Grafana is a visualisation layer that connects to many backends — Prometheus, Loki, Postgres, Elasticsearch and dozens more — and is strongest at time-series dashboards and alerting. Kibana is the front end for Elasticsearch specifically, and is strongest at searching and exploring unstructured log data. Plenty of teams run both, because they are good at different questions.
These get compared as competitors and they overlap only partially. Grafana can query Elasticsearch and Kibana can draw a time-series chart, so each can do some of the other’s job — usually less well.
The useful framing is what question you are asking: “is this metric abnormal” or “what happened in this request”.
Table of contents
- The architectural difference
- Metrics and dashboards
- Logs, where the comparison inverts
- Alerting
- Cost, licensing, and running both
- How this fits the rest of the stack
- FAQ
The architectural difference
Grafana is storage-agnostic. It stores dashboards and alert rules; the data stays wherever it already lives. One dashboard can put a Prometheus metric next to a Postgres business query next to a Loki log volume, in the same view.
That is the core strength. Most organisations have telemetry in several places, and Grafana is a single pane over all of them without a migration.
Kibana is part of the Elastic Stack. It is the interface to Elasticsearch and does not connect to anything else. Everything you want to see in Kibana must first be indexed into Elasticsearch.
That constraint buys real capability. Because Kibana knows it is talking to Elasticsearch, it exposes the full query language, ad-hoc aggregations across arbitrary fields, and full-text search over unstructured content.
The practical consequence:
- Grafana — you already have data in several systems and want dashboards and alerts over them.
- Kibana — you have committed to Elasticsearch as the store and want to explore what is in it.
Metrics and dashboards
Grafana wins here without much argument. It was built for time-series data and the ergonomics show.
rate(http_requests_total{status=~"5.."}[5m])
/ rate(http_requests_total[5m])Template variables that repeat a panel per service, mixed data sources in one dashboard, annotations marking deploys against a latency graph, and dashboards as JSON that live in version control alongside the code they monitor.
That last point deserves emphasis. Grafana dashboards being provisionable from files means your monitoring is reviewable, diffable, and recreated automatically when you rebuild an environment — rather than being something someone clicked together and nobody can reproduce.
# grafana provisioning
apiVersion: 1
providers:
- name: 'dashboards'
folder: 'Services'
type: file
options:
path: /etc/grafana/dashboardsKibana can chart metrics, and its Lens interface is a pleasant drag-and-drop builder. But storing high-cardinality metrics in Elasticsearch is expensive compared with a purpose-built time-series database, and the query language is not designed for the rate-and-ratio calculations that metric dashboards are made of.
Logs, where the comparison inverts
For searching unstructured logs, Kibana is better, and the reason is architectural rather than cosmetic.
Elasticsearch indexes every field by default, so you can search and aggregate across any of them without deciding in advance which ones matter. Discover lets you filter, add columns, and pivot interactively, and Kibana Query Language reads naturally:
status:500 and service:checkout and not user.role:internalThat indexing is also the cost. Elasticsearch storage overhead is substantial, and cluster operation at volume is a genuine specialism — shard sizing, heap tuning, index lifecycle management and cluster rebalancing are all things you will learn about eventually.
The Grafana ecosystem’s answer is Loki, which takes a deliberately different approach: index only labels, store the log content compressed and unindexed, then grep it at query time.
{service="checkout", env="prod"} |= "error" | json | status >= 500That is dramatically cheaper to store and operate. The trade is that queries not filtered by a label scan a lot of data, and ad-hoc exploration across arbitrary fields is slower than Elasticsearch.
A reasonable rule: if you know which service and time window you care about, Loki is fine and much cheaper. If you need to search across everything for an unknown pattern, Elasticsearch earns its cost.
Alerting
Grafana’s unified alerting is the more capable system for most teams. Rules are defined against any data source, evaluated on a schedule, and routed through a notification policy tree with grouping, silences and inhibition.
- Multi-condition rules combining several queries.
- Notification policies routing by label rather than per-rule configuration.
- Silences during known maintenance.
- Alert rules provisionable as code alongside dashboards.
Kibana’s alerting has improved considerably and is well integrated with Elastic’s own features — anomaly detection through machine learning jobs, and connectors into the SIEM and APM products. If you are using those, it is coherent.
Outside the Elastic ecosystem, Grafana’s alerting is the more flexible option, particularly because it can alert on data Kibana cannot see at all.
One practical note that applies to both: alert on symptoms users experience — error rate, latency, saturation — rather than on every metric you can graph. A dashboard with forty alerts is a dashboard whose alerts get ignored, and that is a tooling-independent failure.
Cost, licensing, and running both
Grafana OSS is AGPLv3 and genuinely free, including alerting. Grafana Cloud has a usable free tier; Enterprise adds reporting, RBAC and commercial data source plugins.
Kibana is under the Elastic License, which is not OSI open source and restricts offering it as a managed service. Elastic later added AGPL as an option for Elasticsearch, and the licensing history here has been genuinely confusing. Basic features are free to use; security, alerting depth and machine learning sit in paid tiers, and Elastic Cloud is usage-priced.
The larger cost is usually operational rather than licensing. Running Elasticsearch at volume requires real attention; Prometheus and Loki are simpler to operate at comparable scale.
Running both is common and sensible:
- Prometheus for metrics, Grafana for dashboards and alerts.
- Elasticsearch for logs, with Kibana for exploration.
- Grafana’s Elasticsearch data source to surface log-derived panels beside metrics.
That gives you one alerting surface and one dashboard surface, while keeping Kibana available for the deep log investigation it is better at.
The starting recommendation for a team without an existing commitment: Grafana with Prometheus and Loki. It is cheaper to run, the operational burden is lower, and adding Elasticsearch later is straightforward if log exploration becomes the constraint.
How this fits the rest of the stack
The reason both tools persist is that dashboards and log exploration are different activities. You watch a dashboard to notice something is wrong; you search logs to find out what. Choosing one and forcing it to do both is how teams end up with expensive log storage or an unusable investigation workflow.
What both depend on is applications that actually emit useful telemetry, with logs that reach somewhere readable. RunxBuild keeps build and runtime logs per deploy with metrics per service, so the failing build and the failing request are visible without assembling a stack first — and when you do want one, the service can ship its logs onward like any other. We do not offer managed Elasticsearch. The RunxBuild hosting calculator shows the service, database and storage separately.
Useful related references:
FAQ
What is the main difference between Grafana and Kibana?
Grafana is storage-agnostic and connects to many backends, making it strongest for time-series dashboards and alerting across systems. Kibana is the interface to Elasticsearch specifically and is strongest at searching and exploring unstructured log data.
Can Grafana replace Kibana?
For dashboards and alerting, largely yes — Grafana has an Elasticsearch data source. For interactive log exploration across arbitrary fields, Kibana’s Discover interface and query language remain better. Many teams run both for exactly that split.
Should I use Loki or Elasticsearch for logs?
Loki if you generally know which service and time window you are investigating — it indexes only labels, so storage and operational cost are far lower. Elasticsearch if you need to search across everything for unknown patterns, which is what its full indexing buys you.
Is Kibana free?
Basic features are free under the Elastic License, but that is not OSI open source and restricts offering it as a managed service. Security, deeper alerting and machine learning sit in paid tiers. Grafana OSS is AGPLv3 and free including alerting.
Which has better alerting, Grafana or Kibana?
Grafana for most teams — unified alerting works across any data source, with notification policies, grouping, silences and rules provisionable as code. Kibana’s alerting is well integrated with Elastic’s own anomaly detection and SIEM features, which is an advantage if you use those.