When it comes to monitoring, observability, and data visualization, two tools often come up in conversation: Grafana and Kibana. Both are powerful, open-source platforms used by DevOps engineers, developers, and data analysts. However, they serve slightly different purposes and are optimized for different kinds of data and use cases.
In this post, we’ll compare Grafana vs Kibana in terms of features, strengths, and when you should use one over the other.
What is Grafana?
Grafana is a multi-source visualization platform, primarily known for working with time-series data. It integrates with dozens of data sources like Prometheus, InfluxDB, Graphite, MySQL, PostgreSQL, and Elasticsearch.
Grafana excels at:
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Real-time metric monitoring (CPU, memory, latency, etc.)
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Dashboards with flexible layouts and variables
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Custom alerting and thresholds
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Infrastructure observability (DevOps and SRE use cases)
Grafana is often used alongside Prometheus and Loki in cloud-native stacks.
What is Kibana?
Kibana is the visualization layer of the Elastic Stack (formerly ELK stack: Elasticsearch, Logstash, Kibana). It’s primarily used for log and event data exploration stored in Elasticsearch.
Kibana excels at:
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Full-text log search and filtering
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Real-time log analytics
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Visualizing large log datasets with charts, tables, maps
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Creating dashboards based on Elasticsearch queries
Kibana is tightly coupled with Elasticsearch and is the best choice if you’re already using it as your logging backend.
Key Differences
| Feature | Grafana | Kibana |
|---|---|---|
| Primary Use Case | Metrics and system monitoring | Log analysis and full-text search |
| Data Sources | Multi-source (Prometheus, SQL, etc.) | Elasticsearch only |
| Alerting | Built-in, powerful alert system | Basic (improving in recent versions) |
| Query Language | Depends on source (PromQL, SQL) | Kibana Query Language (KQL) |
| Dashboards | Highly customizable | Powerful with logs and filters |
| Ecosystem | Prometheus, Loki, Tempo | Elasticsearch, Logstash, Beats |
When to Use Grafana
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You need real-time metric dashboards
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You work with multiple data sources (Prometheus, SQL, etc.)
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You need custom alerting and integrations
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You want infrastructure-level observability
When to Use Kibana
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You’re using Elasticsearch for logging
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You need advanced log filtering and search
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You want to explore raw events and unstructured data
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You’re already within the Elastic Stack ecosystem
Final Thoughts
Both Grafana and Kibana are excellent tools, but they shine in different scenarios. Grafana is more versatile when it comes to metrics and multi-source visualizations, while Kibana is the go-to tool for log exploration and full-text search.
In many modern stacks, teams actually use both: Grafana for dashboards and alerts, and Kibana for deep log analysis.
Choose the one that aligns best with your stack—and don’t be afraid to combine them for full observability.
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