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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

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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

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

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My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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