Friday, May 15, 2026

What and where we use Grafana, Splunk, and Prometheus ?

Grafana, Splunk, and Prometheus are all popular tools used in monitoring, logging, and observability, but they serve different purposes and are often used together rather than being direct replacements.

Here’s a clear breakdown:


🔹 1. High-Level Difference

ToolPrimary PurposeWhat it Does Best
GrafanaVisualization & dashboardsDisplays data from multiple sources
PrometheusMetrics collection & monitoringStores and queries time-series metrics
SplunkLog management & analyticsCentralized log search & analysis

🔹 2. Detailed Explanation

✅ Grafana

  • Role: Visualization layer
  • Type: Dashboarding tool
  • Core Function:
    Displays data from sources like Prometheus, Elasticsearch, SQL DBs, etc.

Key Features:

  • Rich dashboards & graphs
  • Alerts & notifications
  • Supports multiple data sources
  • Great UI for business + tech users

Example:

You can use Grafana to:

  • Show CPU usage graphs
  • Visualize API latency trends
  • Create executive dashboards

👉 Important: Grafana does NOT store data itself (except limited cases). It reads from data sources.


✅ Prometheus

  • Role: Monitoring & metrics collection
  • Type: Time-series database
  • Core Function:
    Collects numerical metrics from systems and stores them.

Key Features:

  • Pull-based monitoring (scrapes metrics from endpoints)
  • Uses PromQL (query language)
  • Built-in alerting (Alertmanager)
  • Ideal for Kubernetes / cloud-native setups

Example Metrics:

  • CPU usage
  • Memory consumption
  • Request rates
  • Error counts

👉 Prometheus is focused on metrics only, not logs.


✅ Splunk

  • Role: Log management & analytics platform
  • Type: Enterprise data platform
  • Core Function:
    Collects, indexes, and analyzes machine-generated data (logs).

Key Features:

  • Powerful log search (SPL – Search Processing Language)
  • Real-time log monitoring
  • Security (SIEM), compliance, and audit use cases
  • Handles structured & unstructured data

Example Logs:

  • Application logs
  • Server logs
  • Security logs
  • Audit trails

👉 Splunk is not just monitoring—it’s full-scale data analytics + security platform.


🔹 3. Key Differences (Simple View)

FeatureGrafanaPrometheusSplunk
Stores Data❌ No (mostly)✅ Yes✅ Yes
Data TypeVisual outputMetrics (numbers)Logs + events
Query LanguageDepends on sourcePromQLSPL
Use CaseDashboardsMonitoring systemsLog analysis & security
CostFree/Open SourceFree/Open SourcePaid (expensive)

🔹 4. How They Work Together

A very common real-world setup:

Prometheus → collects metrics
       ↓
Grafana → visualizes metrics

And separately:

Applications → send logs → Splunk → analyze/search logs

👉 In modern architectures:

  • Prometheus + Grafana = Monitoring stack
  • Splunk = Logging + Security analytics

🔹 5. When to Use What

Use Prometheus when:

  • You need system/service monitoring
  • You work with Kubernetes or microservices
  • You need metrics-based alerting

Use Grafana when:

  • You want beautiful dashboards
  • You need to visualize multiple data sources
  • You want business + technical insights

Use Splunk when:

  • You need centralized logging
  • You need security monitoring (SIEM)
  • You want deep log analysis and auditing

🔹 6. Easy Analogy

Think of a car dashboard:

  • Prometheus → collects speed, fuel, engine data
  • Grafana → shows it neatly on the dashboard
  • Splunk → records everything that happened (every trip, error, warning)

✅ Final Summary

  • Grafana → “Show me the data”
  • Prometheus → “Collect and store metrics”
  • Splunk → “Analyze logs and events”

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About the Author : Anurag Pandey | Database cloud Architect | Oracle | SQL Server | PostgreSQL | AI and Data Platform Enthusiast

Anurag Pandey is a Database Architect with expertise in Oracle, PostgreSQL, SQL Server, Linux, and enterprise database platforms and specializes in database architecture, performance tuning, high availability, disaster recovery, cloud modernization, and operational excellence. With extensive experience in enterprise-scale environments, Anurag is passionate about leveraging AI, automation, and modern data platforms to improve reliability, scalability, and business outcomes. He actively contributes to technical communities through knowledge sharing, architecture guidance, and thought leadership in database and data engineering domains.

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