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Legacy Application Performance Management (APM) vs Modern Observability - Part 1

Colin Fallwell
Sumo Logic

In this 3 part series, I will explore, contrast, and discuss the differences as well as the history of APM and the meteoric rise of Modern Observability, why these two are related but simultaneously are vastly different in outcome. Indeed, Modern Observability is disrupting the world, and organizations doing it right are realizing massive gains in innovation, reaping the benefits of higher performance and optimization across numerous dimensions including:

■ IT governance

■ Revenue growth

■ Vendor cost reduction

■ Tool Consolidation

■ DevOps toil and churn

■ Application performance and customer experiences

■ Reliability and Security

■ Employee satisfaction

■ Data Science and Business Analytics

■ AI-controlled automation (AIOps)

Modern Observability is becoming the foundation upon which organizations are able to reduce the toil and churn associated with capital spending across initiatives such as Cloud Migrations, App Modernization, Digital Transformation, and AIOps by leveraging new methodologies such as Observability-Driven-Development (ODD).

Traditional APM is a mature, vendor-led industry, and was built at a time when the world was developing monolithic, 3-tier architectures and when software was typically released once or twice a year. APM is a closed ecosystem, with patented protocols and agents which are deployed to run on every node, injected into runtimes with startup parameters, and have little to no impact on how software is designed or developed.

This is a good thing, right?

In contrast to Modern Observability, and for organizations moving to the cloud, APM is loaded with hidden costs and unintended consequences. From a process perspective, APM does not live within the developer ecosystem and has historically been funded by Ops teams or DevOps/SRE groups that have largely been out of the immediate workstream of software development. This nuance means developers have no real ownership interest in APM and don't feel compelled in taking responsibility for declaring what it means to make something "observable." What enterprises desire most are reliable pipelines of telemetry that provide accurate data inferring the internal state of systems including usage and behavioral insights of end-users, code execution, infrastructure health, and overall performance. Most developers have been poor adopters of APM.

A major characteristic of Modern Observability is in how it becomes designed into the fabric of the applications, services, and infrastructure by DevOps teams, implemented through models such as GitOps, which in turn provides numerous benefits to organizations that legacy APM really does not align to. It is within this point of view or context that I base my opinions on throughout this series. Many organizations still relying on APM vendors will struggle to increase the intrinsic value of data within the organization. It's my firm argument that the most important attribute of Modern Observability lies in its "programmable" nature, whereby the acquisition of telemetry becomes woven into the fabric of developing software and the services offered by anyone competing in this global software-driven economy.

There are many other dimensions of contrast, but I personally believe this to be the most important with respect to organizations embracing digital transformation, or for those that just want to improve maturity, growth, and innovation, or anyone wishing to own their own destiny when it comes to data intelligence.

In the next installment (Part 2) of this series, we dive into the history of APM and how it became a 6 Billion USD market and explore some of the challenges that come with APM.

Colin Fallwell is Field CTO of Sumo Logic

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 ...

Legacy Application Performance Management (APM) vs Modern Observability - Part 1

Colin Fallwell
Sumo Logic

In this 3 part series, I will explore, contrast, and discuss the differences as well as the history of APM and the meteoric rise of Modern Observability, why these two are related but simultaneously are vastly different in outcome. Indeed, Modern Observability is disrupting the world, and organizations doing it right are realizing massive gains in innovation, reaping the benefits of higher performance and optimization across numerous dimensions including:

■ IT governance

■ Revenue growth

■ Vendor cost reduction

■ Tool Consolidation

■ DevOps toil and churn

■ Application performance and customer experiences

■ Reliability and Security

■ Employee satisfaction

■ Data Science and Business Analytics

■ AI-controlled automation (AIOps)

Modern Observability is becoming the foundation upon which organizations are able to reduce the toil and churn associated with capital spending across initiatives such as Cloud Migrations, App Modernization, Digital Transformation, and AIOps by leveraging new methodologies such as Observability-Driven-Development (ODD).

Traditional APM is a mature, vendor-led industry, and was built at a time when the world was developing monolithic, 3-tier architectures and when software was typically released once or twice a year. APM is a closed ecosystem, with patented protocols and agents which are deployed to run on every node, injected into runtimes with startup parameters, and have little to no impact on how software is designed or developed.

This is a good thing, right?

In contrast to Modern Observability, and for organizations moving to the cloud, APM is loaded with hidden costs and unintended consequences. From a process perspective, APM does not live within the developer ecosystem and has historically been funded by Ops teams or DevOps/SRE groups that have largely been out of the immediate workstream of software development. This nuance means developers have no real ownership interest in APM and don't feel compelled in taking responsibility for declaring what it means to make something "observable." What enterprises desire most are reliable pipelines of telemetry that provide accurate data inferring the internal state of systems including usage and behavioral insights of end-users, code execution, infrastructure health, and overall performance. Most developers have been poor adopters of APM.

A major characteristic of Modern Observability is in how it becomes designed into the fabric of the applications, services, and infrastructure by DevOps teams, implemented through models such as GitOps, which in turn provides numerous benefits to organizations that legacy APM really does not align to. It is within this point of view or context that I base my opinions on throughout this series. Many organizations still relying on APM vendors will struggle to increase the intrinsic value of data within the organization. It's my firm argument that the most important attribute of Modern Observability lies in its "programmable" nature, whereby the acquisition of telemetry becomes woven into the fabric of developing software and the services offered by anyone competing in this global software-driven economy.

There are many other dimensions of contrast, but I personally believe this to be the most important with respect to organizations embracing digital transformation, or for those that just want to improve maturity, growth, and innovation, or anyone wishing to own their own destiny when it comes to data intelligence.

In the next installment (Part 2) of this series, we dive into the history of APM and how it became a 6 Billion USD market and explore some of the challenges that come with APM.

Colin Fallwell is Field CTO of Sumo Logic

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 ...