Open-Source Monitoring Stack for Kubernetes with Grafana Loki, Mimir, and Tempo

The Customer The customer runs production workloads on Kubernetes and was scaling out multiple clusters across environments. As the footprint grew, the team needed a reliable way to monitor cluster health, application performance, and infrastructure behavior in real time — without taking on the licensing and storage overhead typically associated with commercial observability platforms.

The Pre-State The customer wanted to set up and configure an open-source monitoring and tracing solution for their Kubernetes workloads. Cost efficiency was a primary constraint, particularly around storage costs, which tend to balloon as log and metrics volume grows with cluster scale.

The solution needed to cover all three pillars of observability; metrics, logs, and traces and support customizable dashboards so different teams could visualize the signals that mattered most to them.

The Core Challenge Implementing a robust, production-grade open-source monitoring solution for Kubernetes clusters that could unify metrics, logs, and traces into a single pane of glass, scale with cluster growth, and keep long-term storage costs under control all without vendor lock-in.

The Post State

CloudifyOps designed and implemented a fully open-source observability stack built around the Grafana ecosystem, chosen specifically for its modularity, cost profile, and native support for object storage backends.

1. Log Aggregation with Grafana Loki:

Grafana Loki, a distributed log aggregation and storage system, was deployed to efficiently collect and store application and infrastructure logs. Loki’s index-light design kept storage overhead low while preserving full log searchability across the cluster.

2. Metrics Aggregation with Grafana Mimir:

Grafana Mimir, an open-source, horizontally scalable metrics aggregation and visualization backend, was integrated to collect and store metrics from across the Kubernetes environment, giving the team a long-term, queryable metrics store.

3. Distributed Tracing with Grafana Tempo:

Grafana Tempo was integrated to enable distributed tracing, allowing the team to correlate logs and metrics with request-level traces and pinpoint latency issues down to individual API calls.

4. Unified Data Collection:

The data collection layer was standardized using the Grafana Agent, configured to gather metrics, logs, and traces from every workload and push them to their respective backends — Mimir, Loki, and Tempo — through a single lightweight collector, reducing operational complexity.

5. Custom Dashboards & Correlated Visibility:

• Metrics dashboards: Real-time cluster and application metrics visualized using Mimir queries.

• Log dashboards: Centralized views of application logs, including error logs and HTTP errors, using Loki.

• Trace dashboards: Latency and dependency mapping for API calls visualized using Tempo.

By correlating logs from Loki with metrics from Mimir, the team could move from “something is wrong” to “here is exactly what and where” far faster than with siloed tooling.

6. Proactive Alerting:

To shift the team from reactive to proactive incident response, alerting rules and notification channels were configured using the Kubernetes Mixin (a curated set of community-tested Kubernetes alerting and recording rules) alongside Alertmanager, ensuring the right people were notified before minor issues became outages.

Reference Architecture:

Application workloads are instrumented via the Grafana Agent, which collects metrics, logs, and traces and routes them respectively to Mimir, Loki, and Tempo. Grafana sits on top as the unified visualization and alerting layer, giving engineering and support teams a single interface into cluster health.

The Bridge

• Comprehensive Observability: Achieved end-to-end visibility into Kubernetes clusters by combining logs, metrics, and traces into a single, correlated observability layer.

• Improved Mean Time to Detect (MTTD): Reduced the time to detect issues, improving on industry-standard MTTD benchmarks through proactive alerting and correlated dashboards.

• Efficient Log Aggregation: Consolidated logs from multiple Kubernetes components into a centralized system for monitoring and analysis, eliminating siloed troubleshooting.

• Storage Cost Reduction: Reduced log storage costs by 20% by configuring Amazon S3 as the backend object store for both Loki and Mimir, taking advantage of their native S3 support.

• Enhanced Metrics Visualization: Delivered custom dashboards built on Mimir queries, giving teams real-time visualization of the metrics most relevant to their workloads.

Conclusion

By combining Grafana Loki, Mimir, and Tempo into a single, cost-optimized observability stack-backed by Amazon S3 for storage CloudifyOps enabled the customer to achieve comprehensive, correlated visibility across their Kubernetes environment while cutting log storage costs by 20%.

The result is a scalable, open-source monitoring foundation that grows with the customer’s Kubernetes footprint without the licensing overhead of commercial APM tools.