What’s the Difference between Azure Monitoring and Cloud Observability?

This is the first blog in our Azure Monitoring series. We’re kicking things off by breaking down the real difference between monitoring and observability—why it matters, where Azure Monitor falls short, and how CloudOps teams can close the gap to move faster, troubleshoot smarter, and stay ahead of issues at scale. Want more? Check out the full series.

Imagine your critical application suddenly slowing down. You check your Azure monitoring dashboards, CPU utilization, memory usage, and network throughput, and they all appear normal. Yet, users report major latency issues. This is the reality of modern cloud operations: a flood of data but limited insights into the real root cause.

Cloud environments generate millions of data points every minute. A microservices-based architecture with hundreds of services and metrics can quickly lead to what’s known as cardinality explosion, making traditional monitoring expensive and often ineffective.

TL;DR

Monitoring shows symptoms. Observability shows the story.

Monitoring tells you when something breaks.

Observability shows you why with full context across metrics, events, logs, and traces.

Microsoft’s Azure Monitor handles the basics but falls short in complex, hybrid, or fast-moving environments.

Modern CloudOps teams need service-aware observability to fix issues faster, reduce alert noise, and stay ahead of what’s next.

LogicMonitor Envision brings it all together, so you can move from reactive troubleshooting to proactive operations.

The Differences Between Monitoring and Observability

Ever had that frustrating experience where your alerts show everything’s fine, but users are complaining your application is crawling? That’s the gap between monitoring and observability. Monitoring tells you something’s broken. Observability shows you why.

Known Unknowns vs. Unknown Unknowns

Traditional monitoring works well when you know what to watch for: high CPU, memory leaks, and disk space running out. You set a threshold, wait for it to trigger, and respond. These are your known unknowns—predictable issues with clear triggers.

But today’s environments don’t play by the rules. You’ve got race conditions between microservices, intermittent API timeouts, and cascading failures that don’t fit any pattern. These unknown unknowns don’t trigger your standard alerts because you didn’t know to set them up in the first place.

That’s where observability comes in. It connects the dots across services and telemetry to spot anomalies, expose hidden patterns, and surface the problems your alerts missed.

Where Microsoft Azure Monitor Fits and Where It Falls Short

Azure Monitor is a solid starting point. If you’re running Azure-native workloads, it gives you the basics: metrics, alerts, logs, and some tracing. It’s helpful for tracking performance, spotting infrastructure issues, and setting up alarms for known problems.

You’ll get:

• Metrics & alerts: CPU spikes, memory pressure, disk utilization. It works, but thresholds often need tuning, and alerts can get noisy.
• Log analytics: All your Azure logs in one place, searchable with Kusto Query Language (KQL). Powerful, but not the most intuitive.
• Application Insights: Built-in request tracing and telemetry for your apps, though it lacks the depth of full APM solutions.
• Network monitoring: Tools like Connection Monitor help spot latency and packet loss before users feel the pain.

See what Azure monitoring in LogicMonitor Envision is all about.

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But Here’s the Problem: It’s Not Enough for Modern CloudOps

Once your environment gets complex—multi-region, hybrid, or multi-cloud—Azure Monitor starts to fall behind.

1. Limited Correlation Across Services

Azure Monitor shows you what happened, but not how it’s connected. You’ll often end up jumping between dashboards, manually stitching together logs and metrics across services to figure out root cause.

For example, a database delay causes slow API responses, which triggers a spike in frontend latency. Azure Monitor might alert you about the CPU load, but it won’t help you trace the issue all the way back to the database layer.

2. Surface-Level Anomaly Detection

If you’re using Azure Monitor’s anomaly detection, you’ll find it does offer dynamic thresholds and some smart detection features, but they’re limited in scope and often require manual setup or deep KQL expertise. More advanced capabilities, like automatically recognizing patterns across metrics, logs, and infrastructure telemetry, just aren’t built in. That means early warning signs can easily get missed.

3. Limited Hybrid and Multi-Cloud Visibility

Azure Monitor is primarily made for Azure-native workloads. If you’re running multi-cloud architectures or on-premises systems, you’ll face integration challenges.

For example, a retail organization operates across Azure, AWS, and an on-prem data center. Monitoring multiple environments with different dashboards and integrations makes it hard to get a clear picture of what’s going on. Engineers don’t have one unified view, which makes their work more difficult and time-consuming.

4. Tracing That Doesn’t Scale

Application Insights can help you trace requests, but only if every service is properly instrumented. In dynamic environments with ephemeral services or frequent changes, gaps in tracing data become the norm.

Why CloudOps Teams Need More Than Monitoring

Monitoring shows you individual symptoms. Observability shows you the full picture. That difference is critical when you’re running distributed services at scale, especially when uptime, customer experience, and cost are on the line.

That’s why many teams are moving beyond Azure Monitor and embracing platforms like LogicMonitor Envision. Not to replace monitoring, but to extend it. To correlate M.E.L.T. signals across services. To spot root causes faster. To make smarter decisions based on context, not just alerts.

Because at the end of the day, observability isn’t just about fixing things faster. It’s about seeing your environment for what it really is: a network of services that power your business.

Moving Beyond Azure Monitor: Why Observability Is the New Baseline

Cloud environments don’t sit still. Services scale, traffic spikes, and new deployments drop every day. You can’t afford to wait for alerts to fire or dashboards to light up after users are already feeling the pain.

Observability gives you the context to act before that happens. It helps you:

• Connect the dots between performance data and real-world impact
• Spot root causes faster with correlated, multi-source telemetry
• Prioritize what matters based on service health, not just CPU graphs
  

And if you’re operating in a hybrid or multi-cloud environment, observability isn’t a bonus. It’s how you stay ahead without burning out your team.

That’s why more CloudOps teams are leaning on platforms like LogicMonitor Envision—not just to monitor infrastructure, but to understand services. To shift from reactive to proactive. From fragmented signals to full-context insight.

The next blog in our Azure Monitoring series breaks down the five biggest challenges that appear when Azure scales and how to solve them before they hit your bottom line.