Cost Optimization Guide
Reduce logging costs with practical, low-risk controls
If logging cost is rising faster than value, the fix is policy and placement, not blind data reduction.
Why this problem exists
Teams over-collect by default because it feels safer than policy-driven filtering.
Cost controls are added late and rarely standardized across services.
Real cost and impact
Runaway logging cost compresses budgets for reliability and product delivery.
Noisy data also lowers mean time to diagnosis by cluttering search results.
Solutions (including alternatives)
- Define what must always be kept, what can be sampled, and what should be dropped.
- Apply these rules before Datadog ingestion and archive complete data in S3.
- Track reduction targets and validate alert quality after each rollout.
How LogTrim solves it
LogTrim gives teams one place to enforce pre-ingestion policy at scale.
Savings and signal quality improvements can be measured quickly after deployment.
Example scenario
A team introduced centralized filtering policy and removed multiple duplicated logger rules.
They reduced monthly ingestion while preserving incident workflows.
Reduce your costs with LogTrim
Start with high-noise categories, keep high-signal logs in Datadog, and archive full retention in S3.