Ensuring optimal application performance is a Herculean task tee’d up for today’s IT operations teams. Adding to the confusion is the shared acronym of the two most common practices:
- Application Performance Monitoring (APM)
- Application Performance Management (APM)
While the terms are similar, the approaches and use cases are different.
Differences by Definition
Imagine an athlete preparing for a baseball game. The athlete’s training routine and performance data (ex: batting average) can be likened to Application Performance Monitoring. The athlete’s overall approach to managing their performance to achieving optimal results (ex: attending every team practice, analyzing then buying better equipment) can be likened to Application Performance Management.
Application Performance Monitoring: Analyzing Application Infrastructure
Application Performance Monitoring refers to the granular understanding of the products providing a detailed analysis of the performance, optimization, and reliability of an application’s infrastructure and components. Closely monitoring the functionality of each step and transaction of the application stack makes it easier for organizations to debug and improve the application. In the event of an application crash or failure, data provided by Application Performance Monitoring allows ITOps teams to quickly pinpoint the source and resolve the issue.
Application Performance Management: A Holistic Approach to Software Environment
Application Performance Management refers to the broader view into how an application is using resources and how that allotment influences the user experience. (We discussed why it’s important to have a Digital Experience Monitoring (DEM)-enabled APM in this article). This user-centric focus helps ITOps teams determine which application performance enhancements to prioritize to enhance the overall performance of their applications.
Three Key Differences Between APM v APM
| Functionality/Feature | Application Performance Monitoring | Application Performance Management |
| Scope of Problem Analysis | Code-level: Focus on code-level problems within a specific application. Focuses on monitoring individual steps. May lack scalability for enterprise-wide application monitoring. | Broad: Focuses on individual steps from an end-user perspective. Offers insights into which applications require optimization then helps with those efforts. May be less effective for managing performance across a large number of applications simultaneously. |
| Data Collection | Collects time-oriented data, analyzing each step in a sequential manner. Beneficial for debugging code-level errors and identifying application-specific issues. | Collects a broad range of data with emphasis on user interaction with the system. Beneficial insights (ex: memory usage and CPU consumption) help identify root causes impacting end-users. |
| Performance Criteria Considerations | More focused on the performance of individual applications. Example: criteria such as time thresholds to determine if the application meets end goal requirements. | More focused on real-user monitoring, directly correlating with the end-user experience. Example: Analyzes overall user experience and resource utilization for specific applications to enhance the end-user experience. |
Use Cases: Application Performance Monitoring
Organizations can leverage APM to gain data-based visibility into the sources of bottlenecks, latency issues, and resource constraints within the infrastructure. APM’s data on response time, CPU usage, memory consumption, and network latency help pinpoint the root causes of application performance degradation.
Here are some other use cases for Application Performance Monitoring:
Proactive Issue Detection uses APM to set up thresholds and alerts for key performance indicators such as slowing response times, spiking error rates, and other anomalies which can produce a negative digital user experience.
Capacity Planning uses APM to focus on CPU usage, memory use, and disk I/O of applications. This data shows where infrastructure resources need to scale or be redistributed to prevent performance issues.
User Experience Monitoring tracks user interactions, session durations, and conversion rates to identify areas where improvements to the infrastructure can enhance the user experience.
Code-Level Performance Analysis uses APM to profile code execution. This data empowers developers with the information needed to identify and diagnose performance bottlenecks (i.e. slower response times or high resource usage) within the application code.
Service Level Agreements (SLA) Compliance and Reporting tracks and alerts anomalies in uptime, response time, and error rates. This level of monitoring helps teams stay in compliance with identified SLA targets. APM is also used to produce compliance reports for stakeholders.
When organizations leverage APM, they gain deep visibility into their application infrastructure, enabling proactive monitoring, real-time diagnostics, and ultimately drive business success.
Use Cases: Application Performance Management
Organizations use APM to know what is going on with resource consumption at the hardware, network, and software levels. This data helps ITOps teams improve resource allocation which helps reduce costs, improve scalability, and enhance overall performance.
Here are some other use cases for Application Performance Management:
Business Transaction Analysis organizations use APM to monitor and analyze the end-to-end journey of a business transaction within the application. APM gives insight into the different transactions’ interactions with components and systems to help ITOps teams identify any sources of performance bottlenecks.
Root Cause Analysis of performance issues or failures within an application environment is correlated through data from different monitoring sources, such as logs, metrics, and traces. When the exact source of the problem is found, troubleshooting and resolution happens faster, and downtime is reduced or avoided.
Compliance and Regulatory Requirements for application performance are more easily met when APM is monitoring and documenting them. Organizations can rely on APM to fill the critical role of providing an audit trail and documentation of their adherence to industry standards and regulations.
SLA Management with APM allows organizations to monitor, measure and report on agreed-upon key performance metrics and levels against predefined SLA targets. This data is then used for SLA reporting and compliance.
While use cases vary between the APMs, there is some common ground: both techniques help an application fully function. Where Application Performance Monitoring is focused on the detailed data feedback used to proactively monitor performance, Application Performance Management is more about an organization’s prioritization of people and process. Both are needed to promote a holistic application performance strategy which promotes a positive user and developer experience.