Bring Intelligence to Your Digital Infrastructure with Observability

Observability combines your technical data with your business priorities to help IT teams drive better outcomes

Tom McLaughlin
Marco Zacchello
Bring Intelligence to Your Digital Infrastructure with Observability

Modern digital infrastructures have become highly complex compared to the IT environments of the past. Organizations now have multiple infrastructure layers—from physical hardware to software applications, code, and cloud-native services like containers and microservices. Development teams are bringing applications and services to market faster than ever, and operations teams are inundated with monitoring data from across the IT stack.

If you want Ops teams to see into and understand the state of their systems, you need a way to organize and sift through all the available data from logs, events, metrics and traces across your IT environment to get the most valuable and necessary information. Observability allows you to not only predict and remediate issues but also align your infrastructure state with your desired business outcomes.

Because of the growing need to gather more meaningful, actionable insights from IT data, observability has become a hot topic for many Ops teams today. Whether they’re implementing it to gain insight into their environment and infrastructure spend, increase their development velocity, or get their cloud costs under control, there isn’t an IT team that hasn’t considered how observability can help them meet their goals.

What is observability?

IT monitoring solutions have been around for a while, collecting and analyzing data from IT equipment to help Ops teams identify and remediate issues. With monitoring solutions, teams can watch particular metrics or set up utilization thresholds (e.g., alert me when CPU utilization on a server reaches 75%).

Observability represents a new step in the evolution of monitoring—giving context to the metrics collected on IT equipment. Technologies like automation, AI, machine learning (ML) and deep learning (DL) have made it possible for us to do much more sophisticated analysis and learning on IT data.

Observability can be defined as the combination of business intelligence with your IT infrastructure state. It allows you to attribute actions and events in your environment more specifically with the business outcomes you want to drive (e.g., determining the cost associated with serving any given workload, on a unit service basis). In other words, observability involves usefully combining business and technical data to drive the outcomes you want.

Why observability matters: Security and cost controls

As any operator knows, there are a million things on an Ops team’s mind at any given time. So, why is observability in the spotlight?

Undoubtedly, organizations are increasingly aware and proactive about cyber threats—with more and more companies facing costly attacks every day. Without a comprehensive view of the assets, users and state of your system, it’s hard, if not impossible, to maintain robust security. Customers expect that their service providers can audit their environments to ensure data security and compliance, and carry out investigations should a breach occur.

Observability also plays an important role in managing infrastructure costs—especially cloud spend. To avoid unexpectedly high infrastructure spending, you need to pay attention to what you’re spending and why. You need an approach that integrates business priorities with operational decisions—and that’s what observability is all about.

Next-gen technologies are changing the game

The rise of automation, AI and infrastructure as code has unlocked new capabilities for organizations to get real-time insight into the state and performance of the many layers of their IT systems. By taking the current state of your systems into consideration, along with the needs of your customers and business, you can optimize the performance of your networks, decrease the costs of providing services and increase the speed of feature delivery.

And observability, in turn, supports the needs of emerging technologies. Operational data is the lifeblood of automation and AI; machines (and humans) can’t react to systems and events they can’t see. Observability brings together technical operations and business intelligence to enable automation that understands the capacity needs of a system, while considering service-level objective (SLO) and service-level agreement (SLA) priorities for implementation. AI systems rely on fresh data to accurately represent the system state at any time and ensure that its automated actions have the desired effect. In turn, those systems must be observed themselves to ensure the goals of their parent project are being met.

How it works: Real-world observability examples

Here’s how observability works, at its most basic level:

Information is generated by networks and devices as logs, events, traces and metrics[1] in the course of normal operations. An example of a common network metric is connection utilization: Every connection has a utilization percentage, the amount of the total capacity currently in use, which can be checked periodically and noted. This historical record can be used for operational and compliance purposes. The act of recording, analyzing and making decisions informed by that data is an example of observability in action.

But compliance is just the tip of the iceberg. Companies are excited about observability today for the new, fine-grained control and visibility into their resources. With observability, you can see how the resources you own or rent are being consumed. Instead of simply being aware that the connection in the above example was nearing capacity, you can write software to automatically right-size the connection up or down to meet the needs of your users or services in real time.

That’s just one example. Observable organizations can do all kinds of new and helpful things:

Observability for DevOps

DevOps teams can use observability to gain greater insight into the behavior of their network and applications, as well as gain the ability to respond proactively to the root source and cause. Observability helps DevOps answer questions such as:

  • What is the breakdown of resources dedicated to my test and production environments?
  • Are there slack testing resources with consistent periodicity? Can I turn them off sometimes?
  • Where are my network bottlenecks?
  • When has the state of my system changed?

Observability for FinOps

FinOps teams can use observability to gain greater ability to charge back, assign budget and spend to specific teams, business units or projects, and take greater advantage of discounts and spot rates. FinOps can address questions like:

  • What is the trace behavior through the system of any given packet?
  • What are the unit economics of a given service?
  • How can I drive down costs without impacting customer experience?
  • Is there a discount or spot instance that could serve the same profile for cheaper?

Observability in action at Equinix

Increasingly, organizations need observability across all layers of their infrastructure—in the data center, in software applications and in their digital services. We know this is a priority for our customers, and Equinix is planning for and investing in more observability capabilities on our platform to give teams what they need to make transformational changes in their reporting and operations.

For data center infrastructure management (DCIM), Equinix Smart View provides real-time online infrastructure monitoring with seamless business integration through flexible APIs. Equinix digital services currently provide a range of observability features through the Equinix Fabric portal, and we’re building even more into our digital solutions in the future.

Learn more about the features in our digital infrastructures services on the Equinix digital services landing page.


[1] Colleen Marinelli, Unified Observability: The Role of Metrics, Logs, and Traces, VMware, November 23, 2022.

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