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OBSERVABILITY VS MONITORING

The Guide to Modern Observability and Monitoring

Modern observability builds on monitoring by connecting application services, infrastructure, networks, and digital experience across the full transaction path. Learn how microservices and DevOps reshaped today’s operational model.

5–7 minutes
Denton Chikura

CHAPTERS

    NEWSLETTER

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    The quick download:

    Observability emerged as a direct response to the complexity introduced by microservices, DevOps, and distributed architectures.

    • Modern applications comprise hundreds of containers running dozens of microservices, making single-server CPU and memory metrics insufficient for detecting service degradation on their own.

    • A complete observability strategy must cover three layers: the end-user’s digital experience, the public networks transactions traverse, and the application infrastructure in private or public clouds.

    • The real shift is toward bringing alerting signals together with the debugging signals you use to trace root cause across metrics, logs, and traces.

    • Start by mapping your full transaction path from user to code, then layer in monitoring and observability capabilities that cover each segment.

    The Guide to Modern Observability and Monitoring 

    This guide explains the various disciplines that define the practice of observability and tracks how it expanded the scope of monitoring over the last decade. What started as a need to monitor a server’s computing resources, like CPU and memory utilization as part of a client-server architecture, gradually transformed into observing digital experiences and isolating the root cause of application slowdowns with the help of distributed tracing and log analysis across infrastructure, applications, networks, and the end-user experience. 

    As form follows function, monitoring follows application architecture, so for this guide to have context, we must start by reviewing recent changes to application architecture. 

    Application architecture based on microservices 

    We now refer to client-server applications as “monolithic” applications and, with hindsight, recognize their limitations: they create a single point of failure by relying on centralized hardware and lack the ability to adapt efficiently to a changing workload. And so, application architects broke the monolithic application into components (known as microservices) that communicate via an application programming interface (API) abstraction layer and invited anyone with a web browser to access the application over the internet through a load balancer that distributes traffic across multiple nodes. This distributed architecture helps in many ways:

    • A development team dedicated to a microservice can work independently.  
    • Each microservice can scale on its own by adding more computing resources. 
    • The application can operate without having a single point of failure.
    • The application utilizes compute resources more efficiently

    The diagram above simplifies the concept of microservices, but the reality is more complex. An application typically comprises dozens of microservices, each replicated across dozens of containers (a container is a lightweight virtual machine), forming an environment of hundreds of containers that are constantly updating and communicating. This dynamic environment requires a new operational model. This dynamic environment requires a new operational model and a broader view of performance that extends beyond any single component.

    The rise of DevOps

    The operational processes have also adapted to the changes in application architecture. Independent development teams can now release code more frequently (as many as dozens of times a day) and roll back mistakes almost instantaneously, blurring the lines between operations and development and resulting in a closer daily collaboration popularized by the term DevOps. 

    Immutable architecture

    As more lightweight containers replaced heavier servers and virtual machines, it has become simpler to replace containers than reconfigure them to keep up with the changing needs of the application. This concept is known as immutable architecture, visually explained below. These changes have enabled operations teams to configure infrastructure the way developers used to configure code, giving rise to a new paradigm known as Infrastructure as Code (IaC), which we introduce next.

    Infrastructure as code (IaC)

    In the early days of infrastructure management, operations engineers logged in to machines and manually updated configuration files whenever the infrastructure required a change (a model now referred to as a mutable architecture). The second generation of configuration management tools (such as Chef and Puppet) relied on local software agents to execute centrally issued changes simultaneously across dozens of servers, reducing manual labor. The latest approach to updating infrastructure (promoted by Terraform) simply describes the desired state via declarations and relies on specialized orchestration software (such as Kubernetes) to achieve it.

    Continuous integration and continuous delivery (CI/CD)

    Once teams can independently release new code into a production environment and update it, they need a pipeline to automate the process to save time. The code delivery pipeline must compile the code and its dependencies, run tests on the new code before deployment, and trigger third-party processes (like updating the configuration of monitoring and observability tools). This pipeline is commonly referred to as Continuous Integration and Continuous Delivery (CI/CD) and is visually summarized below. A popular open-source project in this category is Jenkins.

    Observability 

    The new processes and tools spawned after the break up of monolithic applications into microservices expanded the practice of monitoring. Infrastructure monitoring remains foundational, but modern teams now layer on application-level observability to connect end-user experience back to the underlying infrastructure, network, and cloud components. Along the way, “observability” became a standard part of the DevOps vocabulary.  

    Services vs. infrastructure 

    CPU and memory measurements can reveal problems when a monolithic application is hosted on a single server, but those measurements are no longer sufficient on their own to characterize the digital experience of an application that relies on microservices spanning hundreds of short-lived containers. 

    The dynamic complexity of modern application environments forces operators to observe services from the outside rather than relying solely on monitoring each component from the inside. Monitoring application infrastructure is still necessary to isolate problems once application service degradation is detected, but what ultimately matters is the end-user digital experience and the ability to connect that experience back to the infrastructure, network, cloud, and application signals that explain it.

    Transaction path

    A complete observability strategy must cover the entire path of a digital transaction, which includes:

    • The digital experience of the end-users from their desktops or mobile devices.
    • The public networks that the transactions traverse to reach the application.
    • The application infrastructure hosted in a private or public cloud.

    When applications were architected around the client-server model, end users resided on the same local area network as the application servers. Today’s users freely roam the globe and access application services across various device types, including mobile devices, underscoring the importance of monitoring the digital experience and shared public networks, as well as the hybrid infrastructure that supports them. 

    Troubleshooting in the age of DevOps

    Monitoring the application infrastructure hasn’t become redundant. It is, in fact, more sophisticated than ever, thanks to new technologies that let operators isolate a single error in terabytes of distributed log files, which modern tools aggregate and index. New infrastructure monitoring tools store unprecedented sub-second transaction data in scalable databases that display granular information on demand. However, what has changed is the separation between the “alarming” signals that alert operators to a service problem and the “debugging” signals that DevOps engineers use to isolate the root cause of a performance issue and fix it with more automated correlation and less tool-switching.

    Alerting vs. Debugging Signals

    DevOps engineers rely on application service metrics to alert them to application slowdowns or outages. However, as soon as an application problem arises, all focus shifts to isolating the root cause in the infrastructure, whether manually or automatically, using orchestration and artificial intelligence. A typical sequence of events entails:

    1. Digital experience observers detect a problem and notify the operators
    2. Operators identify or rule out the shared public networks as the root cause
    3. Transaction tracing helps operators isolate the microservice impacted first
    4. Real-time metrics and indexed log files help engineers isolate the root cause 

    In practice, observability and monitoring work together. Service-level and digital experience signals surface legitimate problems across the end-user, Internet path, and application layers. Operators then rely on infrastructure monitoring tools to isolate the root cause using transaction tracing, performance metrics, and system logs.

    Go beyond basic monitoring to full‑path observability.

    Unify digital experience, network, and infrastructure signals so you can spot issues sooner and pinpoint the service causing them.

    FAQs

    What’s the difference between observability and monitoring?

    Monitoring tracks predefined metrics on known infrastructure components to detect problems. Modern observability builds on that foundation by examining application services from the outside in, using traces, metrics, and logs to understand why problems occur across distributed systems. The two work together: monitoring surfaces component-level health, while observability connects those signals to end-user experience and service-level impact.

    Why did observability become necessary?

    It covers three layers: the digital experience of end-users on their devices, the public networks transactions traverse, and the application infrastructure running in private or public clouds. All three must be monitored to detect and isolate issues effectively.

    What does a complete observability strategy cover?

    It covers three layers: the digital experience of end-users on their devices, the public networks transactions traverse, and the application infrastructure running in private or public clouds. All three must be monitored to detect and isolate issues effectively.

    How do alerting and debugging signals work together?

    Alerting signals (from digital experience and service-level metrics) notify operators that a problem exists. Debugging signals (from distributed traces, real-time metrics, and indexed logs) help engineers isolate the specific microservice or component causing the issue.

    By Denton Chikura

    Technical Writer

    Denton Chikura is a technical writer and longtime observability advocate focused on helping site reliability engineers and engineering teams discover the tools and capabilities that strengthen internet resilience. He works at the intersection of monitoring, performance, and infrastructure to make complex systems more understandable and usable, bridging the gap between deep technical detail and real‑world operations. His goal is to help teams build faster, detect issues earlier, and recover smarter, ultimately making the internet a better, more reliable place for everyone.

    Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.

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