Cloud cost

AI features here usually explain spend in plain language, flag anomalies or recommend changes. None of it works well without good tagging and a clear account structure, so plan those first. Check which billing permissions each tool needs, whether it can change resources or only report, and how savings-based pricing is calculated before you sign.

Cost work goes best as a shared habit rather than a one-off cleanup. Give each team a view of its own spend, review the biggest changes each month, and add a short cost check to architecture reviews for new services. Commitments such as savings plans give large discounts but lock you in, so buy them for steady baseline usage and leave spiky or uncertain workloads on demand. Watch data transfer and managed service fees as closely as compute, since they are easy to miss and hard to reduce later. When a tool reports a saving, ask how it was measured and whether it accounts for the work needed to make the change. A tool that finds real savings but gives engineers no easy way to act on them will produce reports that nobody reads after the first quarter. Pair every recommendation with an owner and a date, and track how much was actually saved.