The odds are increasing that if you work in IT, you, or someone close to you, has been talking about “observability” recently. And, simultaneously, the conversation likely includes some comparison or question about the difference between observability and monitoring.

So, let’s try to clear up some of this confusion. 

Over the last several years, the charge into observability has spawned largely from developer and DevOps communities that operate primarily in cloud-centric or cloud-native environments. It has been a conversation born of necessity as developers and Site Reliability Engineers (SREs) have sought to create and improve the tools needed to ensure the delivery of robust and secure cloud applications. Developers and SREs from some of the largest, most recognizable cloud software companies have contributed to open source tools and, in some cases, even launched their own software companies around those tools. Martin Mao of Chronosphere and Charity Majors, along with her team, at Honeycomb, spring to mind.

The confusion comes from the overlapping nature of legacy application delivery compared to modern cloud applications and containerized solutions. The historical best practices in the legacy application world leveraged monitoring software and techniques for both the infrastructure used to deliver applications and to monitor the health and availability of the applications themselves.

Another legacy technique that deserves mention here is classical application debugging techniques vs. cloud application debugging techniques. What used to be done via physical serial-based debugging with symbols, etc., for operating systems, or via a small number of application log files for debugging legacy applications, has shifted to cloud-scale complexity featuring distributed application delivery. This has triggered a wave of more powerful solutions designed to ingest, process, and analyze massive volumes of session logs and trace data.

When thrust into the context of most enterprises today, the result is our modern hybrid cloud world and the underlying blend of old and new tools and techniques used to keep everything running smoothly.

So, observability is the language used by those who are predominantly supporting cloud applications, while monitoring persists as the familiar language to those supporting on-premise, legacy, and hybrid environments.

And what most are discovering is that a mature organization uses monitoring and observability, combined with effective incident management, to glue it all together.

So, the question should be, “how do we bring effective monitoring and observability tools and techniques together in our organization?” and not merely, “what is the difference between monitoring and observability?”.

The best solutions combine best-of-breed tools that deliver end-to-end visibility, automated correlation, and actionable insights that keep things running smoothly and support meaningful data analytics. The data from monitoring and observability tools should allow for effective forecasting, planning, and budgeting, going beyond the basic aspects of security, availability, and performance.

At Loop1, that is precisely what we do. With our monitoring maturity model, L1M3*, we combine people, tools, and processes to deliver better business outcomes.

*L1M3 Loop1 Monitoring Maturity Model (LIME)

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