Grafana vs Kibana — Which Dashboard Tool

I've watched three teams end up running Grafana and Kibana side by side, paying for both, and not one of them planned it that way. It happens because the two tools answer questions people assume are the same question. They aren't. Grafana asks "what is this number doing over time, across all my systems?" Kibana asks "what happened, and can I dig through the logs to find out?" Pick based on which of those you ask at 3am, and most of the confusion goes away.

Here's how we actually decide.

Data sources are the whole story#

This is the fork in the road, so start here.

Grafana is source-agnostic by design. It ships with dozens of data source plugins and treats each one as a pluggable backend: Prometheus for metrics, Loki for logs, Tempo for traces, plus Postgres, MySQL, InfluxDB, CloudWatch, Graphite, and Elasticsearch itself. A single Grafana dashboard can put a Prometheus panel next to a CloudWatch panel next to a raw SQL query. That flexibility is the reason people reach for it.

Kibana is not agnostic. It is the front end for Elasticsearch, full stop. If your data is in Elasticsearch (or OpenSearch, with OpenSearch Dashboards, the forked equivalent), Kibana is the deepest, most fluent way to work with it. If your data lives anywhere else, Kibana has nothing to say. That sounds like a limitation, and it is, but it also means Kibana knows its one backend better than any general tool could.

So the first thing I ask a team: where does the data you care about already live? If the honest answer is "metrics in Prometheus, logs somewhere else," Grafana is the natural hub. If it's "everything's already in an Elastic cluster," you may not need Grafana at all.

What each one is actually good at#

Grafana is a time-series dashboard tool first. Its home turf is the wall of graphs: request rates, latency percentiles, CPU, error budgets, all refreshing on an interval. Give it a metrics store and it renders faster and cleaner than anything else I've used.

Kibana is a log search and exploration tool first. Discover, its core view, is built for the moment when something broke and you need to grep through millions of structured events, filter by field, and spot the pattern. For log analytics and free-text search over large document sets, it's excellent, because Elasticsearch underneath is a search engine, not a metrics database.

The overlap is real but shallow. Grafana can show logs through Loki and Kibana can chart metrics, but each is doing its weaker job when it strays.

Query languages#

You'll live in these, so they matter.

With Grafana you mostly write the query language of whatever backend you attached: PromQL for Prometheus metrics, LogQL for Loki logs, SQL for the databases. Grafana adds template variables and transformations on top, but it doesn't invent one universal language. You learn the source's language.

Kibana gives you KQL and the older Lucene syntax for quick filtering, the classic Elasticsearch Query DSL (JSON) for anything complex, and ES|QL, Elastic's newer piped query language that's genuinely nice for shaping and aggregating results in one expression. All of it is Elasticsearch-flavored, which is the point.

Neither is harder. They're aimed at different data shapes: PromQL thinks in time series, KQL and ES|QL think in documents and fields.

Visualization and alerting#

On visualization breadth Grafana wins on raw count and polish. Time-series panels, heatmaps, geomaps, state timelines, and a large community panel ecosystem. Kibana's Lens and TSVB are strong and its Maps feature is very good, but the catalog is narrower and tuned to Elastic data.

Alerting is closer than people expect. Grafana has a unified alerting system that can fire on any data source and route through its own notification layer to Slack, PagerDuty, and the rest. Kibana's alerting lives in the broader Elastic stack (rules, connectors, and in paid tiers, machine-learning anomaly detection). If you want one alerting brain across mixed backends, Grafana is the cleaner fit. If your alerts are all Elastic queries anyway, Kibana's is right there.

The comparison at a glance#

Dimension	Grafana	Kibana
Data sources	Many (Prometheus, Loki, SQL, cloud, ES)	Elasticsearch / OpenSearch only
Primary job	Metrics & time-series dashboards	Log search, exploration, analytics
Query languages	PromQL, LogQL, SQL, per-source	KQL, Lucene, Query DSL, ES|QL
Visualization breadth	Very broad, big plugin ecosystem	Solid, tuned to Elastic data
Alerting	Unified, any source	Elastic stack rules & connectors
License	AGPLv3 (OSS core)	Elastic License / SSPL
OSS fork	Grafana itself is open	OpenSearch Dashboards (Apache 2.0)

Licensing, because it bites people#

Grafana's core is AGPLv3. Open, but the AGPL copyleft terms matter if you plan to build a hosted product around it, so read them before you embed it.

Kibana moved off Apache 2.0 years back and now ships under the Elastic License and SSPL. Usable for most internal cases, restrictive if you want to offer it as a managed service. That license change is exactly why the OpenSearch Dashboards fork exists, staying on Apache 2.0. If a permissive license is non-negotiable, that fork is your Kibana-shaped answer.

When you'd run both#

Sometimes running both is the correct call, not an accident. A common pattern: Kibana for deep log investigation on an existing Elastic cluster, Grafana as the single pane of glass stitching those logs together with Prometheus metrics and cloud dashboards for the on-call view. The logs stay where they're searchable, and the metrics story lives in one place.

If you're mapping out a wider stack, our roundup of observability tools covers where these two fit alongside tracing and APM.

The trap is running both by default because nobody decided. Two dashboards, two alerting configs, two things to keep patched, and engineers guessing which tab to open. If you can't name why each one exists, you don't need both yet.

The call we'd make#

Reach for Grafana when your metrics and logs come from more than one backend and you want a single dashboard and alerting layer over all of it. Reach for Kibana (or OpenSearch Dashboards, if the license matters) when your data already lives in Elasticsearch and log search is the job you're doing most. Run both only when you can say out loud what each is for. Everything else is just two bills for one answer.