- Real-time monitoring: Both show live dashboards with resource usage and alerts.
- Platform support: Both run on Linux-based HPC or cloud infrastructure.
- Metric-driven: Both rely on telemetry data (metrics, logs, traces) to provide insight.
- Scalability: Both scale from single-node experiments to multi-node HPC/cloud clusters.
- Language-agnostic: Both are language agnostic.
Key Differences
| Category | Grafana | Tracer |
|---|---|---|
| Role/Purpose | General-purpose dashboard for metrics, logs, and traces. Often used to monitor infrastructure. | Observability for scientific pipelines. Tracks performance and errors in real time. |
| Dashboards | Flexible, configurable panels | Instant dashboarding for bioinformatic pipelines |
| Monitoring | Visualizes metrics/logs from connected sources. Relies on Prometheus/Loki for data | Captures system-level data via eBPF. Provides full pipeline and tool visibility. |
| Setup | Requires multiple services and configs | One-line install. Auto-starts with pipelines. No code changes. |
| Data Collection | Pull-based from exporters and logs. May miss short-lived or high-cardinality jobs. | Continuous, automatic per-process telemetry. Captures all jobs by default. |
| Cost Tracking | No built-in cost visibility. External tools or custom metrics needed. | Built-in cost mapping per tool, pipeline, and team. |
Why teams replace Grafana with Tracer for Scientific Pipelines
1. Deep visibility without instrumentation Grafana depends on metric exporters and log shippers to visualize data. Tracer automatically captures CPU, memory, I/O, and system activity directly from the OS, by using eBPF. No exporters or code changes are required. It observes even short-lived jobs and thread-level bottlenecks that Grafana often misses. 2. Built for pipelines, not dashboards Tracer automatically organizes system data by pipeline, run, and tool, no manual tagging or code changes needed. Researchers see step-by-step performance, failures, and costs in context.Grafana, on the other hand, has no understanding of pipelines or processes. It shows raw server metrics, but doesn’t link them to specific jobs, tools, or outcomes unless users manually wire everything together with tags, queries, and dashboards. 3. Built-in cost visibility Grafana can’t tell you which tool or job burned your budget. Tracer breaks down costs by pipeline, tool, and team in real time.
Hereby, Tracer helps teams identify over-provisioning, idle jobs, and compute waste, often resulting in 20–40% of savings in HPC/cloud environments. 4. Actionable debugging enhanced by AI Grafana’s log dashboards rely fully on manual queries and structured inputs. Tracer auto-generates logs for tools that lack them, and surfaces root causes. From this, insights are automatically organized for fast triage and resolution. 5. Simpler to deploy for research environments Tracer installs in one line and works immediately with common pipeline engines like Nextflow, Snakemake, Slurm, and AWS Batch. Grafana setups require multiple moving parts and ongoing maintenance. 6. We know what you are missing We ran into the same limitations. Grafana didn’t give us the visibility we needed for scientific workflows. Here’s the story of why we moved on from Grafana, and what we built instead. → Find out why we left Grafana and built our own dashboards
Feature Comparison
| Capability | Grafana | Tracer |
|---|---|---|
| Instrumentation | Manual setup of exporters and log shippers | Auto-captures via eBPF, no code changes |
| Pipeline Visibility | No pipeline context; requires manual custom tagging | Built-in run and step-level tracking |
| Data Specifics | Aggregated metrics; may miss short-lived jobs | Tracks every process, even short-lived ones |
| Cost Insights | No native cost tracking | Real-time tracking of spend to tools, pipelines, and teams |
| Setup | Multi-service deployment and dashboard config | One-line install; minimal overhead |