tracer-cloud--opensre/docs/comparisons/Tracer_vs_Grafana.mdx

---
title: 'How to use Tracer with Grafana'
sidebarTitle: 'Grafana'
description: 'Execution-aware context beyond dashboards'
---

Grafana provides visualization and alerting for metrics, logs, and traces collected from external telemetry systems such as Prometheus and node-level exporters. In bioinformatics and HPC environments, it is commonly used to explore system- and application-reported signals for compute-intensive workloads through configurable dashboards.

Tracer complements Grafana by observing how tasks, tools, and processes actually execute at runtime and organizing this behavior by pipeline, run, and execution unit.

<Note>
If you're new to Tracer or want a conceptual overview, see [How Tracer fits in your stack](/comparisons/overview).
</Note>

## What Grafana does well

Grafana is designed for visualization and alerting. It provides:

- Dashboards built from metrics, logs, and traces
- Flexible queries, panels, and transformations
- Alerting based on thresholds and rules
- Support for many telemetry backends and data sources

These capabilities make Grafana effective for exploring and monitoring reported telemetry across systems and services.

## What Grafana does not observe

Grafana visualizes data that is collected elsewhere. It does not observe execution directly and does not have inherent awareness of pipeline or task structure. It does not show:

- Execution behavior inside processes or containers
- CPU vs I/O vs memory contention at runtime
- Short-lived processes that complete between metric scrapes
- Idle time masked by aggregate utilization metrics
- How reported metrics map to pipeline runs, tasks, or tools
- How cost relates to actual execution rather than to infrastructure uptime

This information is not present in dashboards unless it is inferred through instrumentation, exporters, or custom queries.

## Why this gap matters

Scientific pipelines often involve heterogeneous tools, nested execution, and short-lived subprocesses. When relying on dashboards alone, teams may see that resources were used, but not how work progressed.

As a result:

- Performance bottlenecks must be inferred from aggregates
- Idle time can be mistaken for productive work
- Cost is attributed to hosts or services rather than to execution units
- Custom dashboards accumulate assumptions about runtime behavior

These approaches can be useful, but they have limits when execution behavior is not directly observed.

## What Tracer adds

Tracer observes execution directly from the host and container runtime and adds:

- Observed CPU, memory, disk, and network behavior
- Visibility into short-lived processes and nested tools
- Attribution by pipeline, run, task, or execution unit
- Cost mapping aligned with observed runtime activity

These insights are based on what actually runs, not on emitted metrics or dashboard queries.

## Example: reported metrics versus observed execution

A dashboard shows high instance utilization during a pipeline run. Tracer reveals that:

- CPU usage remains low
- Tasks spend most of their time blocked on disk I/O
- Multiple short-lived helper processes dominate execution time

This indicates an I/O-bound workload rather than insufficient compute. The distinction is visible only when execution behavior is observed directly.

## Observability comparison

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  src="https://vakisr.github.io/tracer-radar-chart/radar-chart-grafana.html"
  width="100%"
  height="750"
  style={{ border: 'none', borderRadius: '8px' }}
  title="Grafana vs Grafana+Tracer capabilities comparison"
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This comparison highlights the difference between dashboard-level telemetry and execution-level observation.

## What Tracer does not replace

Tracer is not a general-purpose visualization platform.

- It does not replace Grafana dashboards
- It does not collect arbitrary application metrics
- Its alerting is focused on execution behavior, not all system events
- It stores execution-derived metrics it observes, but does not replace metric backends for unrelated or user-defined metrics

Grafana remains useful as a shared visualization layer across many systems.

## When to use Tracer with Grafana

Tracer is most useful alongside Grafana when teams need to:

- Understand pipeline behavior beyond reported metrics
- Diagnose performance issues involving short-lived tasks
- Attribute resource usage and cost to workflows or tools
- Reduce manual dashboard configuration and metric correlation

Tracer focuses on execution behavior. Grafana continues to provide visualization and alerting for reported telemetry.

## Summary

Grafana visualizes metrics, logs, and traces collected from many systems. Tracer adds execution-level visibility that shows how pipelines, tasks, and tools actually behave at runtime and how resource usage and cost map to real work. Together, they provide complementary perspectives without overlap.

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