Building Unified Observability with the LGTM Stack

Grafana
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Introduction: Beyond monitoring

Observability goes further than monitoring. It’s not just about collecting metrics — it’s about understanding, predicting, and solving system issues by combining metrics, logs, and traces.

To achieve this, we implemented the LGTM stack (Loki, Grafana, Tempo, Mimir).

LGTM stack components

lgtm-arch

  1. Loki: Lightweight log aggregation, Kubernetes-friendly
  2. Grafana: Unified dashboards, alerting, visualization
  3. Tempo: Distributed tracing backend, scalable storage
  4. Mimir: Long-term scalable Prometheus storage

How it works together

  1. Prometheus scrapes metrics → RemoteWrite → Mimir
  2. Loki collects logs → Grafana integration
  3. Tempo stores tracing data → Grafana integration
  4. Grafana unifies all signals into a single observability experience

Benefits

  • Remove single points of failure: Prometheus becomes HA with Mimir
  • Long-term retention with stable performance
  • Unified triage: Investigate latency spikes with metrics + logs + traces in one view
  • Cost efficiency: Open-source alternative to SaaS observability platforms

Why we chose Mimir

Among all Prometheus extensions, we selected Mimir because:

  • It scales with our growing workload
  • It integrates seamlessly into LGTM
  • It enables a unified observability workflow

https://github.com/yieon-lyon/lgtm-sample

Conclusion

Observability is about making your system transparent and trustworthy. The LGTM stack builds on Prometheus’ foundation and removes its weaknesses by combining scalable metrics, logs, and tracing into one cohesive solution.

What’s your observability stack today?

  • ELK?
  • Datadog / NewRelic?
  • Or open-source LGTM?
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