AI & CLOUD FINOPS
Cut spend, not velocity, across cloud, GPU and tokens.
Cost visibility and right-sizing built into operations, extended to the two line items AI adds (GPU and LLM/token spend) so AI scales without a runaway bill.
Total Cloud SpendLast 12 Months
Optimization
Implemented
Illustrative shape of a FinOps engagementImplemented
What changes
Spend bends down after optimization
Without slowing delivery
GPU / LLM spend
Attributed per model and endpoint
Idle GPU caught before billing
The Problem
Cloud bills creep up quietly: idle resources, oversized instances, no clear owner. AI makes it sharper: idle GPUs and unmonitored token spend are the fastest-growing waste in modern estates, and finance notices only after it's baked in.
What We Do
- Cost visibility by team, service and environment, and by model and endpoint
- Rightsizing and scheduling to kill idle compute and GPU
- Commitment and savings-plan strategy
- LLM and token spend monitoring with guardrails
- Cost guardrails that catch waste early
How It Works
- 1MeasureCollect & normalize cost data
- 2AttributeAssign clear cost ownership
- 3OptimizeRightsize & reduce waste
- 4GovernEnforce guardrails continuously
Outcomes
- Meaningful, measurable spend reduction: cost cut without slowing delivery
- Clear cost ownership across cloud and AI
- Waste caught before billing
Cost discipline is what keeps an AI programme alive past year one. how our forward-deployed engineers take AI from pilot to production
See where the money goes, then cut it.
Talk to an engineer