Practical answers for managing Azure, AWS, Google Cloud, and AI costs.
Explain a monthly increase with reconciled usage, rate, coverage and credit changes, using AWS CUR 2.0 and Google billing export queries.
WITH params AS (
SELECT TIMESTAMP '2026-07-01 00:00:00' AS base_month,
TIMESTAMP '2026-08-01 00:00:00' AS comp_month
),
src AS (
SELECTWhy NAT data processing survives consolidation, with the gateway endpoint break-even, the cross-AZ penalty and the CUR columns that separate the two meters.
Trace an Azure VM reservation gap through daily usage, billing scope and size ratios, then decide between rescoping, exchanging and waiting.
Separate NAT Gateway-hour, processed-GB, and data-transfer charges so teams investigate the cost driver before changing network paths.
Review activation, historical gaps, and CUR 2.0 line-item values before using AWS resource tags to assign costs to teams.
Compare share-level utilization with snapshot storage in Azure Monitor before reviewing provisioned capacity. Keep account totals out of share-level decisions.
Match Compute Engine resource-based CUD scope to intended project coverage and verify shared reservation projects use the same Cloud Billing account before a move.
Review VM size suggestions against peak demand, compatibility and restart needs. Learn why estimated savings and empty recommendation lists need care.
Classify Cloud Run functions by documented version and console area before comparing costs. Keep pricing-family labels separate from API history and billed-SKU confirmation.