cost-optimized-log-trace-sampling

What problem does it solve?

Observability data can be expensive at scale. Smart sampling strategies maintain visibility while controlling costs.

Core Features & Use Cases

• Head-Based Sampling: Decision at trace start with probabilistic sampling or rate-limiting.
• Tail-Based Sampling: Decisions after trace completes for errors, latency, or attributes.
• Hybrid Approaches: Combine strategies to balance cost and visibility.
• Log Sampling Strategies: Dynamic log levels, sampling, and pre-export aggregation.
• Cost Optimization Tips: Retention, aggregation, tuning, tiered storage.

Quick Start

Use this approach to configure traces to sample a representative subset of requests while filtering verbose logs and aggregating similar events before export.