> ## Documentation Index > Fetch the complete documentation index at: https://opensre.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Metrics > Track and analyze comprehensive pipeline metrics with Tracer ## Overview Tracer automatically captures detailed metrics for every process in your pipeline, providing deep visibility into resource usage, performance, and execution patterns without requiring any code changes. ## Key Metrics Tracked ### Resource Utilization * CPU Usage: Per-process utilization, saturation levels, and hotspots * Memory Usage: Allocation, peak consumption, and memory efficiency * I/O Ops: Disk reads/writes, throughput, and bottleneck detection * Network Activity: Bandwidth usage, transfer volume, and congestion * GPU Utilization: Compute load and VRAM usage (when applicable)

### Performance Metrics * Execution Time: Wall-clock, CPU, and wait times per task * Throughput: Data processed per unit time (e.g., samples/hour) * Latency: Startup delays, scheduling lag, and queueing overhead * Parallelization: Concurrency levels and contention across workers ### Cost Metrics * Compute Costs: Per-task and per-pipeline cost attribution * Resource Costs: Storage, network, and transfer charges * Efficiency: Cost per sample, cost per GB processed * Waste Detection: Idle resources, over-provisioning, and inefficiencies

## Real-Time Monitoring Inspect live metrics as your pipeline runs, with immediate visibility into performance, utilization, and potential bottlenecks. ## Historical Analysis * Analyze past runs to uncover long-term patterns and optimization opportunities: * Performance Trends: How execution times shift across versions * Utilization Patterns: Persistent over/under-use of compute or memory * Cost Evolution: Impact of configuration changes on total spend * Regression Detection: Automatic alerts for performance degradation