APM and Observability: Cutting Through the Confusion — Part 6

Pete Goldin
APMdigest

APM and Observability share a core use case: keeping applications running reliably and reducing mean time to resolution when issues occur, according to Rakesh Gupta, Head of Product Management at Observe.

Start with: APM and Observability - Cutting Through the Confusion - Part 5

Despite this similarity, however, the experts say that APM and Observability serve fundamentally different use cases. Some of this was covered in earlier parts of this series, but the experts delve deeper into the differences of use cases here:

Routine Health Checks vs. Deep Diagnostics

APM and Observability cater to fundamentally different, though related, use cases. APM is typically used for monitoring known application performance indicators, tracking service level objectives (SLOs), and quickly diagnosing common issues within predefined dashboards and workflows. Observability, conversely, shines when dealing with novelty and complexity, such as investigating system-wide issues, debugging unpredictable problems in distributed environments, and exploring hypotheses about system behavior that weren't anticipated during design or initial monitoring setup. One helps with routine health checks, the other with deep diagnostics of unfamiliar ailments.
Juraci Paixão Kröhling
Software Engineer, OllyGarden

Performance vs. the Big Picture

APM and observability have different use cases. APM deals with aspects such as tracking predefined metrics, alerting on thresholds, and providing dashboards and diagnostics that give a prescriptive look into application health. These tools should be utilized in cases where the end goal is helping software developers, testers, and quality assurance professionals quickly identify and resolve performance issues. They are also extremely useful in a production monitoring context when the application's behaviors are well understood, allowing you to monitor for signals that indicate potential problems that could lead to system degradation or failure.

On the other hand, observability is all about inferring the state of the application — a classic "we don't know what we don't know" scenario. Rather than honing in on just application performance itself, observability is focused on the larger challenge of understanding complete systems. These tools should be used to understand and troubleshoot particularly complex or unknown system behavior. As applications become increasingly AI-driven and/or AI-augmented, the discipline of observability will take a larger role.
Bryan Cole
Director of Customer Engineering, Tricentis

Knowns vs. Unknowns

APM and observability serve different, though often complementary, purposes. APM is typically focused on monitoring application health, tracking performance metrics, and ensuring adherence to service-level objectives. It's particularly effective for identifying and resolving common issues like slow response times or elevated error rates. Observability, by contrast, is designed for more complex environments — think distributed systems, microservices, and dynamic cloud-native architectures. It gives teams the ability to dig into system-wide anomalies, troubleshoot elusive or intermittent problems, and gain a deeper understanding of system behavior by correlating metrics, logs, and traces. In that sense, APM handles the known and expected, while observability equips teams to explore the unknown.
Arun Balachandran
Senior Product Marketing Manager, ManageEngine APM Solutions

APM is often used to track application health, SLAs, and response times. Observability supports broader use cases such as release validation, performance optimization across distributed systems, incident prevention, and cross-team collaboration. In short, observability is about exploring the unknowns, while APM focuses on managing the knowns.
Andreas Grabner
Fellow DevRel and CNCF Ambassador, Dynatrace

External vs. Internal

Modern, robust APM tools can test everything from individual database queries to API calls and beyond. However, the focus is on how those elements are experienced from an external point of view, rather than how it works from inside the application (or website, or whatever) itself. On the other side of the fence, if your decision was to address the imaginary issue with a solution that was observability-centric, it would indicate that your first concern was from an inside-the-code perspective; and that you were worried not so much about predictable ways the application (or website, or whatever) could fail, but rather on all the unpredictable things that might happen down the road. The so-called "black swan" events.
Leon Adato
Principal Technology Advocate, Catchpoint

Reactive vs. Proactive

They are related in that both are concerned with ensuring that infrastructure/applications are available and performing as expected, but there are two key differences: APM systems are typically reactive, based on predefined thresholds, and APM has specific capabilities around ensuring application availability and business process KPIs. Observability tools are focused on overall infrastructure health and are proactive, detecting anomalies and assisting with triage across the entire environment.
Paul Appleby
CEO, Virtana

Monolithic vs. Cloud

There's a lot of overlap in the use cases, but I think they shine brightest in different contexts. APM is often a fantastic tool for large monolithic web applications, e-commerce platforms, and mobile app performance. Observability is built specifically for complex cloud environments. Observability use cases extend to microservice architectures and other distributed systems, cloud-native environments, CI/CD workflows, incident response, and post-incident analysis.
Emily Nakashima
VP of Engineering, Honeycomb

VMs vs. Containers

It depends on the organization's specific situation. If an organization has only modern, containerized architectures, then observability tools alone might suffice. However, many organizations are likely still running a mix of older (VM-based) and newer (containerized) architectures. In those cases, they probably need both APM tools (for the older environments) and observability tools (for the newer ones) because the newer tools are not replacements in old circumstances.
Jeff Cobb
Global Head of Product & Design, Chronosphere

Applications vs. Network

APM is focused on applications and performance. Therefore, APM is not going to directly address, say, network reliability. While network reliability may be impacting your application performance (which it unfortunately often can and does), and an APM tool might catch networking issues as the underlying culprit, you will need a separate suite of network observability tools and highly different expertise to actually conduct packet-level analytics, or to diagnose why a network route keeps flapping, or to diagnose and mitigate other issues.
Peter Corless
Director, Product Marketing, StarTree

IT vs. Business

What's interesting is that executives increasingly see broader potential in unified observability data. Many organizations express interest in doing business analytics on their telemetry data or joining it with business metrics — which is a capability that traditional APM tools can't do well.
Rakesh Gupta
Head of Product Management, Observe

Start with: APM and Observability - Cutting Through the Confusion - Part 7, covering the roles that use APM and Observability.

Pete Goldin is Editor and Publisher of APMdigest

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APM and Observability: Cutting Through the Confusion — Part 6

Pete Goldin
APMdigest

APM and Observability share a core use case: keeping applications running reliably and reducing mean time to resolution when issues occur, according to Rakesh Gupta, Head of Product Management at Observe.

Start with: APM and Observability - Cutting Through the Confusion - Part 5

Despite this similarity, however, the experts say that APM and Observability serve fundamentally different use cases. Some of this was covered in earlier parts of this series, but the experts delve deeper into the differences of use cases here:

Routine Health Checks vs. Deep Diagnostics

APM and Observability cater to fundamentally different, though related, use cases. APM is typically used for monitoring known application performance indicators, tracking service level objectives (SLOs), and quickly diagnosing common issues within predefined dashboards and workflows. Observability, conversely, shines when dealing with novelty and complexity, such as investigating system-wide issues, debugging unpredictable problems in distributed environments, and exploring hypotheses about system behavior that weren't anticipated during design or initial monitoring setup. One helps with routine health checks, the other with deep diagnostics of unfamiliar ailments.
Juraci Paixão Kröhling
Software Engineer, OllyGarden

Performance vs. the Big Picture

APM and observability have different use cases. APM deals with aspects such as tracking predefined metrics, alerting on thresholds, and providing dashboards and diagnostics that give a prescriptive look into application health. These tools should be utilized in cases where the end goal is helping software developers, testers, and quality assurance professionals quickly identify and resolve performance issues. They are also extremely useful in a production monitoring context when the application's behaviors are well understood, allowing you to monitor for signals that indicate potential problems that could lead to system degradation or failure.

On the other hand, observability is all about inferring the state of the application — a classic "we don't know what we don't know" scenario. Rather than honing in on just application performance itself, observability is focused on the larger challenge of understanding complete systems. These tools should be used to understand and troubleshoot particularly complex or unknown system behavior. As applications become increasingly AI-driven and/or AI-augmented, the discipline of observability will take a larger role.
Bryan Cole
Director of Customer Engineering, Tricentis

Knowns vs. Unknowns

APM and observability serve different, though often complementary, purposes. APM is typically focused on monitoring application health, tracking performance metrics, and ensuring adherence to service-level objectives. It's particularly effective for identifying and resolving common issues like slow response times or elevated error rates. Observability, by contrast, is designed for more complex environments — think distributed systems, microservices, and dynamic cloud-native architectures. It gives teams the ability to dig into system-wide anomalies, troubleshoot elusive or intermittent problems, and gain a deeper understanding of system behavior by correlating metrics, logs, and traces. In that sense, APM handles the known and expected, while observability equips teams to explore the unknown.
Arun Balachandran
Senior Product Marketing Manager, ManageEngine APM Solutions

APM is often used to track application health, SLAs, and response times. Observability supports broader use cases such as release validation, performance optimization across distributed systems, incident prevention, and cross-team collaboration. In short, observability is about exploring the unknowns, while APM focuses on managing the knowns.
Andreas Grabner
Fellow DevRel and CNCF Ambassador, Dynatrace

External vs. Internal

Modern, robust APM tools can test everything from individual database queries to API calls and beyond. However, the focus is on how those elements are experienced from an external point of view, rather than how it works from inside the application (or website, or whatever) itself. On the other side of the fence, if your decision was to address the imaginary issue with a solution that was observability-centric, it would indicate that your first concern was from an inside-the-code perspective; and that you were worried not so much about predictable ways the application (or website, or whatever) could fail, but rather on all the unpredictable things that might happen down the road. The so-called "black swan" events.
Leon Adato
Principal Technology Advocate, Catchpoint

Reactive vs. Proactive

They are related in that both are concerned with ensuring that infrastructure/applications are available and performing as expected, but there are two key differences: APM systems are typically reactive, based on predefined thresholds, and APM has specific capabilities around ensuring application availability and business process KPIs. Observability tools are focused on overall infrastructure health and are proactive, detecting anomalies and assisting with triage across the entire environment.
Paul Appleby
CEO, Virtana

Monolithic vs. Cloud

There's a lot of overlap in the use cases, but I think they shine brightest in different contexts. APM is often a fantastic tool for large monolithic web applications, e-commerce platforms, and mobile app performance. Observability is built specifically for complex cloud environments. Observability use cases extend to microservice architectures and other distributed systems, cloud-native environments, CI/CD workflows, incident response, and post-incident analysis.
Emily Nakashima
VP of Engineering, Honeycomb

VMs vs. Containers

It depends on the organization's specific situation. If an organization has only modern, containerized architectures, then observability tools alone might suffice. However, many organizations are likely still running a mix of older (VM-based) and newer (containerized) architectures. In those cases, they probably need both APM tools (for the older environments) and observability tools (for the newer ones) because the newer tools are not replacements in old circumstances.
Jeff Cobb
Global Head of Product & Design, Chronosphere

Applications vs. Network

APM is focused on applications and performance. Therefore, APM is not going to directly address, say, network reliability. While network reliability may be impacting your application performance (which it unfortunately often can and does), and an APM tool might catch networking issues as the underlying culprit, you will need a separate suite of network observability tools and highly different expertise to actually conduct packet-level analytics, or to diagnose why a network route keeps flapping, or to diagnose and mitigate other issues.
Peter Corless
Director, Product Marketing, StarTree

IT vs. Business

What's interesting is that executives increasingly see broader potential in unified observability data. Many organizations express interest in doing business analytics on their telemetry data or joining it with business metrics — which is a capability that traditional APM tools can't do well.
Rakesh Gupta
Head of Product Management, Observe

Start with: APM and Observability - Cutting Through the Confusion - Part 7, covering the roles that use APM and Observability.

Pete Goldin is Editor and Publisher of APMdigest

Related Links

Hot Topics

The Latest

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...