Cost Optimization Guide
High-volume logging problems and how to fix them
At high throughput, noisy logging degrades both observability quality and infrastructure efficiency.
Why this problem exists
High-traffic endpoints produce repetitive logs faster than teams can tune policies manually.
Debug-level output can leak into production and remain active for long periods.
Real cost and impact
Large bursts of low-value logs create immediate ingestion spikes and unstable monthly forecasting.
Noise-heavy indexes also make troubleshooting slower.
Solutions (including alternatives)
- Define default suppression for known-noise classes at the ingress edge.
- Prioritize deterministic keep rules for incidents, security, and compliance events.
- Use S3 archival for complete retention without indexed-cost exposure.
How LogTrim solves it
LogTrim handles high-throughput filtering and routing as a dedicated data plane.
Rules can be enforced consistently across services without code-level drift.
Example scenario
A high-traffic API normalized request logging policy across services and removed duplicated access logs.
Daily ingest stabilized and alert readability improved.