Grafana LGTM Stack

The LGTM stack is Grafana Labs' full observability suite: Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics. It extends the popular PLG pattern with distributed tracing (Tempo) and horizontally scalable, long-term metrics storage (Mimir). Together the components cover all three pillars of observability — metrics, logs, and traces — under one open-source, Grafana-centric platform with strong cross-signal correlation.

Components

• Loki: label-indexed log aggregation that is cheap to operate and shares the Prometheus-style label model for easy correlation.
• Grafana: the unified dashboards, exploration, alerting, and correlation UI across all three signals and many external data sources.
• Tempo: a cost-efficient distributed tracing backend that stores traces in object storage with no full index, linking them to logs and metrics by trace ID.
• Mimir: a horizontally scalable, highly available, long-term store for Prometheus metrics that removes Prometheus's single-node and retention limits.
• OpenTelemetry / agents: the Grafana Agent (Alloy) and OpenTelemetry collectors ingest and route metrics, logs, and traces into the backends.

Strengths

LGTM covers all three pillars of observability in one cohesive, open-source platform with deep correlation: you can jump from a metric spike to the related traces and then to the underlying logs using shared IDs and labels, which makes root-cause analysis fast. Tempo and Loki both store data in cheap object storage, and Mimir scales metrics to billions of active series with high availability and long retention. Grafana unifies the experience and embraces OpenTelemetry, reducing agent and vendor lock-in. Managed Grafana Cloud is available for teams that prefer not to self-host the components.

Trade-offs

Running Loki, Tempo, and Mimir at scale means operating several distributed systems, each with its own object storage, components, and tuning — significant complexity for self-hosters. Trace and log search are less full-text-rich than Elasticsearch, favoring known-ID lookup and correlation over arbitrary attribute search. Getting correlation right depends on consistent instrumentation and disciplined labeling. For small environments, the full LGTM suite is heavier than basic PLG and may not be justified.

When to Use It

Choose the Grafana LGTM stack when you need complete, correlated observability — metrics, logs, and traces — at scale on an open-source, OpenTelemetry-friendly foundation. It suits larger cloud-native platforms that have outgrown single-node Prometheus and want unified signal correlation across teams. Smaller teams may start with the PLG stack and add Tempo and Mimir incrementally as tracing needs and metric scale grow. Teams typically converge on it after running PLG for a while and finding they need tracing and durable, scalable metrics, at which point adopting the full suite avoids stitching together unrelated tools. Because it is several systems, adopting the components in stages — metrics, then logs, then traces — spreads the operational learning curve across time. Consistent instrumentation is the key to realizing its correlation benefits.