Skip to main content

APM for Vendor Provided Business Critical Enterprise Applications

Sri Chaganty

Traditionally, Application Performance Management (APM) is usually associated with solutions that instrument application code. There are two fundamental limitations with such associations. If instrumenting the code is what APM is all about, then APM is applicable only to homegrown applications for which access to code is available.

However, the majority of business critical applications are not homegrown. As the chart below shows, the $320B enterprise software market is driven by vendors who provide solutions for which there is no access to the source code. The enterprise software market in the chart covers a full assortment of commercially off-the-shelf products ranging from corporate databases to Enterprise Resource Planning (ERP) solutions and from Cloud-enabled productivity tools to mission-critical vertical applications. However, there are technical challenges with code instrumentation that are overlooked with this traditional association.


Source: Apps Run the World, 2016

Vendor Provided Software

Traditional APM vendors focus on application software that is developed in house, mainly based on Web Services. These solutions employ Byte Code Instrumentation (BCI), a technique for adding bytecode during "run time." These solutions are developer focused. If developers want to debug or profile the code during run time, BCI is an effective solution.

In reality, enterprises depend on both in-house developed software as well as vendor provided software. Applications that businesses use can be dived into two categories: 1) Business Critical Applications and 2) Productivity Applications. While business critical applications are the foundation on which the business success is dependent upon, productivity applications like email are also equally important for enterprises.

Generally, about 80% of the applications that enterprises use in either category are packaged applications supplied by vendors like Microsoft, SAP, Oracle, PeopleSoft and others. Only 20% of the applications are developed in-house. In the majority of cases, the in-house developed applications generally wrap around vendor provided software.



A common example is an application developed based on web services customized for a business that are supported on SAP in the background. Instrumenting vendor provided software is not possible as the source code is not provided by the vendor, therefore, code instrumentation techniques are not feasible for vendor provided software.

Instrumenting in-house developed application software at different points gives a rich view to optimize the application throughout development. However, there are several types of problems that such instrumentation just can't see. It does not, and cannot, always deliver the complete visibility that users think they're getting. In addition, code instrumentation is not "free", even with the expensive tools commercially available, it takes considerable coding skills (not widely available) to achieve effective code instrumentation without degrading the performance of the production code execution.

Technical Challenges

Code instrumentation can report on the performance of your application software stack, but the service offered to customers depends on far more than just the software – it depends on all of the networks, load balancers, servers, databases, external services like Active Directory, DNS etc., service providers and third parties you use to provide the service. 

Traditional APM products do only BCI. They claim to be transaction management solutions, though there are limitations to what they can do in Java environments, and they have zero visibility of non-Java topologies.

A real transaction management product needs to follow the transaction between different types of application-related components such as proxies, Web servers, app servers (Java and non-Java), message brokers, queues, databases and so forth. In order to do that, visibility into different types of transaction-related data is required, some of which only exists at the actual payload of each request. Java is an interpreter and therefore hides parts of the actual code implementation from the Java layer. The Java Virtual Machine (JVM) itself is written in C, therefore there are operating system-specific pieces that are not accessible from Java and thus not accessible through BCI techniques.

If you want to use features of TCP/IP packets for tracing a transaction between two servers, the actual structure of packets is not accessible from the Java layer. There is information that is crucial to trace transactions across more than just Java hops. Such information is available only at a lower layer than the Java code, thus not accessible by BCI, which limits the ability to trace transactions in the real world.

Conclusion

For vendor provided software, BCI is an ineffective technique. For in-house built software, BCI allows programmers to enhance the code they are developing. It is a necessary tool for development teams but insufficient as it does not offer the visibility that IT Operations require in order to understand the application service delivery chain performance. If your business depends on mission-critical web or legacy applications, then monitoring how your end users interact with your applications is more important than how well the code is written. The responsiveness of the application determines the end user's experience. The true measurement of end-user experience is availability and response time of the application, end-to-end and hop-by-hop – covering the entire application service delivery chain.

Sri Chaganty is COO and CTO/Founder at AppEnsure.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

APM for Vendor Provided Business Critical Enterprise Applications

Sri Chaganty

Traditionally, Application Performance Management (APM) is usually associated with solutions that instrument application code. There are two fundamental limitations with such associations. If instrumenting the code is what APM is all about, then APM is applicable only to homegrown applications for which access to code is available.

However, the majority of business critical applications are not homegrown. As the chart below shows, the $320B enterprise software market is driven by vendors who provide solutions for which there is no access to the source code. The enterprise software market in the chart covers a full assortment of commercially off-the-shelf products ranging from corporate databases to Enterprise Resource Planning (ERP) solutions and from Cloud-enabled productivity tools to mission-critical vertical applications. However, there are technical challenges with code instrumentation that are overlooked with this traditional association.


Source: Apps Run the World, 2016

Vendor Provided Software

Traditional APM vendors focus on application software that is developed in house, mainly based on Web Services. These solutions employ Byte Code Instrumentation (BCI), a technique for adding bytecode during "run time." These solutions are developer focused. If developers want to debug or profile the code during run time, BCI is an effective solution.

In reality, enterprises depend on both in-house developed software as well as vendor provided software. Applications that businesses use can be dived into two categories: 1) Business Critical Applications and 2) Productivity Applications. While business critical applications are the foundation on which the business success is dependent upon, productivity applications like email are also equally important for enterprises.

Generally, about 80% of the applications that enterprises use in either category are packaged applications supplied by vendors like Microsoft, SAP, Oracle, PeopleSoft and others. Only 20% of the applications are developed in-house. In the majority of cases, the in-house developed applications generally wrap around vendor provided software.



A common example is an application developed based on web services customized for a business that are supported on SAP in the background. Instrumenting vendor provided software is not possible as the source code is not provided by the vendor, therefore, code instrumentation techniques are not feasible for vendor provided software.

Instrumenting in-house developed application software at different points gives a rich view to optimize the application throughout development. However, there are several types of problems that such instrumentation just can't see. It does not, and cannot, always deliver the complete visibility that users think they're getting. In addition, code instrumentation is not "free", even with the expensive tools commercially available, it takes considerable coding skills (not widely available) to achieve effective code instrumentation without degrading the performance of the production code execution.

Technical Challenges

Code instrumentation can report on the performance of your application software stack, but the service offered to customers depends on far more than just the software – it depends on all of the networks, load balancers, servers, databases, external services like Active Directory, DNS etc., service providers and third parties you use to provide the service. 

Traditional APM products do only BCI. They claim to be transaction management solutions, though there are limitations to what they can do in Java environments, and they have zero visibility of non-Java topologies.

A real transaction management product needs to follow the transaction between different types of application-related components such as proxies, Web servers, app servers (Java and non-Java), message brokers, queues, databases and so forth. In order to do that, visibility into different types of transaction-related data is required, some of which only exists at the actual payload of each request. Java is an interpreter and therefore hides parts of the actual code implementation from the Java layer. The Java Virtual Machine (JVM) itself is written in C, therefore there are operating system-specific pieces that are not accessible from Java and thus not accessible through BCI techniques.

If you want to use features of TCP/IP packets for tracing a transaction between two servers, the actual structure of packets is not accessible from the Java layer. There is information that is crucial to trace transactions across more than just Java hops. Such information is available only at a lower layer than the Java code, thus not accessible by BCI, which limits the ability to trace transactions in the real world.

Conclusion

For vendor provided software, BCI is an ineffective technique. For in-house built software, BCI allows programmers to enhance the code they are developing. It is a necessary tool for development teams but insufficient as it does not offer the visibility that IT Operations require in order to understand the application service delivery chain performance. If your business depends on mission-critical web or legacy applications, then monitoring how your end users interact with your applications is more important than how well the code is written. The responsiveness of the application determines the end user's experience. The true measurement of end-user experience is availability and response time of the application, end-to-end and hop-by-hop – covering the entire application service delivery chain.

Sri Chaganty is COO and CTO/Founder at AppEnsure.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.