Grafana Labs has built over the last few years a complete observability stack that rivals classic options (Elasticsearch + Jaeger + Prometheus) and cloud offerings (Datadog, New Relic). Three open components: Loki for logs, Tempo for traces, Mimir for metrics. All with similar design: object-storage-backed, minimal indices, queries via Grafana.
The Common Pattern: Object Storage + Minimal Index
The shared innovation of the three projects is storing data in S3/GCS/Azure Blob buckets, with only a minimal index for lookup. This contrasts with:
- Elasticsearch: indexes all content, expensive in CPU and disk.
- Jaeger with Cassandra: requires heavy cluster.
- Prometheus TSDB: scales vertically, doesn’t share data across instances easily.
Advantage: S3 storage costs ~20x less than SSD with Elasticsearch indices. For low-query-rate data (old logs, historical traces), the economics are dramatic.
Loki for Logs
Loki doesn’t index log content, only labels (like {app="api", level="error"}). Searches inside the log happen by scanning — slow compared to Elasticsearch, but much cheaper.
Where Loki wins:
- High volume (TB/day) with occasional access.
- Teams already using Prometheus (same label model).
- Budgets where Elasticsearch doesn’t fit.
Where it doesn’t:
- Very frequent free-text searches (Elasticsearch still wins).
- Complex analytics over log content.
Tempo for Traces
Tempo stores traces without indexing them (only the traceID). To search by attribute (e.g., “all traces for /api/v1/orders”), you need a secondary index in Prometheus/Loki.
This may sound limiting, but it fits a common pattern: use metrics/logs to detect problems, use traces to diagnose. From a Grafana dashboard, a click takes you from metric to correlated trace.
Versus Jaeger: Tempo is cheaper to operate (no Cassandra), but Jaeger has a richer UI and more flexible searches.
Mimir for Metrics
Mimir is horizontal Prometheus: same PromQL, same data, but clusterable to billions of series. Uses the same object-storage + index pattern.
For a single Prometheus, it adds nothing. For multi-tenancy, high cardinality, or long retention (>1 year), Mimir is Grafana-Cloud-scale.
Similar alternatives: Thanos, Cortex, VictoriaMetrics. Mimir and Cortex share lineage (Mimir is Grafana Labs’ 2022 fork/refactor of Cortex). Thanos uses slightly different patterns for the same goal. VictoriaMetrics stands out on resource efficiency.
Integration with the Rest
The real value of the Grafana stack is cross-correlation:
- From a Grafana panel with an anomalous metric, click → related logs in Loki.
- From a log with an error, click → trace in Tempo.
- From a trace, click → metrics of the involved service.
Grafana Labs calls this “correlations” and it works well when the three signals share common labels (namespace, service, pod). With OpenTelemetry as unified SDK, this correlation is automatic.
When to Choose the Grafana Stack
Clear scenarios:
- Team already uses Grafana + Prometheus. Adding Loki and Tempo is natural evolution.
- Limited budget with medium-high volumes. Open-source stack + object storage is significantly cheaper than cloud solutions.
- Simple multi-tenancy. All three components support native tenant isolation.
Where to choose something else:
- Complex log analytics. Elasticsearch/Splunk still hold an edge.
- Datadog ecosystem productivity integration. If your team already lives in Datadog, switching just for open-source rarely pays off.
- Team without operational experience with object storage. Efficiency comes from knowing how to tune S3/GCS lifecycle policies, sampling, retention.
Also see how to apply SRE principles without being Google — the tech stack follows practice, not the reverse.
Conclusion
The Grafana stack (Loki + Tempo + Mimir) offers open-source observability with favorable economics at scale, thanks to the common pattern of object storage + minimal index. For cost-sensitive teams or those valuing full stack control, it’s a legitimate alternative to the Elasticsearch + Jaeger duo and to proprietary cloud solutions.
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