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How are observability and monitoring different? Clear the confusion with this complete comparison — plus the tools to help you get started.
The debate between observability and monitoring continues to cause confusion, with many often wondering, “Is there a difference between the two?”
Some see observability and monitoring as the same thing — believing that “observability” only became a buzzword after DevOps coined it as a fancy term for cloud monitoring. Others continue to insist that observability is much more.
The debate has mostly developed due to the exceedingly complex nature of today’s IT environments. Organizations are now distributing their functions and applications across multiple layers of vast remote infrastructures, which are often distributed in the form of cloud microservices and containers.
To clear the confusion, in this guide, we’ll cover exactly what both observability and monitoring are — and how they differ. Going a step further, we’ll also look at the top tools in the space to help you get started in your observability and monitoring efforts.
Table Of Contents
While observability and monitoring might feel like synonyms, they actually refer to different actions when it comes to system maintenance.
Observability describes the extent to which a system’s internal states can be defined based on just its external outputs. The process itself entails reviewing the health of your system without interfering or interacting with it — you, instead, draw conclusions after analyzing the output data coming from the system.
More specifically, DevOps and IT teams measure observability using outputs such as traces, metrics, and logs. By assessing their properties and patterns, the administrators get to determine how well various system components are performing.
An observable system, therefore, allows you to draw quick inferences from patterns and properties that are not necessarily pre-defined. What’s more, you don’t have to dive deep into all the parameters affecting your operations — once you gather insights from the output factors, you can go ahead and act on them:
Examples of instances where observability comes in handy include:
Going by these examples, it’s evident that observability is a pretty straightforward measure. You just need to take note of the output changes and sequences to draw informed conclusions.
Please note, however, that while observability can indeed be analyzed manually, you might want to invest in automated tools for the sake of accuracy. And as it turns out, there’s a wide range of observability solutions that can be integrated into your cloud platform to save you all the trouble.
A good example is Dashbird.io, which is built to offer end-to-end observability for serverless applications running on the AWS cloud.
Organizations use it to not only keep tabs on their cloud-native applications in real-time, but also raise alerts, as well as generate system observability insights in the form of visualizations.
Make no mistake about observability vs. monitoring. Neither of them is intended to replace the other. So, you shouldn’t really view monitoring vs. observability as a contest between two opposing sides.
Observability and monitoring happen to form a sort of symbiotic relationship. That means that the variations you observe between them are the same ones that make observability and monitoring complement each other.
And how does that work?
Observability is all about the ability to draw inferences from output data that is not defined beforehand. Monitoring, on the other hand, complements that by facilitating the collection and display of predefined sets of data.
Put simply, it’s the process of monitoring that collates and relays the actual system metrics after observability explores the patterns from output parameters.
For instance, while observability would give you an indication of when your cloud resources might max out, monitoring goes much deeper to track your applications’ memory and CPU usage using preset metrics. Other commonly tracked parameters include cloud costs, number of users, traffic volume, usage period, etc.
The overall goal of monitoring is to keep you consistently updated on all the critical usage metrics. What you actually choose to track depends on your system priorities and the capabilities of your monitoring tools.
But, the process doesn’t stop there. Once monitoring tools gather the raw data from your cloud platform, they pass it on to their AI engines, which then use sophisticated analytics to transform the data metrics into actionable insights.
This last stage is where you get meaningful information that you can use to really understand your system at a much deeper level, and subsequently, determine the appropriate cloud optimizations to implement.
As the concept of observability and monitoring developed and gained traction, so did the service providers and their solutions. Consequently, the cloud now offers a wide range of advanced tools for observability and monitoring.
Some of them come as built-in tools that run natively within the cloud platforms, while others are developed by third-party providers to be integrated with various cloud services.
Whichever category you decide to sample, you’ll find that the options across both fronts are offered in three variants — you can choose between dedicated observability tools, dedicated monitoring tools, and versatile all-inclusive solutions that serve as both monitoring and observability tools.
Here are some of the top tools in the observability and monitoring space:
Distributed in both freemium and paid versions, Lightstep is an observability platform built specifically for DevOps. You can use it to gain visibility into complex deployments that involve the cloud, runtime, applications, multi-layered infrastructure, and third-party services.
Once you deploy it across your system, Lightstep’s extensive toolset proceeds to automatically detect infrastructural changes, review redundancies, establish sources of performance problems, and facilitate quick resolution of the issues.
If you like the flexibility that open-source offers, you might want to try out this three-in-one software suite. ELK is a highly scalable stack that comprises three open-source tools — Elasticsearch, Logstash, and Kibana.
You can count on it to dive deep into your applications to retrieve logs — after which it proceeds to scrutinize and assess the logs, before finally relaying the insights through visualizations to facilitate troubleshooting.
Going by AWS’s track record in the cloud computing industry, you can already tell that Amazon CloudWatch is not your average solution. The tool itself is robust enough to offer both observability and monitoring functionalities on a single platform.
The unified dashboard here provides all-round visibility, with actionable data insights for applications, infrastructure, AWS hybrid, plus on-premise systems. You can monitor your entire architecture stack, or perhaps generate metrics and insights into not only your containers but also the accompanying containerized microservices.
A decent alternative to Amazon CloudWatch would be Google Cloud’s operations suite, which formerly went by the name Google Stackdriver. As you’ve probably figured already, the suite runs entirely on the Google Cloud environment, where you get to leverage its tools for observing, monitoring, troubleshooting, and improving application performance.
You’ll be able to extract log data and analyze it all for the sake of application troubleshooting. As for the cloud-powered applications, Google Cloud’s operations suite collects metrics and prepares intuitive visualizations on performance indicators, uptime, plus the applications’ overall health.
Although SolarWinds AppOptics prefers to sell itself as an Application Performance Monitoring Solution, it comes with all the standard bells and whistles you’d expect in an observability and monitoring solution. You’ll notice, for instance, that it combines full-stack application monitoring with infrastructure monitoring.
The application side focuses on tracking exceptions, profiling live codes, and helping you identify, as well as troubleshoot bottlenecks. Then the corresponding infrastructure monitoring engine stretches to cover all possible architectures. You can use it to generate and analyze metrics for cloud, on-premise, and hybrid infrastructures.
The cloud cost intelligence platform itself is a cost monitoring solution that enables organizations to measure and monitor cost metrics that matter most to their business — like SaaS COGS, unit cost, cost per customer, feature, product, dev team, and more.
Engineers can see the cost impact of their work and finance teams can financially answer critical questions around costs. Teams can even drill into cost data from a high level down to the individual components that drive their cloud spend — and see exactly how services drive their costs and why.
Request a demo today to see what CloudZero’s cost intelligence platform can help your organization monitor, measure, and control cloud spend.