Your next GPU purchase might already be sitting idle.
AI workloads run on GPUs on-prem and in the cloud, but only the cloud side shows up on a bill. Visual One Intelligence® is extending Hybrid FinOps™ to the infrastructure AI runs on: a GPU telemetry collector built on NVIDIA DCGM is live today, and GPU utilization and cost reporting and AI token usage tracking are in development. We are inviting a limited group of early adopters to shape them, free for participants.
Sound familiar?
Every stage of GPU adoption asks a new question.
Some clusters start with a few experimenting teams and grow into production. Others run fraud scoring, contact center assistants, and risk models at full load from day one. The questions are the same either way; high-volume adopters just reach them in days instead of months.
Few teams, low utilization.
- Set a utilization baseline and tagging
- Compare on-prem cost with cloud
Many teams, mixed schedulers.
- Find orphaned capacity
- Normalize cost across resources
- Begin showback
Production and research share it.
- Move to chargeback
- Forecast and plan purchases
- Weigh buying against bursting to cloud
Wherever your cluster sits on this climb, the beta is where these answers get built: telemetry from the collector today, reporting shaped by the teams using it. Apply for a seat
What the beta is building toward.
“Do we really need more GPUs?”
The goal: measured utilization and fully loaded cost of the GPUs you already own, so a purchase request becomes a data question instead of a budget fight. The collector that measures them is built; the reporting on top is what the beta shapes.
“Who is using our GPUs, and who should pay?”
Chargeback and showback by business unit, owner, or cost center is how VOI treats storage and compute today. The beta extends that same tag model to GPU workloads, so “who pays for AI” gets an answer.
“Should this workload run on-prem or in the cloud?”
VOI already prices on-prem infrastructure fully loaded: acquisition, maintenance, power, floor space, and personnel, with the same rigor as cloud. The beta brings GPU-level placement decisions into that view.
“What will AI cost us next year?”
Budget-versus-actual, trends, and forecasting across the hybrid environment are platform capabilities today. AI token usage tracking, in development, extends them to what AI workloads actually consume.
AI Cost Governance for the Post-Merger Enterprise
How ITFM discipline, chargeback, showback, and defensible TCO extend to the infrastructure AI runs on. The thinking behind this beta, in three pages.
Three steps, and your fingerprints on the result.
Participation is free. You get early access and a direct line to the people building it; we get real environments to build against.
Apply
Tell us about your GPU footprint in the short form below. We will set up a call to walk through what we have built and confirm fit.
Deploy
The DCGM-based collector runs in your environment, stood up alongside our team, capturing GPU telemetry at the source.
Shape
You work directly with our product and analyst teams, with early access to GPU utilization and cost reporting as it lands, and a real say in how it takes form.
Join the GPU Visibility beta.
Built for the IT and infrastructure leaders who own the GPUs but cannot see how they are used or what they really cost. Seats are limited, participation is free, and the collector runs in your environment on a platform that is SOC 2 Type II certified.
Help shape GPU cost visibility.
Tell us about your environment and we will set up a call to show you what we have been working on. No cost, no commitment beyond honest feedback.
Not ready to talk? Take the self-guided tour or price it yourself.