Top 7 Azure Monitoring Tools Compared

Azure Monitor is free for basic use and integrated with every Azure service. For simple environments, it is sufficient. For anything more complex, it has gaps: limited cross-service correlation, no distributed tracing across heterogeneous stacks, and a dashboard experience that requires significant custom work to produce actionable views. Most production environments end up supplementing Azure Monitor rather than relying on it alone.

Here is an honest comparison of the tools that appear in serious Azure monitoring conversations, what each is actually good at, and where each falls short.

Azure Monitor

Microsoft's native monitoring platform. Everything in Azure publishes metrics and logs to Azure Monitor. Application Insights provides APM. Log Analytics handles centralised log ingestion and querying via KQL. Alerts can trigger Logic Apps, Functions, or Action Groups. The Workbooks feature produces customisable visual dashboards.

Strengths: Zero-configuration integration with Azure services. No agent required for Azure-native metrics. Log Analytics is genuinely powerful once you know KQL. Application Insights distributed tracing works well for applications instrumented with the Application Insights SDK.

Weaknesses: Cross-service correlation requires significant manual configuration. If your stack mixes Azure services, on-premise infrastructure, and third-party services, stitching a complete picture together in Azure Monitor is difficult. The alert experience produces noise before it produces signal. Dashboard tooling (Workbooks) is powerful but complex, and most teams do not have the capacity to build and maintain them. No anomaly detection without additional services.

Best for: Azure-native workloads, small teams starting with monitoring, environments where cost containment is the primary constraint.

Datadog

A unified observability platform with deep Azure integration: 40+ Azure integrations covering all major services, infrastructure metrics, APM distributed tracing, log management, synthetic monitoring, and security monitoring in a single platform.

Strengths: Correlation across the full stack is the core capability. Infrastructure metrics, application traces, and logs are correlated automatically, so you can move from a latency alert to the service causing it to the specific request trace and the log entries for that request in one session without switching tools. Anomaly detection is built in. The Azure integration is first-class, with auto-discovery of resources. Unified dashboards across Azure, AWS, GCP, and on-premise infrastructure in a single pane.

Weaknesses: Cost scales with host count and log volume. For large estates or log-heavy workloads, costs require active management. Learning curve for teams new to the platform. Requires agent installation for infrastructure monitoring (or agentless integration for Azure-native metrics, but full infrastructure visibility needs the agent).

Best for: Multi-cloud and hybrid environments, regulated businesses that need correlation across layers for incident response, teams running 24/7 operations where MTTR matters. Critical Cloud's platform of choice, as the world's first Powered by Datadog accredited partner.

Microsoft Sentinel

Azure's cloud-native SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) platform. It ingests security events from Azure services, Microsoft 365, and third-party connectors, applies analytics rules to detect threats, and supports automated response playbooks.

Strengths: Deep integration with the Microsoft stack. Pre-built connectors for Active Directory, Entra ID, Microsoft Defender, Azure Firewall, and dozens of third-party sources. Analytics rules cover known attack patterns. Incident investigation with entity mapping. UEBA (User and Entity Behaviour Analytics) identifies anomalous user activity.

Weaknesses: Sentinel is a security operations tool, not a general monitoring or observability platform. Infrastructure performance monitoring is outside its scope. Cost depends on data ingestion volume, which can be substantial for verbose log sources. Requires security operations expertise to tune effectively; out-of-the-box alert fidelity is noisy.

Best for: Security operations, compliance evidence, threat detection for regulated businesses that need SOC capabilities.

Dynatrace

An AI-driven observability platform. The proprietary AI engine (Davis) automatically discovers dependencies, baselines performance, and surfaces root cause analysis rather than just symptoms. Deployment uses a single agent (OneAgent) per host.

Strengths: The AI-driven root cause analysis is genuinely useful for complex environments where manual correlation is time-consuming. OneAgent simplifies deployment. Full-stack observability from infrastructure to user session. Strong for Java and .NET heavy application estates.

Weaknesses: Higher cost than many alternatives, particularly at scale. The platform is opinionated about how you instrument and monitor, which reduces flexibility. The AI suggestions require a period of baseline learning before they are meaningful. Less community-driven than open platforms.

Best for: Large enterprises with complex application estates, teams where engineering capacity for observability tooling is limited, environments requiring deep JVM and .NET performance monitoring.

Grafana

An open-source visualisation and dashboarding platform. Grafana Cloud is the managed SaaS version. It connects to Azure Monitor, Prometheus, InfluxDB, and dozens of other data sources to produce dashboards and alerting.

Strengths: Highly flexible. If your monitoring data is in Azure Monitor or Prometheus, Grafana can produce exactly the dashboards you want without the constraints of platform-native tooling. Free tier for smaller usage. Active open-source community with a large library of pre-built dashboards.

Weaknesses: Grafana is a presentation layer, not a data store. You need separate infrastructure for metrics collection, log storage, and trace storage. Running the full Grafana OSS stack (Grafana, Prometheus, Loki, Tempo) requires operational effort. Grafana Cloud simplifies this but the cost model for larger environments adds up.

Best for: Teams with engineering capacity to manage the stack, environments wanting maximum control over tooling, hybrid setups where Azure is one of several data sources.

New Relic

A full-stack observability platform with broad integration coverage. Pricing is based on data ingested rather than hosts, which is distinctive.

Strengths: The data-based pricing model can be predictable if log volumes are controlled. Good APM for a wide range of languages. Browser monitoring and synthetic monitoring included. Wide integration library.

Weaknesses: Azure-specific integration depth is behind Datadog. Less native Azure service discovery. The data ingestion pricing model can become expensive for log-heavy workloads if ingest is not actively managed.

Best for: Environments with varied language stacks, teams migrating from on-premise APM tools.

Azure Application Insights

Microsoft's APM component within Azure Monitor. Instruments web applications to capture request rates, dependency calls, exceptions, page load performance, and custom events. Can operate standalone (without the full Azure Monitor stack) for application-level monitoring.

Strengths: Deep integration with .NET, Node.js, Java, and Python via SDK. Distributed tracing works well within the Application Insights ecosystem. Live Metrics stream for real-time application telemetry. Availability tests (synthetic monitoring) at low cost. No additional cost beyond Log Analytics ingestion.

Weaknesses: Scope is limited to application-layer monitoring. Infrastructure monitoring requires separate configuration. Does not extend to non-Azure services without custom code. Cross-stack correlation requires manual work to connect with infrastructure metrics.

Best for: Application performance monitoring for Azure-hosted applications, development teams wanting fast setup, complement to Azure Monitor infrastructure monitoring.

How to choose

The right tool depends on three questions:

Is your stack Azure-only or mixed? Azure Monitor is a reasonable choice for Azure-only environments with simple topologies. Any mix of Azure, AWS, GCP, on-premise, or third-party services needs a platform that spans them, which means Datadog, Dynatrace, Grafana, or New Relic.

Do you need security operations (SIEM) or operational observability, or both? Sentinel for SIEM. An observability platform (Datadog, Dynatrace, etc.) for operational monitoring. Most regulated businesses need both, with integration between them.

What is your MTTR target and engineering capacity? If MTTR matters and your engineering capacity for observability tooling is limited, a platform with built-in correlation and anomaly detection (Datadog, Dynatrace) reduces the manual work during incidents. If you have engineering capacity and value flexibility, the Grafana stack gives you control.

Where Critical Cloud comes in

We are the world's first Powered by Datadog accredited partner. That means Datadog is how we run observability for the Azure environments we manage, and we have direct accreditation for the depth of our integration with the platform. We reduce MTTR by 60% for the businesses we operate for, which comes directly from having correlated metrics, traces, and logs in one place rather than multiple tools at 3am. If your monitoring setup is producing alerts rather than answers, see how Critical Support works.