AWS CloudWatch & Azure Monitor vs. APM Stack (Considering Your Observability Needs)

Considering Real Observability Needs

Observability determines how well systems can be understood, operated, and evolved. It is not an add-on for operations but a core capability of modern platforms. Accordingly, the question of where observability is anchored is relevant: as a consumed cloud service or as an independent, controllable architecture.

AWS CloudWatch and Azure Monitor are the native observability services of the major hyperscalers. Open stacks based on Grafana, VictoriaMetrics, VictoriaLogs, and OpenTelemetry take a different approach. Both deliver metrics, logs, and alerts—but they do so with fundamentally different implications for cost, portability, and control.

Hyperscaler Observability: Tightly Integrated, Clearly Limited

AWS CloudWatch and Azure Monitor are designed to collect, display, and evaluate metrics, logs, and events from their respective cloud environments. They integrate deeply into IAM, deployment pipelines, and infrastructure dependencies of the platforms. Many resources are automatically onboarded, dashboards and alerts can be quickly configured, and billing is usage-based.

For workloads that run entirely in AWS or Azure and are intended to remain there, this model works well. The operational entry barrier is low, the functionality is solid, and the integration is convenient.

However, this convenience has structural limits.

Proprietary Architecture as a Systemic Feature

CloudWatch and Azure Monitor are not neutral observability layers. Data schemas, analysis and query languages, retention models, and storage mechanisms are proprietary. Logs, metrics, and events can be exported, but not without friction—especially with large data volumes or longer retention periods.

Costs increase not only with infrastructure but also with data ingestion, retention, and query volume. Particularly with 24/7 monitoring, high-frequency metrics, or extensive logs, observability quickly becomes a difficult-to-calculate cost factor. Technically, everything works—but economically and strategically, it becomes increasingly opaque.

Here, observability remains part of the platform logic.

Open Observability: Infrastructure Instead of Cloud Function

In contrast, there is an open APM and observability stack, as used by ayedo in Managed Apps or in customer-specific observability strategies. Typically, this stack combines:

• Grafana for visualization and alerting
• VictoriaMetrics for performant time series storage
• VictoriaLogs for structured log data
• OpenTelemetry as a standard for instrumentation

This stack serves the same functional purpose as CloudWatch or Azure Monitor. Golden signals, service correlation, alerting, historical analyses—all are possible. The crucial difference lies not in the what but in the how.

Decoupling Source, Storage, and Evaluation

An open observability stack decouples measurement data from individual platforms. OpenTelemetry ensures that metrics, logs, and traces are not tied to proprietary formats. Grafana acts as a unified analysis and presentation layer, independent of where data is generated or stored.

The data resides permanently in its own backends—on infrastructure of your choice. On-premises, in European clouds, or distributed across multiple providers. Observability follows the application and platform architecture, not the hyperscaler.

This fundamentally changes the role of monitoring.

Portability with Real Consequences

This openness has tangible effects. Costs for storage and analysis can be planned linearly because storage and compute resources are controllable. There are no implicit price levers over query volume or API calls.

Switching between clouds, data centers, or platforms occurs without a “reset” of observability data. Histories are preserved, dashboards remain usable, alerts stay consistent. Observability becomes a long-lasting capability—not a cloud-dependent byproduct.

This is also relevant from a regulatory perspective. Requirements from NIS-2, DORA, or the Data Act gain a robust technical foundation through open formats and standardized APIs. Data sovereignty is not a promise but an implementable feature.

Control Instead of Black Box

The difference is not theoretical. Proprietary observability services bind analysis models to their platform logic. Queries, retention, and costs follow provider decisions. Optimization often means: measure less or pay more.

Open stacks dissolve this coupling. Data source, storage medium, and query engine are deliberately separated. Components like Grafana, VictoriaMetrics, and VictoriaLogs make observability a controllable technical capability—not a black box with an unclear cost curve.

Complexity is not eliminated but made manageable.

Condensed Comparison

When Each Approach Makes Sense

Hyperscaler observability is sensible for:

• purely AWS or Azure-centric workloads
• manageable data volumes
• short retention periods
• focus on minimal operational effort

An open observability stack is sensible for:

• Kubernetes-centric platforms
• multi-cloud or hybrid architectures
• high demands on cost control
• long-term data retention
• regulatory and sovereign environments

Conclusion

Observability is not a comfort feature. It determines how systems are understood, evaluated, and controlled.

Hyperscaler monitoring is convenient but tightly controlled. An open-source APM stack is infrastructure-open, standardized, and portable.

The difference is not in the dashboard but in the control. Those who consume observability as a cloud function accept platform boundaries. Those who build it as their own architecture retain data sovereignty, cost control, and strategic flexibility—independent of the operating cloud provider.