Azure Monitor vs. Loki

Observability as a Service or as Your Own Infrastructure

Azure Monitor and Loki take two fundamentally different approaches to monitoring and logging. Both provide insights into systems, metrics, and logs. However, the key difference is not in visibility, but in who retains control over data, costs, and architecture.

Observability is not a comfort feature. It determines how quickly systems can be understood, errors analyzed, and incidents processed. Making incorrect assumptions here not only leads to a loss of transparency but also, in the long run, to a loss of control.

Azure Monitor: Observability as a Managed Service

Azure Monitor is Microsoft’s central observability platform. Metrics, logs, traces, and alerts are tightly integrated and fully managed. For Azure workloads, this works seamlessly. Logs automatically flow from VMs, AKS, App Services, Entra ID, and many other services. Dashboards are immediately available, alerts can be defined with a few clicks, and queries are formulated using KQL.

The operational effort is minimal. There is no need to plan, operate, or scale infrastructure for logging and monitoring. Observability is consumed—similar to compute or storage. For teams that want to be productive quickly or cannot take on much operational responsibility, this is attractive.

However, this convenience is not neutral.

Integration as a Limit

Azure Monitor is optimized for Azure—not for openness. KQL is powerful but proprietary. Those who commit to it bind analysis, dashboards, and knowledge to a specific query model. Logs reside in Microsoft’s data platform, and retention, access, and billing follow the Azure pricing model.

This becomes relevant especially as log volumes increase. Billing is based on ingested data, retention duration, and queries. Security events, audit logs, or debug logs at high frequency can quickly drive up costs—often only becoming visible when observability is already business-critical.

A later exit is possible but cumbersome. Data must be exported, queries reformulated, and dashboards rebuilt. Observability thus becomes a structural lock-in rather than a supportive tool.

Loki: Logs as Infrastructure, Not as a Product

Loki deliberately stands on the other side. Open source, reduced in functionality, tightly integrated with Kubernetes and the Grafana ecosystem. Logs are not fully indexed like in traditional log systems but accessed via labels. The actual log content remains largely unindexed.

This approach has consequences. Queries work differently, and analysis requires clean label strategies. At the same time, storage and index costs drop drastically. Loki scales horizontally, is storage-efficient, and is particularly suitable for high log volumes from Kubernetes workloads.

The price for this is operation. Loki must be planned, operated, monitored, and secured. However, control remains entirely with the operator.

Data Sovereignty and Portability

The crucial difference between Azure Monitor and Loki is data sovereignty. Loki can run anywhere: on-premises, in Kubernetes, in any cloud, even hybrid. Storage is interchangeable—from local object storage to S3-compatible backends. Retention rules are self-defined, costs transparently calculable.

Logs remain logs. They do not become a proprietary analysis product with an unclear pricing model. Queries with LogQL are open, reproducible, and portable. Dashboards in Grafana can be migrated, versioned, and reused.

This is crucial, especially in platform scenarios. Observability data is used not only for operations but also for security analyses, audits, incident reviews, and compliance proofs. Handing over this data is a strategic decision.

Costs as an Architecture Driver

Azure Monitor excels in convenience. For purely Azure-centric setups with moderate log volumes, this is often sufficient. However, as logs become the central data source, the model shifts. Query and retention costs become a risk factor—not because observability is unnecessary, but because it suddenly becomes economically limited.

Loki treats logs as infrastructure. Costs primarily arise from storage and operation, not from every access or query. This makes the model calculable in the long term. Observability is not restricted because it becomes “too expensive.”

Condensed Comparison

When Each Approach Makes Sense

Azure Monitor is suitable for:

• Clearly Azure-centric workloads
• Moderate log volumes
• Teams focused on quick implementation
• Scenarios without a long-term observability strategy

Loki is suitable for:

• Kubernetes as a central platform
• High log volumes
• Requirements for cost control
• Security, audit, and compliance use cases
• Architectures with a multi-cloud or hybrid perspective

Conclusion

Observability determines how well systems remain manageable. It is not a comfort feature—it is a power factor in operations.

Azure Monitor optimizes for consumption and integration. Loki optimizes for control and calculability.

The difference is not primarily technical. It is strategic: Do you consume observability—or do you consciously operate it as part of your own platform.