Download Your Distributed Tracing Sampling Guide

A system handling 10,000 RPS with 20 spans per trace and 1KB per span generates 200MB per second—17TB daily. At typical vendor pricing, that’s $155,000 monthly just for storage. Random sampling at 1% reduces costs but loses value: errors are rare, so you’ll miss them. Slow requests disappear. Finding specific traces becomes impossible. The goal isn’t cost reduction—it’s signal improvement. Keep traces that teach you something (errors, latency outliers), discard millions of identical successful requests.

This complete guide teaches you how to sample traces intelligently.

Read this e-book to understand:

• Why 100% sampling fails at scale: storage costs, query performance degradation, and signal-to-noise problems
• What sampling preserves and loses: representative traces and statistical estimates vs. specific trace lookup
• Head-based sampling: making sampling decisions at the entry point without knowing if the trace matters
• Tail-based sampling: collecting all spans, then deciding which traces to keep based on actual behavior
• Error prioritization: ensuring errors are sampled higher than success cases
• Latency-aware sampling: capturing slow requests while keeping fast requests at lower rates
• Implementing sampling in OpenTelemetry: configuring samplers and propagating decisions across services

Download Your Distributed Tracing Sampling Guide now to get trace visibility at manageable cost and noise levels.