When should I use Grafana instead of Prometheus?

Building rich, interactive observability dashboards from multiple data sources in one interface. Alerting on pipeline metrics, infrastructure health, and business KPIs in a unified view. Visualizing Prometheus, InfluxDB, Loki, and other time-series data side by side

When should I use Prometheus instead of Grafana?

Collecting and storing time-series metrics from infrastructure and applications via pull-based scraping. Alerting on metric thresholds via Alertmanager for infrastructure and data pipeline SLAs. Service discovery-based metric scraping for dynamic Kubernetes and container environments

What are the main weaknesses of Grafana?

Dashboards must be built and maintained manually; no auto-generation from metrics. Self-hosted Grafana requires maintaining the full observability stack (Prometheus, Loki, Grafana). Dashboard-as-code (Grafonnet, Terraform provider) has a steep learning curve for complex dashboards

What are the main weaknesses of Prometheus?

Pull-based model requires all targets to expose a metrics endpoint — push model needs Pushgateway. Local storage is not designed for long-term metric retention at scale — use Thanos or Cortex for that. No native log or trace collection; must be combined with Loki and Jaeger for full observability

Grafana vs Prometheus: Key Differences for Python Data Engineering

Monitoring & Observability

Grafana

Observability & Dashboarding Platform

★ 4.8

AGPL-3.0 (Open Source)

pip install grafana-client

Prometheus

Open-Source Monitoring System

★ 4.7

Apache-2.0

pip install prometheus-client

Side-by-Side Comparison

Grafana

Prometheus

Grafana

Prometheus

Best For

✓Building rich, interactive observability dashboards from multiple data sources in one interface
✓Alerting on pipeline metrics, infrastructure health, and business KPIs in a unified view
✓Visualizing Prometheus, InfluxDB, Loki, and other time-series data side by side

✓Collecting and storing time-series metrics from infrastructure and applications via pull-based scraping
✓Alerting on metric thresholds via Alertmanager for infrastructure and data pipeline SLAs
✓Service discovery-based metric scraping for dynamic Kubernetes and container environments

Best For

✓Building rich, interactive observability dashboards from multiple data sources in one interface
✓Alerting on pipeline metrics, infrastructure health, and business KPIs in a unified view
✓Visualizing Prometheus, InfluxDB, Loki, and other time-series data side by side

✓Collecting and storing time-series metrics from infrastructure and applications via pull-based scraping
✓Alerting on metric thresholds via Alertmanager for infrastructure and data pipeline SLAs
✓Service discovery-based metric scraping for dynamic Kubernetes and container environments

Weaknesses

•Dashboards must be built and maintained manually; no auto-generation from metrics
•Self-hosted Grafana requires maintaining the full observability stack (Prometheus, Loki, Grafana)
•Dashboard-as-code (Grafonnet, Terraform provider) has a steep learning curve for complex dashboards

•Pull-based model requires all targets to expose a metrics endpoint — push model needs Pushgateway
•Local storage is not designed for long-term metric retention at scale — use Thanos or Cortex for that
•No native log or trace collection; must be combined with Loki and Jaeger for full observability

Weaknesses

•Dashboards must be built and maintained manually; no auto-generation from metrics
•Self-hosted Grafana requires maintaining the full observability stack (Prometheus, Loki, Grafana)
•Dashboard-as-code (Grafonnet, Terraform provider) has a steep learning curve for complex dashboards

•Pull-based model requires all targets to expose a metrics endpoint — push model needs Pushgateway
•Local storage is not designed for long-term metric retention at scale — use Thanos or Cortex for that
•No native log or trace collection; must be combined with Loki and Jaeger for full observability

License

AGPL-3.0 (Open Source)

Apache-2.0

License

AGPL-3.0 (Open Source)

Apache-2.0

Install

pip install grafana-client

pip install prometheus-client

Install

pip install grafana-client

pip install prometheus-client

Rating

★ 4.8

★ 4.7

Rating

★ 4.8

★ 4.7

Key Features

Grafana

1Open-source observability platform for visualizing metrics, logs, and traces
2Data source plugins connect to Prometheus, Loki, InfluxDB, BigQuery, and 100+ more
3Alerting engine with multi-condition rules and notification channels
4Templated, parameterized dashboards for reuse across environments
5Annotations mark pipeline run events on time-series charts

Prometheus

1Pull-based metrics collection from instrumented services via HTTP scraping
2Dimensional time-series data model with labels for multi-dimensional querying
3PromQL query language for computing rates, aggregations, and alerts
4Alertmanager integration routes fired alerts to PagerDuty, Slack, and email
5Recording rules pre-compute expensive queries as new time-series

How Python Data Engineers Use These Tools

Grafana

Python data engineers use Grafana to monitor data pipeline performance — building dashboards that show rows ingested per minute, error rates, and processing latency sourced from Prometheus metrics. The Grafana API allows Python scripts to programmatically create and update dashboards and annotations — marking pipeline run start/end times directly on metric charts for correlation analysis.

Prometheus

Python data engineers instrument pipeline services with the `prometheus_client` library — exposing gauges for queue depth, counters for rows processed, and histograms for processing latency. A Python pipeline that exposes a `/metrics` HTTP endpoint is automatically scraped by Prometheus, enabling PromQL dashboards and alerts on pipeline performance without custom logging infrastructure.

Individual Tool Pages

View Grafana details →View Prometheus details →

Side-by-Side Comparison

Grafana

Prometheus

Grafana

Prometheus

Best For

✓Building rich, interactive observability dashboards from multiple data sources in one interface
✓Alerting on pipeline metrics, infrastructure health, and business KPIs in a unified view
✓Visualizing Prometheus, InfluxDB, Loki, and other time-series data side by side

✓Collecting and storing time-series metrics from infrastructure and applications via pull-based scraping
✓Alerting on metric thresholds via Alertmanager for infrastructure and data pipeline SLAs
✓Service discovery-based metric scraping for dynamic Kubernetes and container environments

Best For

✓Building rich, interactive observability dashboards from multiple data sources in one interface
✓Alerting on pipeline metrics, infrastructure health, and business KPIs in a unified view
✓Visualizing Prometheus, InfluxDB, Loki, and other time-series data side by side

✓Collecting and storing time-series metrics from infrastructure and applications via pull-based scraping
✓Alerting on metric thresholds via Alertmanager for infrastructure and data pipeline SLAs
✓Service discovery-based metric scraping for dynamic Kubernetes and container environments

Weaknesses

•Dashboards must be built and maintained manually; no auto-generation from metrics
•Self-hosted Grafana requires maintaining the full observability stack (Prometheus, Loki, Grafana)
•Dashboard-as-code (Grafonnet, Terraform provider) has a steep learning curve for complex dashboards

•Pull-based model requires all targets to expose a metrics endpoint — push model needs Pushgateway
•Local storage is not designed for long-term metric retention at scale — use Thanos or Cortex for that
•No native log or trace collection; must be combined with Loki and Jaeger for full observability

Weaknesses

•Dashboards must be built and maintained manually; no auto-generation from metrics
•Self-hosted Grafana requires maintaining the full observability stack (Prometheus, Loki, Grafana)
•Dashboard-as-code (Grafonnet, Terraform provider) has a steep learning curve for complex dashboards

•Pull-based model requires all targets to expose a metrics endpoint — push model needs Pushgateway
•Local storage is not designed for long-term metric retention at scale — use Thanos or Cortex for that
•No native log or trace collection; must be combined with Loki and Jaeger for full observability

License

AGPL-3.0 (Open Source)

Apache-2.0

License

AGPL-3.0 (Open Source)

Apache-2.0

Install

pip install grafana-client

pip install prometheus-client

Install

pip install grafana-client

pip install prometheus-client

Rating

★ 4.8

★ 4.7

Rating

★ 4.8

★ 4.7

Key Features

Grafana

1Open-source observability platform for visualizing metrics, logs, and traces
2Data source plugins connect to Prometheus, Loki, InfluxDB, BigQuery, and 100+ more
3Alerting engine with multi-condition rules and notification channels
4Templated, parameterized dashboards for reuse across environments
5Annotations mark pipeline run events on time-series charts

Prometheus

1Pull-based metrics collection from instrumented services via HTTP scraping
2Dimensional time-series data model with labels for multi-dimensional querying
3PromQL query language for computing rates, aggregations, and alerts
4Alertmanager integration routes fired alerts to PagerDuty, Slack, and email
5Recording rules pre-compute expensive queries as new time-series

How Python Data Engineers Use These Tools