Grafana vs Kibana (2026): Dashboards, Data Sources, and Which to Pick

Grafana vs Kibana comes down to one sentence: pick Grafana if you need dashboards that read from many backends at once with metrics and alerting at the center, and pick Kibana if your data already lives in Elasticsearch and deep log search is the daily job. They look similar from a distance - both build dashboards and charts on top of your telemetry - but they come from opposite directions, and the right choice depends almost entirely on where your data already lives and what you mainly do with it.

This guide keeps it tight: just Grafana vs Kibana, the metrics-led backend-agnostic tool versus the Elasticsearch-native one. If you are still sorting out the storage layer underneath, our Prometheus vs VictoriaMetrics comparison covers the metrics backend that most Grafana setups sit on top of.

The 2026 verdict

Grafana wins for most teams that need one operational view, because it reads from every backend you already run and treats metrics and alerting as first-class rather than as an add-on. Kibana wins when Elasticsearch is your storage layer, because nothing explores Elastic-indexed logs faster than Discover and ES|QL. And if you already run both an Elastic cluster and a Prometheus stack, the honest verdict is run both and give each the job it is actually good at. That is the most common production setup, not a failure to decide.

Two things moved recently that make older comparisons misleading:

• The licensing argument is basically over. Grafana has been AGPLv3 since April 2021, when Grafana, Loki, and Tempo were relicensed from Apache 2.0. Elastic then added AGPLv3 as a third option for the Elasticsearch and Kibana source code in August 2024, alongside Elastic License 2.0 and SSPL. Kibana source is OSI-approved open source again, so “Grafana is open source and Kibana is not” is no longer a tiebreaker. The default Elastic binary distribution still ships under Elastic License 2.0, which matters only if you plan to redistribute or resell.
• Both sides are absorbing OpenTelemetry and each other’s strengths. Grafana Alloy is now a fully compliant OpenTelemetry Collector distribution, and Grafana 13 ships OpenTelemetry packages that install on Linux in one command plus Kubernetes support through the OpenTelemetry Operator. From the other direction, Elastic 9.4 added native Prometheus and PromQL support in technical preview, so you can ship Prometheus metrics into Elasticsearch and run existing PromQL queries in Kibana. The convergence means data gravity, not the feature checklist, is the real deciding factor.

If you have 30 seconds: mixed backends and metrics-led operations, take Grafana. Elasticsearch-native logs, search, and security analytics, take Kibana. Both already in the building, run both.

The short answer

If you only read one section, read this. It is self-contained.

Pick Grafana if:

• Your telemetry is spread across multiple backends - Prometheus, Loki, Elasticsearch, InfluxDB, SQL, Tempo, cloud providers - and you want one dashboard tool that reads all of them.
• Metrics dashboards and alerting are your center of gravity.
• You want a vendor-neutral, data-source-agnostic layer that does not lock you into one storage engine.

Pick Kibana if:

• Your data already lives in Elasticsearch and you want the deepest integration available.
• Log exploration and full-text search are the daily job - Discover, ad-hoc queries, drilling into raw documents.
• You are invested in the Elastic Stack (Beats, Logstash, Elastic APM, Elastic Security) and want one cohesive UI for all of it.

Use both when: you want Kibana for Elasticsearch log search and Grafana for unified metrics dashboards across the rest of your stack. This is one of the most common production setups, not a compromise.

Most of the decision is about data gravity. Grafana goes to your data wherever it is; Kibana assumes your data is in Elasticsearch and rewards that assumption with depth.

Deciding factor to pick

The rule: pick by where your data lives and what you do with it - Grafana for cross-source metrics dashboards, Kibana for Elasticsearch-native log and search analytics.

Use-case decision matrix

The abstract “deciding factor” table is fine for a shortlist, but most people arrive here with a specific job in mind. Find your job, take the pick.

The pattern in this table is worth naming: Grafana wins the “put it all on one screen” jobs and Kibana wins the “dig into what happened” jobs. If your day is mostly watching, Grafana. If your day is mostly investigating documents, Kibana.

What each tool is

Grafana (from Grafana Labs) is an open-source, data-source-agnostic visualization and dashboarding tool. It stores no telemetry of its own - instead it connects to dozens of backends (Prometheus, Loki, Elasticsearch, InfluxDB, SQL databases, Tempo, and the major cloud monitoring services) and renders dashboards, panels, and alerts on top of them. Its strength is unifying metrics, logs, and traces from a mixed stack into a single operational view, with mature alerting built in. Because it owns no datastore, Grafana is the neutral pane of glass you can point at almost anything.

Kibana (from Elastic) is the visualization and exploration UI for the Elastic Stack, tightly coupled to Elasticsearch. It is where you run Discover for ad-hoc log search, build dashboards and visualizations, and use Elastic-specific surfaces like APM, Maps, and Security. Its strength is full-text search and log exploration over data indexed in Elasticsearch - when your logs are in Elastic, nothing explores them better. The trade-off is that Kibana is not a general-purpose, multi-backend tool; it is the front door to Elasticsearch.

Grafana vs Kibana: head-to-head

The pattern is clear: Grafana wins on breadth and metrics dashboards across many sources; Kibana wins on Elasticsearch-native log exploration and full-text search. Neither is strictly better - they optimize for different starting points.

When to choose Grafana

Grafana is the right call when dashboards need to span more than one backend and metrics lead the way. Concretely:

• You run a mixed stack and want one tool reading Prometheus, Loki, Elasticsearch, and cloud sources side by side.
• Metrics dashboards and alerting are your daily driver, and you want alerts that work across sources.
• You want to correlate metrics, logs, and traces in a single view - for example Prometheus metrics next to Loki logs and Tempo traces.
• You value a vendor-neutral layer that does not couple your dashboards to one storage engine.
• You want a large library of community dashboards and plugins to start from.
• You are standardizing on open-source observability and want an AGPLv3 tool you can self-host freely.

The trade-off you are accepting: Grafana is only as good as the sources behind it for deep log search, and it does not give you Elasticsearch’s full-text Discover experience out of the box.

When to choose Kibana

Kibana is the right call when your data lives in Elasticsearch and search is the job. Concretely:

• You are running the Elastic Stack and want one cohesive UI for logs, APM, and security.
• Log exploration is central - engineers spend their day in Discover, drilling into raw documents and refining queries.
• You need full-text search at scale over indexed logs, which is exactly what Elasticsearch is built for.
• You use Elastic-specific surfaces like Elastic APM, Maps, or Elastic Security and want them in the same pane.
• You rely on Elasticsearch features such as Index Lifecycle Management and want the UI that manages them.
• Your ingestion is already built on Beats and Logstash, and Kibana completes that pipeline.

The trade-off you are accepting: Kibana is tied to Elasticsearch, so it is not the tool for unifying dashboards across non-Elastic backends, and metrics-first workflows feel more natural in Grafana.

Can you use them together?

Yes, and it is a very common, healthy setup. The realistic combined pattern looks like this: Kibana handles Elasticsearch log exploration - Discover, full-text search, drilling into raw events during an incident - while Grafana provides the unified operational dashboards that span Prometheus metrics, Loki logs, Tempo traces, and cloud sources. Grafana can also read from the same Elasticsearch cluster as one of its data sources, so a single Grafana dashboard can blend Elastic logs with Prometheus metrics when you want them side by side.

In practice teams reach for Kibana when they need to search and investigate logs, and Grafana when they need a single-pane view of system health across the whole stack. They complement rather than compete.

A split that works in production

If you decide to run Grafana and Kibana together, draw the line by job, not by data type, and write it down so people stop arguing about it during incidents:

1. Grafana owns the on-call surface. Every alert, every SLO board, every “is the system healthy” dashboard lives in Grafana, sourced from Prometheus, Loki, Tempo, and cloud metrics. On-call engineers should only ever need one bookmark.
2. Kibana owns investigation. Once an alert fires and the responder needs to read raw documents, they jump into Discover with the trace ID or request ID already in hand.
3. Grafana reads Elasticsearch too. Add your Elastic cluster as a Grafana data source so a single incident dashboard can show Prometheus error rates next to a count of matching Elastic log documents. You are not duplicating storage, only querying it from a second front end.
4. Deep links connect the two. Put a Grafana data link on your log panels that opens the equivalent Kibana Discover query with the time range and filters carried across. This is the single highest-value hour of setup work in a dual-tool stack.
5. One alerting system, not two. Pick Grafana Alerting or Elastic alerting and route everything through it. Two alerting engines means two escalation paths and two places to silence a page at 3am.

What running both actually costs

The licenses are free, so the real cost of a dual-tool setup is human. Budget for two upgrade cycles, two sets of access controls and SSO integrations, two dashboard-sprawl problems, and two query languages your team has to stay fluent in (PromQL and LogQL on one side, ES|QL and KQL on the other). That is a fair trade when both tools have a genuine job. It is pure overhead when one of them is only there because nobody turned it off.

When running both is a mistake

Skip the dual setup if all your telemetry already lands in Elasticsearch and you have no Prometheus stack, because Grafana adds a second UI without adding a second capability. Skip it equally if you are all-in on Prometheus and Loki and someone is proposing an Elasticsearch cluster purely to get Discover, since that is a heavy storage commitment to buy one workflow. And never run both as a migration that quietly stalls halfway, with half the dashboards in each tool and no one sure which is authoritative. Pick a direction, set a date, and finish it.

Cost comparison

Both tools have free, self-hostable cores, so the meaningful comparison is the model, not a price tag.

Grafana: open-source and free to self-host. The Grafana server is open-source under AGPLv3, so you can run it yourself at no license cost. Grafana Cloud adds a managed option with a free tier and usage-based paid plans for hosted metrics, logs, and traces. Because Grafana stores nothing itself, your real spend is the backends underneath it (Prometheus, Loki, Elasticsearch) plus, optionally, Grafana Cloud or Enterprise features.

Kibana: free with the Elastic Stack. Kibana ships as part of the Elastic Stack and is free to self-host under the Elastic License v2 / SSPL, with AGPLv3 available as a third source-code option since August 2024. Paid Elastic tiers and Elastic Cloud unlock advanced capabilities such as enhanced security, alerting, and machine learning. As with Grafana, the dominant cost is usually the storage and ingest layer - running and scaling Elasticsearch - rather than the visualization tool.

The honest takeaway: neither dashboard tool is where the money goes. Pick based on fit, and budget for the storage and ingest engine underneath, because that is what actually scales with your data volume.

Where each project stands in 2026

Comparisons of these two age badly, because both projects shipped major versions in 2026 that moved the goalposts. Here is the current state of play.

Grafana. The stable line is Grafana 13, launched at GrafanaCON 2026 in Barcelona in April, with 13.2.0 out in August 2026. The headline changes are aimed squarely at the blank-page problem that kills most Grafana rollouts: suggested dashboards, dashboard layout templates built around established methodologies like DORA and USE/RED, dynamic dashboards now generally available so one board adapts to variables and context instead of spawning twenty near-identical copies, and Git Sync generally available so dashboards live in version control. Grafana Advisor flags configuration problems, and Grafana Assistant brings an AI agent into dashboard and query building. Underneath, Grafana Labs shipped a next-generation Loki architecture alongside the release, with Loki itself on the 3.6.x line. Licensing is unchanged at AGPLv3.

Kibana and the Elastic Stack. The current line is Elastic Stack 9.x, with 9.5.2 released in August 2026. Kibana 9.0 brought a redesigned UI theme with a better dark mode, and the strategic shift across the 9.x line is ES|QL, Elastic’s newer piped query language, which now covers lookup joins, subqueries, and inline highlighting in Discover. Elastic 9.4 added the change most relevant to this comparison: native Prometheus and PromQL support in technical preview, meaning you can ship Prometheus metrics straight into Elasticsearch and run your existing PromQL queries and alert rules inside Kibana without rewriting them. Elastic 9.5 continued with Agent Builder, Workflows, and further dashboard work.

What this means for the choice. Kibana is pushing into metrics territory that used to be automatically Grafana’s, and Grafana is pushing into standardized OpenTelemetry collection that used to require vendor agents. Neither has taken the other’s home turf yet: Grafana still cannot match Discover for raw document exploration, and Kibana still cannot pull a dozen unrelated backends into one board. But if you evaluated these two more than a year ago and ruled one out on licensing or on metrics support, the evaluation is worth redoing.

Common pitfalls

• Treating them as interchangeable. Grafana is a multi-backend metrics-first dashboard layer; Kibana is an Elasticsearch-native search and log tool. Choosing on looks alone leads to a poor fit.
• Expecting Grafana to match Kibana’s search. Grafana can query Elasticsearch, but it does not replicate Discover and Elastic’s full-text exploration. If deep log search is the job, do not force Grafana into it.
• Expecting Kibana to unify non-Elastic sources. Kibana is built around Elasticsearch. Pointing it at a mixed stack of Prometheus and cloud metrics is not its strength.
• Forgetting that Grafana stores no data. A Grafana dashboard is only as healthy as the backends behind it. Provision and scale those storage engines deliberately.
• Underestimating the storage layer’s cost and ops. The visualization tool is free or cheap; running and scaling Elasticsearch or your metrics store is the real commitment. Plan for it up front.
• Deciding on a stale licensing picture. Plenty of internal comparison docs still say Kibana is not open source. Elastic added an OSI-approved AGPLv3 option for the Elasticsearch and Kibana source in August 2024. If your architecture review rejected Elastic on license grounds before then, that objection no longer holds.
• Running both without drawing the line. Two dashboard tools with overlapping dashboards and two alerting engines is worse than either one alone. Decide which tool owns on-call and which owns investigation, wire deep links between them, and keep alerting in exactly one place.

Related reading

• Jaeger vs Tempo - distributed tracing backends, both of which Grafana can visualize alongside your metrics and logs.
• Prometheus vs VictoriaMetrics - the metrics storage layer that most Grafana dashboards sit on top of.

Getting help

Choosing a dashboard tool is the easy part; wiring it into an observability stack that actually surfaces your real bottlenecks is where teams get stuck. We run vendor-neutral performance and observability engagements - baselining your stack, instrumenting the right metrics, logs, and traces, and building the dashboards your team will actually use, whether that is Grafana, Kibana, or both. If you want a clear-eyed read on what to monitor and how, we can help you scope it. Book a free scope call.