Explanation
Some GCP services and workloads generate INFO-level logs at very high frequencies - for example, load balancers logging every HTTP request or GKE nodes logging system health messages. While valuable for debugging, these logs can flood Cloud Logging with non-critical data. Without log-level tuning or exclusion filters, organizations incur continuous ingestion charges for messages that are seldom analyzed. Over time, this behavior compounds into a persistent waste driver across large-scale environments.
Relevant Billing Model
Cloud Logging costs are driven by data ingestion volume and storage retention. Excessive INFO-level logs increase both metrics, especially when emitted by high-traffic resources such as API endpoints, Kubernetes clusters, or compute instances. Since these logs rarely indicate actionable events, their ingestion often yields limited operational value.
Detection
- Identify resources or services with log ingestion volumes significantly above expected baselines
- Correlate ingestion costs with log severity levels to determine if INFO logs dominate total volume
- Review application or service configurations that define log verbosity levels
- Assess whether INFO-level logs provide measurable operational value or simply duplicate metrics already available through monitoring
Remediation
- Adjust log verbosity in resource configurations or application code to emit fewer INFO-level messages
- Apply Logging sink filters or exclusions to prevent ingestion of repetitive INFO logs
- Aggregate high-volume INFO logs into sampled or summarized events where detailed records are unnecessary
- Establish logging standards that define appropriate verbosity levels for each environment and service type