Cost Optimization Guide
Build a log filtering pipeline that cuts cost and preserves signal
A reliable log pipeline separates ingestion, filtering, masking, and routing into explicit stages.
Why this problem exists
Many teams blend filtering into app code, which creates inconsistent behavior across services.
Pipeline stages are often added ad hoc after incidents, leading to brittle configurations.
Real cost and impact
Without a clear filtering layer, every event is treated as expensive observability data.
Noisy logs degrade both cost efficiency and dashboard usability.
Solutions (including alternatives)
- Keep applications focused on event emission and centralize filtering policy in the data path.
- Apply PII masking and sampling before forwarding to observability backends.
- Use dual routing for high-signal operations in Datadog and full retention in S3.
How LogTrim solves it
LogTrim acts as the dedicated pre-ingestion data plane for filtering and routing.
Rules are managed centrally, reducing drift between services.
Example scenario
A multi-service platform standardized filtering rules in one control surface instead of patching every app.
The team shipped fewer logging hotfixes and reduced ingestion spend.