Tracer fills that gap with step-level performance, resource, and cost insights. There are a few substantial differences between the two, however, there are also clear similarities:
- Pipeline-friendly: Both help track scientific jobs and workflows running on AWS.
- Framework support: Work in EC2, Batch, Kubernetes, or on-prem environments.
- Dashboards: Each provides centralized dashboards for system health and job activity.
- Resource metrics: Track CPU, memory, and disk usage.
- Built for engineers: Serve technical users who need programmatic and visual access to system data.
Key Differences
| Category | CloudWatch | Tracer |
|---|---|---|
| Role/Purpose | Monitor AWS services and infrastructure | Observability for scientific pipelines and compute workloads |
| Dashboards | Manual config with custom metrics and alarms | Instant dashboards for pipeline tools, steps, and outcomes |
| Monitoring | Collects host/app metrics via agent; no task-level visibility | Deep tracing via eBPF with per-task and per-process insight |
| Setup | Needs manual agent installation, permissions, and alarms | One-line install; auto-starts with pipelines; no code changes needed |
| Data Collection | Aggregated metrics from AWS or CW Agent | Real-time telemetry from every process even short-lived tasks |
| Cost Tracking | Basic usage metrics; no job or tool attribution | Built-in cost mapping by job, tool, and team |
| Pipeline Awareness | Not aware of workflow structure or steps | Auto-detects steps, tools, and runtime behavior |
Why Teams Use Tracer Instead of CloudWatch
1. Deeper pipeline-level observabilityCloudWatch is designed to monitor infrastructure like EC2 and ECS, but not to notice what’s happening inside a scientific job.
Tracer captures each tool, task, and process in real-time, including CPU, memory, disk, and network behavior, using eBPF. This offers a detailed visibility into every pipeline step without requiring code instrumentation. 2. Ease of use
Many teams struggle to configure CloudWatch properly. Logs are hard to navigate, and alerts require manual setup. Tracer installs with one line and shows task-level insights automatically, without the need for manual tagging or code changes. 3. Automatic cost attribution and optimization
While CloudWatch can surface AWS usage patterns, it does not track compute waste nor explains why costs are high.
Tracer maps resource usage to exact pipeline steps. showing where CPU or memory are overallocated, where tasks are idle, and where you can save. Teams using these insights reduce compute waste by 20 – 40%. 4. Faster debugging
When CloudWatch raises an alarm, it’s hard to trace the cause without digging through logs. Tracer tells you live which tool is causing it, what files it was using, and whether it was stuck on I/O, CPU, or memory, allowing immediate diagnosis and solution suggestions. 5. Unified view with zero disruption
CloudWatch requires setting up agents and configuring services, Tracer installs with a single line and automatically collects telemetry from all running workloads. It expands beyond CloudWatch’s dashboards with task-level insights. 6. Built for scientific workloads
Many teams find CloudWatch difficult to configure and lacking in visibility, especially for scientific pipelines. This has led to widespread adoption of Grafana as a workaround, but that still leaves gaps.
Tracer addresses the underlying limitations directly, offering deeper, task-specific observability without manual setup or stitching together dashboards. → See how Tracer compares to Grafana
Feature Comparison
| Capability | CloudWatch | Tracer |
|---|---|---|
| Instrumentation | Agent-based; requires configuration | Auto-captures via eBPF; no code changes |
| Pipeline Visibility | No awareness of pipelines or tasks | Built-in tracking of pipeline runs, tools, and steps |
| Data Specifics | Aggregated metrics; may miss short-lived jobs | Tracks every process, including short-lived containers |
| Cost Insights | Limited to usage metrics | Deep real-time tracking by job, tool, and team |
| Setup | Multi-step: agents, config, dashboards, alerts | One-line install; minimal setup |
| Anomaly Detection | Threshold-based alarms; complex to configure | Auto-detects stalls, silent errors, and compute waste |
| Scientific Workload Fit | Basic host metrics only | Built for large-scale, multi-step scientific workflows |
| Observability Depth | Basic host and service-level | End-to-end: task, process, system, and cost levels |
| Pricing | Usage-based per metric, log, and dashboard | Free up to 2,000 runs/month |