Application performance monitoring tools focus exclusively on monitoring and represent only one aspect of APM.

Traditional monitoring solutions rely on small software components called “agents,” which are deployed throughout the application environment and supporting infrastructure to sample performance and performance-related metrics (or telemetry) at regular intervals (as frequently as once every minute). More modern solutions use agentless monitoring for a non-intrusive approach to data collection, relying on network traffic analysis to gather app performance data.

Application performance management is, in many ways, the next natural step in the app maintenance lifecycle (after monitoring). App performance management systems glean insights from application performance data and monitoring processes to help developers optimize the performance and availability of enterprise applications.

APM solutions provide one-size-fits-all tools for capturing rich data and analytics from services within an application. They make application architecture observable. And while the APM approach used to be sufficient for high-quality application management, it’s ill-equipped for the job of managing distributed apps and services with multiple runtimes and multiple layers.

Today’s applications rely on services and microservices, often running in containerized Kubernetes clusters. That means multiple runtimes with each runtime outputting logs in different locations within the architecture. To accommodate multiple runtimes using APM, developers would need to deploy multiple APM tools. They’d also need to use a log streaming service or another aggregation tool to consolidate logging data from each location. 

And as businesses add more services and microservices to the architecture, they introduce more complexity, making it harder to track requests when something goes wrong.

Observability solutions surpass APM tools by taking a holistic, cloud-native approach to application logging and monitoring. They provide seamless process automation and work with historical contextual data to help teams better optimize enterprise applications.

With observability tools, teams can better understand how services work with each other (using dependency graphs, for instance) and fit into the overall architecture. They can also use observability solutions to aggregate and vet app data and receive actionable insights from that data.

Today’s APM tools are versatile, with a range of customizable features that help businesses implement tailored APM strategies. Each feature can help IT teams get full-stack observability into their application ecosystems. Some examples include:

• Application monitoring: APM tools monitor the entire application stack, including the app framework (Java or .NET, for instance), operating system and database, any application programming interfaces (APIs) or middleware and the web application server.

• Error tracking: APM solutions can cluster related app errors into groups of issues, providing teams more context for understanding system errors and maximizing efficiency in the troubleshooting process.

• Infrastructure monitoring: With APM infrastructure monitoring protocols, teams can gather data on CPU utilization, disk space and network performance.

• Distributed tracing: APM tools enable developers to track data requests as they traverse the network, from front-end to backend services, helping teams monitor per-request dependences and isolate specific errors.

• Database monitoring: APM platforms help businesses visualize host-level and query performance metrics (such as top users, programs and objects), and assess SQL procedures to determine how database design and resource saturation affect app performance.

• Code profiling: Using APM tools, teams can take code performance snapshots to discover which app components are moving too slowly or using too many resources.

• Digital experience monitoring: APM tools can implement real user monitoring (RUM) and synthetic monitoring to gather performance data (load time, response time, latency, uptime and downtime) from user devices, bots and other non-human software components.

• Internet of Things (IoT) monitoring: Because users typically access apps using mobile devices, troubleshooting app issues can become a complex, time-consuming process. IoT monitoring capabilities within APM solutions provide teams a unified view of applications across connected devices.

Like many other IT operations (ITOps) practices and solutions, APM tools have changed significantly with the proliferation of artificial intelligence (AI) and the evolution of cloud computing.

The periodic sampling associated with traditional APM tools was sufficient for managing monolithic apps and traditional, distributed applications (where new code is released periodically and workflows, dependencies, servers and related resources are well-known or easy to trace).

But today, as businesses adopt modern application development practices and cloud-native technologies (such as Agile and DevOps methodologies, microservices, Docker containers, Kubernetes and serverless functions), they often deploy new app components too frequently, across too many languages and locations, to rely on traditional monitoring strategies. 

Furthermore, traditional APM techniques monitor code execution to diagnose issues. But today’s cloud-based SaaS applications comprise millions of lines of code, often spread across containers.

That’s why leading APM tools deploy cutting-edge monitoring instruments that enable full-stack observability, and rely on AI and machine learning (ML) technologies to correlate and analyze data in real time.

AI-driven APM tools can work across complex, distributed IT environments, deploying AI algorithms that can quickly analyze large volumes of performance data, correlate performance data with contextual data and pinpoint the root cause of performance problems.

Modern APM systems also use ML models to generate predictive analytics and forecast performance trends. And with natural language processing (NLP) capabilities, APM software can methodically sift through performance data and provide teams plain-language insights.

AI technologies aren’t without their challenges; explainability, privacy and data security are common concerns with AI-based IT tools. However, AI-driven APM software can significantly accelerate monitoring and troubleshooting and help enterprises make smarter, more proactive decisions about their application portfolios.

APM tools can help facilitate:

• Enhanced user experience. APM tools help businesses maintain high levels of user satisfaction and engagement, both of which are vital to customer retention and reputation management.
  
  
• Faster MTTD and MTTR. APM enables teams to find and fix performance issues and bottlenecks quickly, accelerating mean time to detect (how long it takes an organization to find an issue) and mean time to repair (how long it takes an organization to fix an issue).
  
  
• Better resource utilization. Monitoring and analyzing performance data allows organizations to optimize resource allocation, increasing cost savings and streamlining operations.
  
  
• Increased business agility. APM supports agile methodologies by providing real-time feedback on application performance, enabling rapid iteration and continuous improvement.
  
  
• Smoother migrations. Distributed tracing and service mapping features can help ensure that cloud migrations and modernizations go smoothly and don’t introduce errors to the network.
  
  
• Better employee collaboration. With APM’s end-to-end tracing capabilities, front-end and backend teams can access the same data simultaneously, facilitating more collaborative troubleshooting and optimization processes.