Hey everyone, let’s talk about something that most teams either ignore until it’s too late or overcomplicate from day one: observability. You know that feeling when production breaks at 2 AM and you’re staring at logs like a detective trying to solve a cold case with no evidence? Yeah, that’s what happens when your monitoring strategy is an afterthought. Today we’re going to walk through how to build a Monitoring Golden Path, a structured, repeatable approach to observability that any organization can adopt.

This article is based on an open-source project I built called Azure Observability Strategies, which contains production-ready Bicep modules, demo applications with distributed tracing, and step-by-step verification guides. Feel free to explore the repo and use it as a reference for your own implementations.

What is a Monitoring Golden Path?

Think of it as the paved road for observability. Instead of every team reinventing the wheel deciding which tools to use, how to name resources, where to send logs, or what alerts to set up, you provide a well-defined path that covers the essentials out of the box. Teams get production-ready monitoring by following the path, and they can extend it when needed.

A solid Golden Path typically covers these pillars:

Why Should You Care?

• Cost control: Unmanaged telemetry is like leaving all the lights on in a mansion. Azure Monitor charges per GB ingested, and without sampling and retention policies, costs spiral fast.
• Faster incident response: When services talk to each other, you need distributed tracing to understand what actually happened, not just where something failed.
• Compliance: GDPR, HIPAA, and friends don’t care about your excuses. You need to know where your data lives and who can access it.
• Developer experience: Nobody wants to spend three days setting up monitoring for a new service. A Golden Path means you get observability in minutes, not sprints.

Step 1: Centralized Monitoring Strategy

The first decision is architectural: where does all your telemetry go?

The recommended approach is a single Log Analytics Workspace feeding all your Application Insights instances. This gives you unified querying across all services, no more jumping between five different dashboards to understand one incident. You can see this architecture implemented in the main.bicep orchestration file of the repository.

Each component gets its own Application Insights instance (for isolation and independent sampling), but they all flow into the same workspace. This is the difference between “I can see everything” and “I can see my one service, good luck with the rest.”

Why Workspace-Based App Insights?

The classic (standalone) Application Insights is deprecated. Workspace-based mode is the modern approach and gives you:

• Unified KQL queries across all services
• Longer retention options (up to 730 days with archiving)
• Integration with Azure Sentinel for security analysis
• Per-table retention tuning for cost optimization

Step 2: Naming Conventions & Tags

This might sound boring, but trust me, naming saves lives at 2 AM.

Adopt a consistent pattern like: {type}-{workload}-{env}-{region}

And enforce required tags through policy. At minimum:

• env: dev, staging, prod
• workload: which application does this belong to
• owner: who to call when things go south
• costCenter: who pays for this

The key here is enforcement, not just guidelines. Use policies in Audit mode for dev (warn but don’t block) and Deny mode for production (no tag, no deployment).

Step 3: Distributed Tracing with OpenTelemetry

This is where it gets really interesting. In a microservices world, a single user request can touch 5, 10, or 20 services. Without distributed tracing, debugging is like trying to follow a conversation in a crowded room while wearing earplugs.

The OpenTelemetry Standard

OpenTelemetry (OTel) is the industry-standard framework for distributed tracing, and it’s the recommended approach for cloud-native observability. The key concepts:

• Traces: The full journey of a request across services
• Spans: Individual operations within a trace (an HTTP call, a database query)
• W3C TraceContext: The standard header (traceparent) that propagates trace IDs across services

What Does a Distributed Trace Look Like?

Imagine a user hits your web app, which calls your API, which triggers a serverless function:

1. 🌐 Web receives GET /demo           (AppRequests)
2. 📡 Web calls API /orders/{id}       (AppDependencies)
3. 🔧 API receives /orders/{id}        (AppRequests)
4. ⚡ API calls Function /api/enrich    (AppDependencies)
5. 🔌 Function receives /api/enrich    (AppRequests)

All five operations share the same TraceId. You can query them all at once and see the exact flow, latency at each hop, and where things broke. The demo applications in the repository implement exactly this pattern with a .NET 9 Web + API + Azure Functions stack.

Minimal Code, Maximum Observability

The beauty of modern OTel distros is how little code you need. For a .NET application, it’s essentially one package and three lines of code:

// Package: Azure.Monitor.OpenTelemetry.AspNetCore

builder.Services.AddOpenTelemetry()
    .ConfigureResource(resource => resource
        .AddService(serviceName: "web-myapp", serviceVersion: "1.0.0"))
    .UseAzureMonitor(options =>
    {
        options.SamplingRatio = 1.0f; // 100% in dev, 0.1 (10%) in prod
    });

That’s it. This automatically instruments:

No manual span creation, no decorators, no middleware. The distro handles it. Check the full OpenTelemetry standard documentation for detailed configuration examples across Web, API, and Azure Functions.

Step 4: Sampling, The Art of Keeping Less But Seeing Enough

Here’s a question most teams don’t ask until the bill arrives: Do you really need 100% of your telemetry in production?

If your API handles 10 million requests per day, capturing every single one means massive storage costs and slow queries. The answer is sampling: keep a statistically significant percentage and let the math do the rest.

Key Insight

The sampling decision is made at the trace level, not the span level. This means if a trace is sampled in, all spans in that trace are kept. No broken traces, no missing pieces in your dependency chain.

And here’s the thing: metrics are never sampled. Your alert rules based on error counts, response times, and failure rates always use 100% of the data, regardless of the sampling ratio. Sampling only affects detailed transaction logs.

If production catches fire and you need to temporarily see more, you can override sampling at runtime:

# Temporarily increase to 50% during an incident investigation
az webapp config appsettings set \
  --name mywebapp \
  --resource-group myrg \
  --settings OTEL_SAMPLING_RATIO=0.5

No redeployment needed.

Step 5: Retention & Cost Controls

Observability at scale requires intentional choices about what to keep and how long to keep it. Uncontrolled telemetry leads to exploding costs, important signals buried in noise, and slow queries over massive datasets.

The Cost of Logging

This is something people don’t think about until it’s too late:

A single logger.LogInformation("Request body: {Body}", jsonBody) in a hot path can cost more than the compute running your app. Be intentional about what you log.

Per-Table Retention

Not all telemetry is created equal. You might want to keep AppExceptions for 90 days but AppTraces (verbose logs) for only 14 days. Modern Log Analytics supports per-table retention policies, letting you fine-tune costs without losing critical data. For a deeper dive into sampling ratios, log level strategies, and cost estimation formulas, check the Sampling, Retention & Cost Controls Standard.

Step 6: Governance & Security

The governance pillar covers four key areas:

1. Access Control (RBAC): Who can view telemetry? Who can modify alerts? Follow least-privilege and scope roles to the monitoring resource group.

2. Data Minimization (PII Prevention): This is a big one. Your telemetry should never contain PII. No email addresses, no user IDs, no IP addresses in log messages. Establish a default DENY policy for PII in telemetry and use safe structured logging patterns:

// ❌ BAD - PII in logs
logger.LogInformation("User {Email} placed order {OrderId}", user.Email, orderId);

// ✅ GOOD - No PII
logger.LogInformation("Order {OrderId} placed by user {UserId}", orderId, user.HashedId);

3. Data Residency: Know where your telemetry lives. If you operate in the EU, your Log Analytics Workspace should be in an EU region. No cross-border data transfer surprises.

4. Retention & Deletion: Right to erasure (GDPR Article 17) applies to telemetry too. Plan for it.

Step 7: The Operational Layer, Alerts, Dashboards, and Proactive Monitoring

This is where raw telemetry transforms into actionable intelligence. Collecting data is meaningless if nobody sees it when things break.

Baseline Alerts

Every production workload should ship with a minimum set of alerts. Here’s a solid starting point:

Notice the dev thresholds are more relaxed. You don’t want to wake someone up because a dev environment had a hiccup, but you absolutely want to know when production crosses the line.

Action Groups

Alerts need to go somewhere. Configure Action Groups with:

• Email for the ops team
• Teams/Slack webhooks for real-time chat notifications
• Azure Functions/Logic Apps for auto-remediation (restart a service, scale up)

Use the Common Alert Schema so all alerts produce a consistent JSON payload. This makes it trivial to build automation on top. The ops-alerting-workbooks module in the repository deploys all 8 baseline alerts, action groups, and an operational workbook in a single Bicep module.

Operational Dashboards

Finally, build a workbook (or Grafana dashboard, whatever your team prefers) that shows at a glance:

• Request rates and error rates per service
• P50/P95/P99 latencies
• Dependency health (are downstream calls failing?)
• Top exceptions
• Resource utilization trends

The Infrastructure as Code Angle

Here’s the thing that ties everything together: all of this should be codified. Your monitoring infrastructure should live in the same repo as your application, deployed through the same pipeline, and reviewed through the same PR process.

Whether you use Bicep, Terraform, or Pulumi, your monitoring stack should be:

• Version-controlled: Every alert rule, every dashboard, every policy
• Environment-aware: Different thresholds for dev vs. prod
• Modular: Reusable modules that teams can compose
• Parameterized: One template, many environments

This isn’t just good practice, it’s the only way to scale observability across an organization. You can’t have a “monitoring team” manually clicking through portals for 50 workloads. The Azure Observability Strategies repository is a working example of this approach, with reusable Bicep modules for foundation, App Insights, compute, diagnostics, alerting, policies, and security.

The 8-Step Summary

If you take one thing away from this article, it’s that observability is not a single tool or service, it’s a strategy with multiple layers:

1. Monitoring Strategy: Centralize telemetry in a single workspace
2. Naming & Tags: Make resources findable and accountable
3. Distributed Tracing: Follow requests across service boundaries with OpenTelemetry
4. Sampling: Keep costs under control without losing visibility
5. Retention & Costs: Be intentional about what you keep and for how long
6. Auto-Instrumentation: Get telemetry with minimal code effort
7. Governance & Security: Protect data, enforce access, prevent PII leaks
8. Operational Layer: Turn data into alerts and dashboards that drive action

Each step builds on the previous one, and together they form a complete observability foundation that any team can adopt.

Remember

Observability is never “done.” Your architecture evolves, your traffic patterns change, and new services introduce new failure modes. Treat your monitoring strategy the same way you treat your application code: iterate, review, and improve continuously. The goal isn’t perfection on day one, it’s having a foundation solid enough that when production breaks at 2 AM, you actually have the data to fix it before your coffee gets cold.

References

• Azure Monitor Documentation
• Application Insights Overview
• OpenTelemetry for .NET
• OpenTelemetry Official Site
• W3C Trace Context Specification
• Log Analytics Data Retention
• Sampling in Application Insights
• Azure Observability Strategies (GitHub)