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.
Understand reservations, unused resources, and Azure OpenAI charges.
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.
Review AWS transfer usage types, Azure bandwidth categories, and Google Cloud network tiers before assigning a compute, storage, or network cost cause.
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.
Review AWS and Azure telemetry to separate application requests, model calls, retries, and evaluation activity before allocating AI spend.
Compare scoped AI spending with accepted business outcomes using a read-only worksheet that keeps retries, failed attempts and attribution gaps visible.
Check managed online deployment minimums and training-cluster node floors against workload needs, then use endpoint cost tags to prioritize owner reviews.
Compare Azure OpenAI token charges with hourly PTU capacity costs, including idle hours, model-specific sizing, reservation coverage and unused commitments.
Compare Azure reservation and savings plan scopes with eligible usage before widening access across an EA enrollment or MCA billing profile.
Compare model candidates on shared tasks, quality limits and response times. Use a read-only worksheet to estimate cost per accepted result from evaluation records.
Compare Cosmos DB partition utilization and 429 responses before a throughput cut, then use existing logs to identify keys with concentrated demand.
Review Standard and Premium profile fees, request counts, and transfer charges separately to identify which costs caching can reduce and which remain.
Separate model tokens, search grounding, and tool-resource measurements so an AI workflow budget does not hide charges behind prompt counts or monitoring data.
Separate bandwidth, peering and Front Door charges before estimating Azure cross-region costs. Use a read-only register to match network paths to billed meters.
Separate reservation use from GPU activity, then compare period cost with accepted workload output before deciding whether to renew capacity.
Review shared-service allocation rules, target percentages and cost records so internal chargeback stays separate from Azure invoice reconciliation.
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.
Use a read-only worksheet to compare Azure OpenAI, Amazon Bedrock, and Vertex AI costs, separating token categories, cache charges, and provisioned capacity.
Separate repeated imports from valid charges on one traffic path. Reconcile a closed invoice period while keeping credits, corrections, adjustments and distinct service fees visible.
Review backup charge types, stopped protection and Archive eligibility before proposing retention changes that could remove needed recovery points.
Before approving a cloud commitment, compare historical recommendations with lasting demand and check whether recent purchases have been reflected.
Identify provisioned Azure SQL databases and pools worth a sizing review using workload peaks, storage limits and purchasing-model checks, not low CPU alone.
Use a read-only worksheet to compare monthly cloud costs and separate usage changes from prices, discounts, credits, and invoice adjustments.
Check cost denominations before combining cloud exports, choose billed or effective cost, and record a conversion policy that keeps totals traceable.