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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...