Ask a scoped cost question. Get a reviewable investigation.

Compare billed Azure cost for [cost group] in [month A] with [month B]. Rank the three largest service and subscription drivers, call out anomalies and unallocated spend, and tell me what to investigate next.

For [cost group], compare billed Azure cost on [anomaly date] with the prior 30-day baseline. Identify the services and resources behind the increase, treat any derived likely cause as a hypothesis, and list the evidence an owner should check.

Show unallocated billed Azure cost for [month] by subscription and service. Rank the largest gaps, include the percentage of total spend, and identify missing cost-group or owner context when it is available.

Rank the open Azure savings recommendations for [cost group] by annual savings. Include monthly savings, the evidence, caveats, and owner when available, then suggest the first three reviews to schedule.

Using billed Azure cost for [cost group] from [month start] through [as-of date], when will spend cross the [budget amount] budget at the current run rate? Show actual spend, the forecast, and the assumptions behind the date.

Using effective Azure cost for [cost group] in [fiscal period], explain the current run-rate forecast. Show actual spend through [as-of date], rank the services moving the forecast, and state the assumptions that could make it wrong.

Summarize billed Azure cost for [month] across [cost groups] and reconcile it to the total. Show allocated and unallocated amounts, shared-cost treatment, and any group whose month-over-month change needs an owner explanation.

For [cost group] in [month], compare billed Azure cost with the [budget amount] budget. State actual spend, the variance, the largest service driver, and one evidence-backed question leadership should ask next.

Compare daily billed Azure cost for [workload or cost group] during the seven days before and after [deployment date]. Rank the resources and meters that changed, and tell me whether the pattern looks persistent or temporary.

Why did billed Azure cost for [service] in [cost group] change between [period A] and [period B]? Break the variance down by subscription, resource, and meter, then list the first evidence the service owner should verify.

For the highest-value right-sizing recommendation in [cost group], summarize the affected resource, suggested action, modeled monthly and annual savings, evidence, caveats, and owner when available. List the checks an engineer should complete before changing it.

Summarize the [anomaly or savings] investigation for the owner of [cost group]. Include the period, billed Azure impact, likely driver, supporting evidence, uncertainty, and a read-only checklist for the next review.

Compare effective Azure cost for [product cost group] in [period A] and [period B]. Rank the service and resource drivers, separate one-off movement from the new run rate, and list the business context a product owner should add before judging value.

Summarize billed AI cost and connected GitHub Copilot usage for [product cost group] from [start date] through [as-of date]. Rank provider, model, and license drivers only where a live feed exists, show the simple month-end run-rate forecast, and name any provider telemetry that is not connected.

Using effective Azure cost for [project cost group], compare actual spend through [as-of date] with the [project period] budget. Show the current run rate, forecast at completion, and the assumptions the project manager should revisit.

For [product cost group], compare billed Azure cost in [recent period] with the prior [baseline period]. Separate one-off anomalies from sustained service growth and explain whether the budget baseline should stay, rise, or be investigated further.