Grafana vs Kibana: Dashboards for Metrics vs Logs
Grafana vs Kibana 2026 — metrics vs log dashboards, datasource support, query languages, pricing, and which visualization platform fits your observability stack.
Quick Answer
Grafana wins as the general-purpose observability dashboard: it unifies metrics, logs, and traces from 150+ datasources. Kibana wins within the ELK/Elastic stack: its log analysis, KQL search, and Elastic Security/APM integrations are unmatched when you are already on Elasticsearch. Choose Grafana for multi-source observability; choose Kibana if Elasticsearch is your primary data store.
Grafana vs Kibana: Overview
Multi-source observability, Prometheus + Loki + Tempo stacks, infrastructure dashboards
OSS (AGPL 3.0) free forever; Grafana Cloud: 10K series + 50GB logs free
Grafana Cloud Pro: $8/month; Enterprise: custom pricing
ELK stack log analysis, Elastic APM, Elastic Security SIEM, full-text log search
Basic tier free (includes most dashboards, Discover, APM); Elastic Cloud: $95/month minimum
Elastic Cloud from $95/month (Platinum for ML features); self-hosted free for basic
Grafana vs Kibana: Feature Comparison
| Feature | Grafana | Kibana |
|---|---|---|
| Primary data source | Any of 150+ (Prometheus, Loki, MySQL, etc.) | Elasticsearch only |
| Full-text log search | LogQL label-filter (weaker for grep) | KQL/Lucene full-text (industry-leading) |
| APM / distributed tracing | Tempo (separate service) | Built-in Elastic APM |
| Security / SIEM | None in OSS | Elastic Security (Platinum) |
| Log storage cost (10GB/day) | ~$50–80/month (Loki) | ~$400–600/month (Elastic Cloud) |
| Multi-source dashboard | Yes (150+ datasources in one dashboard) | No (Elasticsearch only) |
Pros & Cons
Grafana
Pros
- 150+ datasource plugins: one dashboard spans Prometheus, Loki, MySQL, Elasticsearch, Cloudwatch simultaneously
- Loki: Grafana's log aggregation is 10x cheaper than Elasticsearch at scale — label-based indexing, no full-text index
- Tempo integration: click a log line in Loki → jump to the trace in Tempo — correlated log-trace-metric in one UI
- Alerting engine: Grafana 10 unified alerts work across all datasources with one rule engine and notification policy
- Terraform provider: all dashboards, datasources, and alerts are manageable as code with grafana/grafana Terraform
Cons
- Log search is weaker than Kibana: LogQL is label-filter-based — Kibana's KQL full-text search is faster for arbitrary log grep
- No built-in log ingestion: Loki requires a separate promtail/alloy agent — Kibana's Beats ecosystem is more mature
- Security analytics: no equivalent to Elastic Security SIEM — Grafana has no threat detection or ML anomaly in OSS tier
- Enterprise lock: RBAC and fine-grained dashboard permissions require Grafana Enterprise — OSS has only org-level access
Kibana
Pros
- KQL full-text search: Kibana Discover lets you grep across billions of log lines with sub-second Lucene queries
- Elastic APM: distributed tracing, service maps, error grouping integrated natively — no extra tool needed
- Elastic Security: built-in SIEM with detection rules, threat intel, ML-based anomaly detection (Platinum tier)
- Canvas + Lens: Kibana Lens auto-suggests chart types from field types — fastest path from data to visualization
- Elasticsearch ML: anomaly detection jobs on log data available directly in Kibana Platinum — no external tool
Cons
- Elasticsearch dependency: Kibana is useless without Elasticsearch — no support for Prometheus, MySQL, or other sources
- Resource-heavy: Elasticsearch + Kibana requires 8GB+ RAM minimum for production — 3–5x Loki + Grafana resource cost
- Licensing complexity: Elastic shifted to SSPL in 2021; AWS forked to OpenSearch — commercial Kibana features restricted
- Cost at scale: Elastic Cloud log ingestion at 10GB/day costs ~$400–600/month; Loki equivalent is ~$50–80/month
Our Verdict: Grafana vs Kibana
Use Grafana if you are building a general observability stack — especially with Prometheus for metrics and Loki for logs. Grafana's multi-source support and dramatically lower log storage cost (Loki vs Elasticsearch) make it the better default. Use Kibana if you are already running Elasticsearch for log ingestion, need Elastic APM's service maps, or require Elastic Security SIEM — the full-text search and APM integration justify the higher resource cost. Never run Kibana without Elasticsearch; never expect Grafana to replace Kibana's grep-style log analysis.
Grafana vs Kibana — FAQs
Can Grafana replace Kibana for log analysis?
Partially. Grafana + Loki handles structured log analysis (filtering by labels, service, level) very well and at 10x lower cost. But for unstructured full-text log search — where you grep arbitrary strings across billions of lines — Kibana's KQL/Lucene is significantly faster and more powerful. If your log analysis workflow involves "find all lines containing exception XYZ across all services," Kibana wins. If it involves "show error rate for service X in the last hour," Grafana + Loki is sufficient and much cheaper.
Is the ELK stack (Elasticsearch + Logstash + Kibana) still the standard in 2026?
ELK is still widely deployed in enterprises but has lost ground to alternatives. Elastic's SSPL relicense in 2021 prompted AWS to fork into OpenSearch (with OpenSearch Dashboards as the Kibana fork), fragmenting the ecosystem. The PLG stack (Prometheus + Loki + Grafana) has become the Kubernetes-native default because Loki is 10x cheaper to operate than Elasticsearch at log scale. ELK remains the standard in security operations (SIEM) and compliance-heavy environments where Elastic's ML anomaly detection and Elastic Security are required.
What is the difference between Grafana Loki and Elasticsearch for logs?
Elasticsearch indexes every word in every log line (full-text index) — this enables fast arbitrary-string search but is expensive (8GB+ RAM, 3–10x storage overhead). Loki indexes only labels (service, environment, pod) and stores log chunks compressed in object storage — this is 10x cheaper but means you must filter by labels first, then grep within those streams. The right choice depends on query patterns: security teams that grep for IOCs (indicators of compromise) need Elasticsearch; SRE teams that query by service+level+time range do fine with Loki.
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