AWS CloudTrail enables event logging across AWS services, but when multiple trails are configured to log overlapping events — especially data events — it can result in redundant charges and unnecessary storage or ingestion costs. This commonly occurs in decentralized environments where teams create trails independently, unaware of existing coverage or shared logging destinations.Each trail that records data events contributes to billing on a per-event basis, even if the same activity is logged by multiple trails. Additional costs may also arise from delivering duplicate logs to separate S3 buckets or CloudWatch Log groups. While separate trails may be justified for audit, compliance, or operational segmentation, unintentional duplication increases both cost and operational complexity without added value.

Engineers often enable verbose logging (e.g., debug or trace-level) during development or troubleshooting, then forget to disable it after deployment. This results in elevated log ingestion rates — and therefore costs — even when the detailed logs are no longer needed. Because CloudWatch Logs charges per GB ingested, persistent debug logging in production environments can create silent but material cost increases, particularly for high-throughput services.In environments with multiple teams or loosely governed log group policies, this issue can go undetected for long periods. Identifying and deactivating unnecessary debug-level logging is a low-risk, high-leverage optimization.

In Azure Databricks environments that rely on Private Link for secure networking, it’s common to route traffic through multi-tiered network architectures. This often includes multiple VNets, Private Link endpoints, or peered subscriptions between data sources (e.g., ADLS) and the Databricks compute plane. While these architectures may be designed for isolation or compliance, they frequently introduce redundant routing paths that add cost without improving performance. Each additional hop may result in duplicated Private Link ingress and egress charges. Without regular review, this can create persistent and unrecognized network inefficiencies tied to Databricks usage.

Databricks cost optimization begins with visibility. Unlike traditional IaaS services, Databricks operates as an orchestration layer spanning compute, storage, and execution — but its billing data often lacks granularity by workload, job, or team. This creates a visibility gap: costs fluctuate without clear root causes, ownership is unclear, and optimization efforts stall due to lack of actionable insight. When costs are not attributed functionally — for example, to orchestration (query/job DBUs), compute (cloud VMs), storage, or data transfer — it becomes difficult to pinpoint what’s driving spend or where improvements can be made. As a result, inefficiencies persist not due to a single misconfiguration, but because the system lacks the structure to surface them.

CloudWatch log groups often persist long after their usefulness has expired. In some cases, they are associated with applications or resources that are no longer active. In other cases, the systems may still be running, but the log data is no longer being reviewed, analyzed, or used by any team. Regardless of the reason, retaining logs that no one is monitoring or using results in unnecessary storage costs. If log data is not needed for operational visibility, debugging, compliance, or auditing purposes, it should either be deleted or managed with a shorter retention policy.