cost-optimized-log-trace-sampling

Reduce observability costs by implementing smart log and trace sampling. You can apply head-based or tail-based strategies to maintain visibility while controlling expenses in high-volume environments.

Head-based trace sampling makes decisions at trace start using probabilistic rates, while tail-based sampling decides after completion based on errors or latency. Hybrid approaches combine both to balance cost and visibility.

Configure OpenTelemetry sampling by setting configurable rates and applying tail-based rules. This captures traces based on specific error conditions and latency thresholds after the trace completes.

Sample logs before export using dynamic log levels and pre-export aggregation. This filters verbose logs and aggregates similar events, reducing data volume in centralized log pipelines.

Hybrid sampling strategies combine head-based and tail-based approaches for traces alongside dynamic log sampling. This balances cost and visibility across both distributed tracing and centralized logging.

Cost-aware retention and aggregation strategies involve tiered storage, data tuning, and pre-export aggregation. These approaches reduce long-term observability expenses while keeping necessary data accessible.