The AI cost review cadence.

Traditional cloud cost discipline assumes the bill moves with steady infrastructure. AI breaks that assumption. A single prompt change, a new retrieval step, or an idle GPU fleet can shift spend sharply within a week. The cadence below matches each driver to the timescale on which it actually changes. The broader programme that this sits inside is the cross cloud cost optimization guide.

What does the weekly review catch?

The weekly loop is operational and short. Review token volume by model and feature, GPU utilisation against any reserved or provisioned capacity, spend anomalies above a set threshold, and any workload that appeared since last week. This is where you catch a retrieval pipeline that doubled its context window, a batch job left running on demand instead of scheduled, or a fleet of reserved GPUs sitting at 20 percent utilisation. Caught weekly, these cost days of overspend; caught quarterly, they cost a quarter. Good observability makes this loop fast, which we cover in observability for AI cost.

What belongs in the monthly unit economics review?

The monthly loop moves from raw spend to value. Compute cost per outcome, whether that is cost per resolved support ticket, per generated document, or per thousand inference calls, and track it against the previous month. Absolute AI spend will rise as adoption grows, so the absolute number alone tells leadership nothing useful. Unit cost shows whether each unit of AI is getting cheaper as you optimise model choice, caching, and prompt size. This is the number a CFO can act on, framed for finance in AI spend governance for the CFO.

What does the quarterly capacity review decide?

The quarterly loop is a commitment decision. Review whether provisioned throughput, where you pay for reserved model capacity rather than per token, still beats on demand inference at your current volume. Review GPU capacity reservations against the forward forecast, because reserving GPUs you do not use is as wasteful as any stranded commitment. Decide which AI workloads have stabilised enough to move from on demand to a committed instrument, and which are still too volatile to commit. Capacity reservations and provisioned throughput carry the same use it or lose it risk as any cloud commitment, so coverage follows a defensible forecast, never a hope.

The three loop cadence

One more discipline cuts across all three: find shadow AI. Teams spin up models and API keys outside the platform, and that spend hides until the bill arrives. The weekly new workload check is your first line; a quarterly account sweep is the backstop. Keeping AI facts consistent and current, naming the instruments and the year, is what makes the cadence defensible to both engineering and finance.

Install the cadence with us

We set up the weekly, monthly, and quarterly AI cost loops on your real usage, wire the observability that makes them fast, and hand the rhythm to your team. We take zero provider commissions. Our guarantee: we reduce your cloud spend or we reimburse our service fee.