Many organisations deploy cloud native applications across multiple environments (Azure, AWS, on-prem).
With services spinning up and down in milliseconds and applications distributed across multiple platforms, pinpointing the root cause of failures has become more challenging than ever.
To meet this challenge, we need cloud native observability.
Reading time 9 minutes Published: 17 June 2025
Even though the terms are closely related, they slightly differ. While closely related to the idea of gaining insights, observability means:
“The capacity to determine a system's internal state by examining its external outputs.”
Observability in IT and Cloud Computing refers to the ability to understand a system's internal state by examining its outputs.
With outputs, we refer to the data it generates such as: logs, metrics, and traces.
In the context of Cloud Native architectures, observability goes beyond traditional monitoring to provide actionable insights into complex, distributed systems.
Cloud Native observability means inferring the current and potential states of systems using monitoring data. It's about designing your system so that its internal state can be understood by analysing its external behaviour.
This makes it possible (even in complex systems) for you to understand what is going on, and troubleshoot problems by identifying why and where failures happen. It involves collecting and correlating data from logs, metrics and traces to gain deep insights into system health and troubleshoot issues quickly. Let’s have a look at an example.
You’re driving a car, but you cannot see the engine. All you have are the dashboard indicators like the speedometer, fuel gauge and temperature gauge. These are external outputs telling you what’s happening with the car. Now if the temperature gauge suddenly spikes it doesn’t tell you what’s causing it – maybe the engine is overheating because of a coolant leak, or maybe, it is a faulty thermostat?
You cannot look inside the engine, but by reading these indicators and combining them with what you know about cars you can start to figure out what’s wrong and take action, like pulling over to check the engine.
In software, observability works the same way. You can’t see the internal workings of your entire system (cars-engine) in real-time.
However, you can gather data from logs, metrics and traces (your “dashboard indicators”).
With this information, you can infer the system’s internal state, like identifying slow database queries or services that are malfunctioning, without seeing every part of the system. This helps you troubleshoot and fix issues fast, just like how the dashboard lights help you figure out what’s wrong with your car.
Systems become more complex and downtime becomes more costly. Hence, it's critical to have actionable data about your systems to make the right decisions in real-time (especially in high-stress production incidents).
Traditional tools can handle recurring issues, but fail when it comes to one-off problems that are becoming more common in modern distributed systems.
With Cloud Native observability, you can:
Read more about how we help organisations like yours become (more) Cloud Native.
Cloud Native by InterceptObservability captures all the signals across the system: metrics, logs and traces. This means no blind spots. So no critical data is missed during troubleshooting. Every internal process can be traced to reveal what caused a particular issue in your application or cloud infrastructure.
Cloud Native observability means real time monitoring so you can detect and resolve issues before they become critical. You get less downtime and more reliable systems.
Modern observability tools use AI and automation to predict system failures before they hit end users, which might have been missed by software engineers. By analysing patterns in logs, traces and metrics, AI can automate anomaly detection and reduce manual work. So teams can focus on strategic improvements.
The following are some big pitfalls in observability:
Observability is key to a system being able to provide insight into its behaviour so you can improve and adapt. If you delay setting up observability until problems arise then it may be too late. The earlier, the better.
Expecting observability to be the magic solution is another common “misperception”. While it can provide valuable data and insight, it can’t solve scalability issues or prevent system failures. Observability tells you why something went wrong, but the underlying problem still needs to be fixed. The data gathered must lead to actionable, and those must be prioritised against your business needs.
Another mistake is thinking that installing popular open-source observability stacks means the job is done. Observability needs continuous attention and should be part of the software development lifecycle. Like testing, observability is not a set-and-forget solution but needs regular review, improvement and cultural adoption within the organisation. And it can’t rest on one team – everyone from developers to operations needs to be aligned and contribute.
Another mistake is collecting every possible piece of telemetry data, such as high-cardinality metrics, all traces and full logs – without considering the practical value. It’s tempting to collect everything for future use, but this can quickly overwhelm resources and budgets. Processing too much data chokes systems and slows performance. A more practical approach is to start with the essentials and log or trace on demand and make sure the data gathered serves specific, actionable purposes.
The challenges of Cloud Native Observability are multifaceted.
If you need help or have any questions for implementing observability in Azure, feel free to contact us.
Get in TouchCloud Native Observability Tools in Azure
Many organisations are running in multi-cloud environments, on Microsoft Azure, AWS, on-prem and also hybrid: and they all need a single pane of glass to correlate data across sources.
To address these needs, Microsoft provides Azure Monitor: a full-stack observability solution for all cloud environments. If you’re already running on Azure, Azure Monitor is native and available from day one.
Think about it, your issues can come from anywhere: app layer, infrastructure, network. Azure Monitor collects data across all layers and gives you insights to trace issues.
It’s also enterprise-ready: secure, private, compliant, governed, and fully documented. So you get a robust, production-grade solution.
You can also configure alerts, including smart ML-powered dynamic alerts that learn your usage patterns.
All collected data (observability, security, management) goes into a central log analytics platform. That’s the heart of Azure Monitor. You can use KQL (Kusto Query Language) for diagnostics and troubleshooting and even Copilot to make querying easier.
Application performance monitoring (APM) is handled by Application Insights, which tracks failures, performance, and usage.
Microsoft uses AI for scenarios as detection, troubleshooting, and optimisation.
Visualisation options include Azure dashboards and workbooks, but for cloud-native and multi-cloud users, Azure Managed Grafana is a powerful option. It’s an enterprise-grade, fully managed Grafana service that integrates seamlessly with Azure Monitor. You can spin up a Grafana instance, connect it to your data, and use built-in or community dashboards.
In Kubernetes-heavy environments, Azure Monitor’s capabilities have grown rapidly. Here, you can take advantage of the managed Prometheus service. Prometheus is a widely used open-source tool for time-series metric storage that can scale to 1 billion time series. With just a few clicks, you can start collecting and analysing metrics.
To help manage costs, Azure Monitor offers a basic logs tier, which can reduce log spend by up to 33%. Azure Monitor Container Insights aggregates all your container logs, AKS audit and infrastructure logs, and syslog, giving you a unified view of your containerised environments.
Azure Arc extends Azure Monitor’s observability capabilities to other environments. You can onboard servers or Kubernetes clusters running outside of Azure (whether on other cloud platforms or on-prem). Azure Arc also supports edge observability, making it easier to monitor and manage workloads running in remote or disconnected environments.
Observability is the ability to answer questions about behaviour that allows your business to improve, adapt and grow. These are the harder questions of our systems that we can’t answer with metrics alone.
It doesn’t matter what subset of logs, traces and/or metrics you’re collecting; what matters is whether can you answer your questions with the signals you have.
In cloud-based environments like containerised microservices, there’s a lot of data being generated all the time. It’s up to developers and system architects to turn that data into insights that drive.
Monitoring with intent, i.e. focusing on the important bits and not trying to capture everything, is key especially when debugging.
And not to forget, building systems with observability in mind means designing them to produce the data to understand their internal state. We hope you now have a clearer understanding of observability and Cloud Native observability.
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