering a holistic view of system behavior."

Observability Limitations

Although Observability is considered essential by the majority of experts, this does not mean the technology always lives up to this endorsement. Some experts outline the issues here:

Lack of True Insight

APM or application performance monitoring is agent-based application instrumentation that measures standard stats such as memory utilization and transaction latency, and is focused on known failure modes. Very little has changed with the rise of observability — the same concepts apply with modest efforts to meet the definition of observability. The fundamental focus of observability is finding insights into your data, centered around the idea of unknowns, which enable the discovery of unknown failure modes. Current observability platforms struggle to deliver the definition of observability and instead are primarily traditional APM with a better UI, that dabble in observability.

Observability is supposed to provide teams with insight into known failure states, but in practice, the ability to provide true insight is limited. Observability has become more about the upsell than delivering actual value.
Ed Bailey
Field CISO, Cribl

Costly Data Volumes

Many vendors claim to offer "observability" but still force customers into the same costly tradeoffs — sampling traces, limiting log retention, or splitting data across multiple tools. Whether you call it APM or observability is irrelevant if the platform can't actually handle modern data volumes economically.
Rakesh Gupta
Head of Product Management, Observe

Go to: APM and Observability: Cutting Through the Confusion - Part 6, covering the differing use cases of APM and Observability.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

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 ...

APM and Observability: Cutting Through the Confusion — Part 5

Pete Goldin
APMdigest

Many of the experts see Observability as an evolution of APM, providing even greater visibility.

Start with: APM and Observability - Cutting Through the Confusion - Part 4

"APM remains a cornerstone in the toolkit for application performance management, crucial for pinpointing and resolving application-specific issues. Observability, however, is the evolution of this concept, expanding the scope to encompass distributed systems and cloud environments," explains Gab Menachem, VP ITOM at ServiceNow.

"Based on the current needs for complex, cloud-native systems, many APM tools have evolved into observability platforms," Ajay Khanna, CMO at Yugabyte, agrees. "This is evident in how the Gartner Magic Quadrant name has evolved over the years — from APM to APM & Observability to the current iteration of Observability Platforms."

Observability encompasses a broader spectrum of monitoring and diagnostic capabilities compared to traditional APM tools, Khanna observes. As systems grow in scale and complexity, the value of observability as something broader and more adaptable than APM is becoming clearer. Ultimately, while APM is useful for maintaining performance baselines and triggering alerts, observability provides the depth, flexibility, and adaptability required to manage modern, dynamic systems. It empowers teams not only to detect that something is wrong, but also to understand why it's happening — even in cases where the problem was previously unknown or poorly understood.

Sven Delmas, VP of Research at Mezmo adds, "The boundaries are fluid and changing in these kinds of dynamic systems — APM use morphs into observability; observability implementations draw from the more predefined capabilities/solutions of APM."

In fact, some experts believe that the confusion between APM and Observability is rooted in this evolution. Severin Neumann, Head of Community & Developer Relations at Causely, says, "There is confusion in the market about APM vs. observability, largely because the shift has been more evolutionary than revolutionary. Many observability concepts build on capabilities that APM tools have offered for years behind vendors' closed gardens, like code-level tracing and analytics, just with more flexibility, scale for cloud native systems and with open standards. This overlap blurs the lines, especially as both types of tools adopt similar language and features."

Bringing Everything Together

Some experts see Observability's role as bringing a range of capabilities, including APM, together. Mimi Shalash, Observability Advisor at Splunk, a Cisco Company, explains, "Observability brings once-separate monitoring domains (like application performance monitoring, infrastructure monitoring, digital experience monitoring, AIOps and log analytics) together in order to enable unified visibility and eliminate blind spots. This is especially critical with the shift to cloud native as technology environments become more distributed. A comprehensive observability practice should include all of these components mentioned above and leverage artificial intelligence (AI) and machine learning (ML) to drive earlier detection and faster investigation of business impacting incidents."

Andreas Grabner, Fellow DevRel and CNCF Ambassador, Dynatrace, adds, "Observability provides a broader, real-time view of system health by integrating signals from across the entire stack — not just the application layer. This includes infrastructure telemetry, cloud services, security events, and user behavior data. It enables proactive problem detection, faster root-cause isolation, and more effective collaboration across DevOps, SRE, and business teams."

Another way Observability has evolved from APM is the addition of standardized open source elements. Neumann, from Causely says, "Observability's shift from proprietary APM vendor agents to open, standardized data has significantly expanded the surface of applications that can be instrumented."

Observability Is Essential

Many of the experts see this evolution making Observability essential in today's dynamic distributed enterprise.

Carlos Casanova, Principal Analyst at Forrester, explains, "The features/functions of APM do not go away and are still very much needed. They are just added onto with the investigative capabilities of observability. I personally don't see why organizations would settle for just APM these days when there are so many options to do much more with observability. The tracing, alerting, analytics are all vital elements that observability needs in order to dig deeper and explore without the pre-instrumentation. Observability provides the high cardinality and multi-dimensional support for a system that you don't get with just APM."

"For modern, distributed architectures, observability has become essential," says Brian Douglas, Head of Ecosystem, Cloud Native Computing Foundation (CNCF). "It allows teams to understand relationships between components, identify emergent issues, and trace performance degradations across microservices and infrastructure layers."

CNCF's 2025 Tech Radar validates this shift, with OpenTelemetry and Cortex positioned in the "adopt" tier, reflecting their growing role in powering flexible, telemetry-driven operations.

"Observability is like having a superpower for IT operations," asserts Varma Kunaparaju, SVP and GM for Cloud Platform and OpsRamp Software at HPE. "It dives into logs, metrics, and traces, revealing hidden issues across distributed systems. Designed for microservices and cloud-native architectures, it provides end-to-end tracing and correlates data from multiple sources, offering a holistic view of system behavior."

Observability Limitations

Although Observability is considered essential by the majority of experts, this does not mean the technology always lives up to this endorsement. Some experts outline the issues here:

Lack of True Insight

APM or application performance monitoring is agent-based application instrumentation that measures standard stats such as memory utilization and transaction latency, and is focused on known failure modes. Very little has changed with the rise of observability — the same concepts apply with modest efforts to meet the definition of observability. The fundamental focus of observability is finding insights into your data, centered around the idea of unknowns, which enable the discovery of unknown failure modes. Current observability platforms struggle to deliver the definition of observability and instead are primarily traditional APM with a better UI, that dabble in observability.

Observability is supposed to provide teams with insight into known failure states, but in practice, the ability to provide true insight is limited. Observability has become more about the upsell than delivering actual value.
Ed Bailey
Field CISO, Cribl

Costly Data Volumes

Many vendors claim to offer "observability" but still force customers into the same costly tradeoffs — sampling traces, limiting log retention, or splitting data across multiple tools. Whether you call it APM or observability is irrelevant if the platform can't actually handle modern data volumes economically.
Rakesh Gupta
Head of Product Management, Observe

Go to: APM and Observability: Cutting Through the Confusion - Part 6, covering the differing use cases of APM and Observability.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

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 ...