Grafana vs Kibana: data sources, investigations, and deployment tradeoffs

An order service slows down. Its metrics live in Prometheus, its logs in Elasticsearch, and the on-call engineer has two browser tabs open. Choosing between Grafana and Kibana starts with a practical question: how do you get from the latency spike to evidence that explains it?

Grafana fits teams that want a shared investigation interface over several existing backends. Kibana fits teams whose data and investigation workflows already center on Elasticsearch and Elastic applications. They can share Elasticsearch, and keeping both can be sensible. Grafana is not limited to metrics, nor is Kibana limited to searching logs.

This comparison covers Grafana's visualization and query layer and Kibana's role in the Elastic Stack, with hosted services discussed separately. Documentation was checked on October 21, 2026. The scenarios illustrate design choices; they are not performance benchmarks.

Start with the data behind the charts

Grafana's Elasticsearch data source supports metrics and log queries. An Elasticsearch deployment does not force a choice of Kibana, but changing the interface does not transfer responsibility for Elasticsearch capacity, mappings, or backups.

Prometheus: querying a backend versus ingesting another copy

Grafana's built-in Prometheus data source calls the query API directly. Existing PromQL and recording rules can remain part of the same metrics system; displaying the data does not require moving it first.

The usual Kibana path is different. Elastic's Prometheus integration can collect exporter metrics, receive Prometheus remote_write, or collect results through PromQL queries. These paths ingest metrics into Elastic. They do not give Kibana the same external Prometheus query panel as Grafana, and an arbitrary Elasticsearch HTTP endpoint is not a remote_write receiver.

Duplicating metrics solely to recreate a dashboard adds decisions about collection intervals, mappings, retention, and cost. A second copy may be worthwhile when the team wants metrics inside its existing Elastic analysis workflow. The useful question is which investigation step that copy enables, rather than how many browser tabs it removes.

Follow an order timeout through the tools

Suppose Prometheus shows rising order latency and Elasticsearch holds the service logs. In Grafana, an engineer can narrow the time window on a latency panel and follow a configured data link to the relevant logs. The link should carry service, environment, and time context. A Trace ID can also point to the appropriate tracing backend.

Field differences often interrupt this path. Metrics might use service="orders" while logs identify only a container; one system may record milliseconds while another displays seconds. Adjacent charts do not fix those differences. Consistent service and environment identifiers, followed by matching request identifiers in logs and traces, make correlation useful.

In Kibana, the engineer can filter Discover by time, service, version, and error fields, then use Lens to examine which instances are affected. With Elastic APM data available, the investigation can continue into failed requests and downstream calls. That path uses Elastic's data and application model, but still depends on ingestion, mappings, and permissions. Log search alone cannot reconstruct a complete distributed request.

A matching chart does not establish matching queries or alerts

Grafana preserves the query language of each source. PromQL, LogQL, and SQL do not acquire identical semantics by appearing on one dashboard. Kibana's KQL is a filtering language: it does not aggregate, transform, or sort results. Use Lens, ES|QL, or another suitable Elasticsearch mechanism for those tasks.

When migrating a payment failure chart, compare denominators, time windows, null handling, and retries before colors or legends. Two charts can both display 2% while one counts requests and the other counts log records.

Alerts need a separate check. Grafana-managed rules run on the backend and require a supported data source plugin. A browser-side panel transformation or dashboard variable is not evidence that the same calculation works in a rule. Design no-data, query-error, and recovery behavior explicitly.

Kibana alerting also has deployment and subscription boundaries for rules and connectors. Classic Alerting and Universal Alerting have distinct documentation; configuration must match the system in use. Evaluation intervals depend on task capacity rather than promising strict real-time execution. In a shared deployment, test access using an ordinary role: an administrator opening a dashboard does not demonstrate data isolation.

Licensing and cost depend on the distribution

Grafana OSS uses AGPLv3; Enterprise and Cloud have separate commercial terms. Elastic's history needs more precision than a blanket statement about version 7.11. In 2021, 7.11 moved to SSPL/ELv2. In 2024, Elastic added an AGPLv3 option for the free portions of Elasticsearch and Kibana source code. The default distribution remains under ELv2. Source licensing and paid feature entitlements are separate questions: check the actual release, file licenses, and subscription matrix. Elastic licensing FAQ.

Include the data systems in the cost comparison. Self-hosted Grafana needs metrics, log, and trace backend capacity and operations. A self-managed Elastic Stack needs Elasticsearch capacity and any required subscription. For hosted services, compare the relevant Grafana Cloud, Elastic Cloud Hosted, or Serverless usage and retention terms. A free interface is not equivalent to an entire managed platform.

A useful trial uses the same data, retention window, queries, and alerts, then tests a source outage and access through a read-only role. Include backups, upgrades, commercial plugins, and cross-region traffic. Without those inputs, neither a price winner nor a performance winner is established.

Which should you choose?

Choose Grafana when several backends already work well and the missing piece is shared visualization and investigation. Consistent fields, links, backend rules, and permissions can matter more than moving data.

Choose Kibana when Elasticsearch is already the center of analysis and Elastic applications are part of the workflow. Queries, mappings, permissions, and alerts are engineering assets worth accounting for.

If both needs exist, sharing Elasticsearch and keeping both interfaces is a reasonable option. Preserve the path that lets an on-call engineer finish an investigation, even if it uses more than one tool.