Review Azure Files provisioned capacity and snapshot storage
Compare share-level utilization with snapshot storage in Azure Monitor before reviewing provisioned capacity. Keep account totals out of share-level decisions.
Find answers to common questions about cloud and AI costs.
Compare share-level utilization with snapshot storage in Azure Monitor before reviewing provisioned capacity. Keep account totals out of share-level decisions.
Check lifecycle rules before moving an individual Azure Block Blob out of Archive, and account for retrieval and early-deletion meters before approval.
Compare retained Redshift Serverless usage with capacity and RPU-hour limits, then confirm whether each action warns, logs, alerts, or stops query processing.
Check whether an existing Azure Blob Inventory report covers retained versions and deleted objects, then compare its scope with Blob Capacity before planning storage changes.
Check BigQuery partition tiers and last-write timestamps before forecasting storage costs. Separate logical and billable bytes without treating metadata as a full bill.
Separate Cloud SQL charges from usage charts before choosing a cost change. Review one invoice month and include any separate MySQL enhanced-backup project.
Break down Amazon RDS costs by usage type so instance hours, storage, IOPS, backups, and transfer are reviewed separately before capacity changes.
Review Cloud Storage versioning, soft delete, and locked retention settings before treating deletion as storage savings.
Compare Cosmos DB partition utilization and 429 responses before a throughput cut, then use existing logs to identify keys with concentrated demand.
Compare factory-level Azure Data Factory costs with per-run meter consumption to choose a workload for review without treating duration or usage as the charged price.
Review backup charge types, stopped protection and Archive eligibility before proposing retention changes that could remove needed recovery points.
Review Athena partition filters, scan statistics, per-query cancellation and workgroup alerts. Reuse results only when the permitted result age fits the business need.
Compare DynamoDB capacity modes using traffic patterns, CloudWatch metrics, index usage and throttling before changing how you pay for throughput.
Compare Dataflow job estimates with billed costs, then use Dataflow and Managed Service for Apache Spark metrics to assign performance follow-up without mistaking errors for spend.
Review one BigQuery SELECT for unnecessary partition scans. Compare dry-run bytes without dropping required rows or treating clustered-table estimates as final costs.
Review the dataset and project requirements for Cloud Billing export, including location limits, encryption constraints, project links, and available usage-cost history.
Identify provisioned Azure SQL databases and pools worth a sizing review using workload peaks, storage limits and purchasing-model checks, not low CPU alone.
Review Cloud Storage lifecycle plans for unsupported transitions, retention blockers, and early-deletion charges before approving expected savings.
Inventory available EBS volumes in one AWS account and Region, then verify ownership and retention before treating any volume as a removal candidate.