Why GPU spend is so hard to control
GPUs have become one of the largest and fastest-growing lines in many technology budgets. Yet most organisations cannot answer basic questions with confidence: how many GPUs do we have, who is using them, and how much of that capacity is sitting idle?
The estate is usually fragmented across several clouds, regions, on-premise clusters and teams, each with its own tools and reports. Capacity is reserved just in case, experiments are left running, and finance sees the bill long after the decisions that drove it.
- Idle or underused GPUs that are still being paid for
- Over-provisioned capacity reserved for peaks that rarely arrive
- No shared view between engineering, platform and finance teams
- Cost surprises discovered only when the invoice lands
GPU FinOps: one view for engineering and finance
FinOps practice works when the people who spend and the people who pay share the same facts. Prizm gives your GPU estate a single source of truth, so platform engineers, AI teams and finance can see the same picture of capacity, usage and cost.
With that shared view, conversations change from arguing over whose numbers are right to deciding what to do next: which workloads deserve priority, where capacity can be released and how budgets should be allocated across teams.
GPU utilisation monitoring that leads to action
Dashboards alone rarely reduce a bill. GPU utilisation monitoring is only useful if it points to clear, safe actions. Prizm highlights where GPU spend is being wasted and recommends changes your teams can review and approve.
Every change is governed and reversible. Your people decide what is applied, a record is kept of what changed and why, and any change can be rolled back. That makes it practical to act on savings opportunities without putting production AI workloads at risk.
Reduce GPU cloud costs without slowing AI delivery
Cost cutting that stalls model training or inference is a false economy. The goal of GPU cost optimisation is to put the capacity you pay for to productive use, so AI teams get what they need while waste is taken out of the system.
It also changes planning. When you can see how existing GPUs are really used, decisions about buying, reserving or releasing capacity rest on evidence rather than guesswork, and new AI projects can be budgeted with more confidence.
Prizm is designed for mixed estates: public cloud, private cloud and on-premise clusters, managed as one. It suits AI-native companies, enterprises scaling generative AI, research organisations and service providers running GPU capacity for others.
- Clear ownership of GPU spend by team, project or workload
- Waste surfaced continuously, not in a quarterly review
- Changes approved by people and fully reversible
- One approach across cloud and on-premise
How to get started with GPU cost optimisation
We begin with a discovery conversation about your GPU estate, how it is used and where cost or capacity pressure is felt most. A private walkthrough on your own data, under NDA, shows what Prizm reveals about your environment.
A pilot on part of your estate, such as one cloud account, cluster or team, lets you validate the value before extending it across the organisation. Prizm can be delivered as managed cloud or on-premise, scoped to your requirements.
