Why Metrics Alone Don’t Cut It: What You Really Need for Azure Monitoring and Troubleshooting
This is the tenth blog in our Azure Monitoring series, and it’s all about what metrics miss. We’ll break down why teams need more than CPU graphs to troubleshoot effectively and how events, logs, and traces work together to expose what’s really going on behind those “all green” dashboards. Missed our earlier posts? Check out […]
This is the tenth blog in our Azure Monitoring series, and it’s all about what metrics miss. We’ll break down why teams need more than CPU graphs to troubleshoot effectively and how events, logs, and traces work together to expose what’s really going on behind those “all green” dashboards. Missed our earlier posts? Check out the full series.
“Everything’s green—so why isn’t it working?”
If you’ve ever stared at a perfectly healthy Azure dashboard while users flood the help desk with complaints, you’re not alone. Metrics might say everything’s fine, but without the full picture, you’re left guessing.
This is the tenth blog in our Azure Monitoring series. We’re digging into why metrics-only monitoring doesn’t cut it anymore, and what your team actually needs to troubleshoot complex environments faster and smarter.
TL;DR
Metrics only show you symptoms. M.E.L.T. data reveals the cause.
“Everything’s green” doesn’t mean everything’s fine
Events show what changed before things went sideways
Logs explain what the metrics can’t
Traces uncover where things break down across services
A unified observability platform helps you find and fix issues faster
When Metrics Lie
Let’s go back to that stuck dashboard. A financial services ops team saw normal CPU, memory, and network usage. But customers couldn’t complete transactions, and no one knew why. After three weeks of finger-pointing, they found it: a missing database index. That config change went live just before the failures started—but without logs, traces, or event context, it stayed invisible.
Here’s the truth: metrics only tell you what’s happening. They rarely tell you why.
Resource-specific data across compute, storage, and networking
Guest OS metrics (with agents)
Platform health indicators
That’s a decent start. But without logs, traces, and event visibility, you’re missing:
The actual error messages triggering issues
Where failures cascade across microservices
When a code push or policy change caused something to break
You also hit retention limits (93 days max) and sampling gaps that can mask fast-moving problems. And if you’re not collecting higher-resolution metrics or paying for extra retention, critical data disappears before you even get a chance to analyze it.
Gain deeper insights into your Azure environment by integrating logs and metrics.
Let’s say your VM shows high CPU. Metrics tell you something’s off. But they don’t answer:
Is an inefficient code path consuming excessive cycles?
Are failed API calls triggering CPU-intensive retry loops?
Has a missing database index caused query compilation overhead?
Is connection latency forcing components to wait while holding resources?
Without supporting context—events, logs, and traces—you’re guessing. And guessing slows everything down.
Events, Logs, and Traces: The Essential Missing Elements
After examining the limitations of metrics-only monitoring, it’s clear that a more comprehensive approach is needed. This is where events, logs, and traces become invaluable. These three observability pillars complement metrics by providing the context, causality, and connection details that metrics alone cannot deliver.
What Events Add to the Picture
Events are the “what changed” signal every ops team needs. They fill in the blanks when metrics spike or alerts fire unexpectedly.
With event data, you can:
See when a config change, deployment, or policy update happened
Correlate changes with emerging issues in real time
Separate user-generated issues from systemic failures
Validate whether an issue was caused by a release or just bad timing
Event signals provide the timeline and causality that tie the rest of your telemetry together. Without them, you’re stuck searching for clues. With them, root cause often surfaces in seconds.
How Logs Expand the Picture
Logs give you the story behind the symptom. They show you:
The exact error that triggered a failure
Which component threw the exception
Session behavior and user patterns
Audit and access trails for security reviews
Enriched logs that include change events—like deployments, config edits, and alert state transitions—make troubleshooting even faster. They show you what changed right before things went sideways.
Check out how LM Logs makes root cause analysis way easier.
In modern, service-heavy environments, tracing is your map. It connects the dots across services, functions, containers, and APIs. With traces, you can:
Visualize how a request flows through your stack
See which service added latency
Spot retry loops, broken dependencies, and bottlenecks
Understand how one failure ripples across the system
This matters when your app is no longer a single VM but a collection of interconnected services that each contribute to the user experience. Traces give you the full execution path, even when it spans dozens of components.
Why All of This Should Live in One Platform
Collecting logs, traces, metrics, and events in separate tools is a visibility tax your team can’t afford. It leads to:
Context switching during incidents
Missed root causes from fragmented data
Slower incident response
LogicMonitor Envision brings it all together.
What LM Envision Delivers
Unified Visibility Across Telemetry Types
One view across metrics, logs, traces, and events
No toggling between Azure Monitor, App Insights, and third-party log tools
Visibility across Azure, hybrid, and multi-cloud environments
Metrics provide vital health indicators, but they only tell part of the story. True observability requires the context and depth that events, logs, and traces deliver, transforming isolated data points into a comprehensive understanding of the system.
Organizations implementing observability across all four pillars consistently report:
Cut MTTR by up to 46%
Resolve issues before users notice
Eliminate alert noise with context-aware triage
Break down silos between teams
And most importantly? You get your time back.
Next in our series: how LogicMonitor Envision enhances Azure monitoring. We’ll show how LogicMonitor fills the Azure Monitor gaps with unified visibility, intelligent alerts, and predictive analytics. Through customer stories, you’ll see how organizations achieve faster troubleshooting, fewer alerts, and better efficiency.
See how LM Envision brings metrics, events, logs, and traces together.
If Azure Monitor already shows me metrics, why should I bother setting up logs and traces too?
Azure Monitor’s default metrics give a high-level health snapshot — CPU usage, memory, etc. But they can’t explain why something is slow or broken. Logs reveal the exact errors, and traces show how the issue travels through your services. This is why most teams outgrow metrics-only Azure monitoring tools and move toward full observability that includes events, logs, and traces.
What’s the difference between an event and a log?
An event marks a specific change like a config update or deployment. A log records what the system or app was doing (or failing to do) during that time.
How do I know if I’m missing traces in my current Azure setup?
If you’re using Azure Monitor without integrating Application Insights or OpenTelemetry, you’re likely missing distributed tracing in Azure. A clear red flag: you can’t follow a request across services or visualize where the latency or failure originated in a multi-service environment.
Can I use LogicMonitor Envision alongside Azure Monitor, or do I need to replace it?
You don’t need to replace Azure Monitor. LogicMonitor Envision complements it by aggregating logs, metrics, traces, and events into a single pane of glass. This unified approach supports Azure observability best practices, helping you avoid blind spots and context switching during incidents.
I’m getting a lot of alerts, how does “context-aware triage” help with that?
When data streams are siloed, alerts fire without enough context, creating noise. Context-aware triage links logs, metrics, events, and traces to highlight only what truly matters.
How do I collect logs and traces without using up too much storage or blowing my budget?
Set up sampling and filtering for instance, collect full traces only for error cases or latency outliers. Platforms like LogicMonitor help you retain critical logs and traces cost-effectively, supporting smarter long-term data strategy without compromising visibility.
By Nishant Kabra
Senior Product Manager for Hybrid Cloud Observability
Results-driven, detail-oriented technology professional with over 20 years of delivering customer-oriented solutions with experience in product management, IT consulting, software development, field enablement, strategic planning, and solution architecture.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
