Grafana Stack vs SigNoz: Choosing an OpenTelemetry-Native Observability Platform

Both the Grafana LGTM stack and SigNoz give you metrics, traces, and logs in one UI, and both ingest OpenTelemetry natively. The real difference is architectural: Grafana is a composable stack of four specialized stores you assemble and operate, while SigNoz is a single application on top of one columnar database. That choice drives everything else: operational cost, query flexibility, scaling model, and how many engineers you need to keep it running.

Architecture Comparison

Dimension	Grafana LGTM Stack	SigNoz
Metrics store	Mimir (or Prometheus)	ClickHouse
Traces store	Tempo	ClickHouse
Logs store	Loki	ClickHouse
Visualization	Grafana	Built-in UI (or query ClickHouse directly)
Components to operate	4+ backends plus object storage	OTel Collector + ClickHouse + query service
Query languages	PromQL, LogQL, TraceQL	ClickHouse SQL + query builder
License	AGPL (OSS), Grafana Cloud	MIT (community), SigNoz Cloud/Enterprise

Grafana LGTM: Composable, Specialized Stores

Each signal gets a purpose-built store, each optimized for its workload:

• Mimir: horizontally scalable Prometheus-compatible metrics, multi-tenant, object-storage backed
• Loki: log aggregation indexing only metadata (labels), not log content, keeping storage cheap
• Tempo: trace storage keyed by trace ID, object-storage backed, no indexing of spans
• Grafana: the query and visualization layer tying them together

A minimal self-hosted deployment with Docker Compose:

# docker-compose.yml (simplified)
services:
  mimir:
    image: grafana/mimir:2.14.0
    command: ["-target=all", "-auth.multitenancy-enabled=false"]
    volumes:
      - ./mimir.yaml:/etc/mimir/mimir.yaml

  loki:
    image: grafana/loki:3.2.0
    command: ["-config.file=/etc/loki/loki.yaml"]
    volumes:
      - ./loki.yaml:/etc/loki/loki.yaml

  tempo:
    image: grafana/tempo:2.6.0
    command: ["-config.file=/etc/tempo/tempo.yaml"]
    volumes:
      - ./tempo.yaml:/etc/tempo/tempo.yaml

  grafana:
    image: grafana/grafana:11.3.0
    ports:
      - "3000:3000"
    environment:
      - GF_AUTH_ANONYMOUS_ENABLED=true

Each backend needs its own config file, its own storage tuning, and its own scaling plan. In production, all three stores want object storage (S3/GCS) as their persistence layer.

The strength of this model: each store scales independently, and the ecosystem is enormous. Every exporter, dashboard, and alerting rule template assumes Prometheus/Grafana first. The weakness: you are operating four distributed systems, and correlating signals means jumping between three query languages.

SigNoz: One Store, One Query Model

SigNoz stores all three signals in ClickHouse, a columnar OLAP database. The OTel Collector receives telemetry, the query service reads from ClickHouse, and the UI renders it:

# SigNoz core components (from the official deploy manifests)
services:
  otel-collector:
    image: signoz/signoz-otel-collector:0.111.0
    command: ["--config=/etc/otel-collector-config.yaml"]
    depends_on:
      - clickhouse

  clickhouse:
    image: clickhouse/clickhouse-server:24.1
    volumes:
      - clickhouse-data:/var/lib/clickhouse

  query-service:
    image: signoz/query-service:0.55.0
    environment:
      - ClickHouseUrl=tcp://clickhouse:9000

  frontend:
    image: signoz/frontend:0.55.0
    ports:
      - "3301:3301"

Because everything lands in one database, cross-signal queries are native. Want p99 latency grouped by the log level emitted during each request? That is one SQL query, not a dashboard with three data sources and fragile label-matching:

SELECT
    toStartOfMinute(timestamp) AS t,
    quantile(0.99)(durationNano) / 1e6 AS p99_ms
FROM signoz_traces.distributed_signoz_index_v3
WHERE serviceName = 'checkout'
    AND timestamp > now() - INTERVAL 1 HOUR
GROUP BY t
ORDER BY t;

The trade-off: ClickHouse is a serious database. At high ingest volumes you need to understand sharding, replication, TTL-based retention, and part merging. You trade "four simple-ish stores" for "one powerful store you must actually know."

Instrumentation: Identical, Thanks to OTel

Both platforms ingest via the OpenTelemetry Collector, so application instrumentation is the same. Point your SDK or collector at either backend:

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317

exporters:
  # For SigNoz: OTLP straight to its collector
  otlp/signoz:
    endpoint: signoz-otel-collector:4317
    tls:
      insecure: true

  # For Grafana: Prometheus remote write + OTLP to Tempo/Loki
  prometheusremotewrite:
    endpoint: http://mimir:9009/api/v1/push
  otlp/tempo:
    endpoint: tempo:4317
    tls:
      insecure: true

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [otlp/signoz] # or [otlp/tempo]
    metrics:
      receivers: [otlp]
      exporters: [otlp/signoz] # or [prometheusremotewrite]

This is the key strategic point: if you instrument with vendor-neutral OTel SDKs, switching platforms later is a collector config change, not a re-instrumentation project.

Query Experience

Grafana is the better dashboarding tool, full stop. A decade of ecosystem means prebuilt dashboards for nearly everything, powerful transformations, and alerting (Grafana Alerting or Mimir's ruler) that is battle-tested at scale. But you query each signal in its own language: PromQL for metrics, LogQL for logs, TraceQL for traces.

SigNoz has a solid query builder covering common cases, and anything the builder cannot express can be written as raw ClickHouse SQL. Correlation between signals (jump from a trace to its logs) is built in rather than configured via derived fields. Dashboarding and alerting are good but younger; complex alert routing is less mature than Grafana Alerting.

Operational Overhead and Cost

This is usually the deciding factor.

Grafana LGTM self-hosted: expect to run Mimir, Loki, Tempo, Grafana, plus object storage, plus (for HA) per-backend replication. Realistic production deployments run 10-20+ pods. The upside is cheap storage: Loki and Tempo lean on object storage, so retention is nearly free. Grafana Cloud removes the ops burden at usage-based pricing that grows with ingest.

SigNoz self-hosted: fewer moving parts, but ClickHouse storage is the cost driver. Columnar storage with compression is efficient (typically 10-20x compression on telemetry), but you are on block storage, not S3, so long retention costs more than Tempo/Loki's object-storage model. SigNoz does support S3-backed ClickHouse disks for cold data, which closes much of the gap if you configure tiered storage.

Rule of thumb from teams running both: below roughly 100 GB/day of telemetry, SigNoz is meaningfully simpler to operate. Above that, the answer depends on whether you have ClickHouse expertise in-house. If you do, SigNoz scales fine. If you do not, Grafana's object-storage backends are more forgiving.

Decision Framework

Choose Grafana LGTM when:

• You already run Prometheus and have years of PromQL dashboards and alerts
• You need the deepest dashboarding, alerting, and plugin ecosystem
• You want object-storage economics for long retention
• You have platform engineers to operate multiple backends (or you will pay for Grafana Cloud)

Choose SigNoz when:

• You are starting fresh with OpenTelemetry and want one platform, not four
• Cross-signal queries and trace-log correlation matter more than dashboard polish
• Your team is comfortable operating (or learning) ClickHouse
• You want a single UI purpose-built for OTel semantics (services, spans, exceptions) rather than a general-purpose dashboard tool

A pragmatic middle path many teams take: SigNoz for traces and APM workflows, Prometheus/Grafana for metrics and alerting. Since both speak OTel, this costs you nothing in instrumentation.

Migration Path

1. Instrument with OpenTelemetry SDKs (never vendor agents) so telemetry stays portable
2. Run both platforms in parallel: the OTel Collector's fan-out makes this trivial

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [otlp/signoz, otlp/tempo] # dual-write during evaluation

3. Rebuild your top 10 dashboards and all paging alerts on the target platform
4. Compare query performance and storage cost against real data for 2-4 weeks
5. Cut over alerting last, after on-call has validated the new alert paths

Checklist

1. Instrument with vendor-neutral OpenTelemetry, never proprietary agents
2. Inventory your dashboards and alerts before evaluating; they are the real migration cost
3. Estimate ingest volume (GB/day per signal) to model storage costs on both platforms
4. Assess in-house ClickHouse skills honestly before committing to SigNoz self-hosted
5. Dual-write during evaluation with the OTel Collector
6. Decide retention per signal before choosing a storage tier strategy

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