Technology / GPU Infrastructure
A single H100 instance can run $30 to $50 an hour on demand - 60 to 100 times the cost of a standard compute instance. Zolix tracks GPU utilization continuously and tells you exactly where that spend is going, without installing anything inside your training or inference environment.
Idle allocation
Reserved but not computing
Wrong hardware fit
Training GPUs serving inference
Discount coverage
Reserved vs. on-demand baseline
Average GPU utilization across production Kubernetes environments runs as low as 5%.
Read-only IAM roles
Training vs. inference split
Multi-cloud pricing compare
The Scale of the Problem
5%
average GPU utilization reported across many production Kubernetes environments, meaning most teams are paying for capacity they almost never touch.
55-80%
the share of enterprise AI GPU spend now going to inference rather than training, a shift from just a few years ago.
20-40%
how much ancillary costs like storage, data movement, and experiment tracking typically add on top of raw GPU compute spend.
GPU spend is shaped by more than the accelerator itself. Zolix connects the layers around compute so teams can see where capacity, data, and platform choices are creating cost.
GPUs get reserved for a job, and the reservation often outlives the job itself. An instance sitting allocated but not actively computing is the single biggest source of waste in most GPU environments, and it's invisible unless something is watching utilization in real time.
It's common to see eight GPUs serving inference traffic that two could comfortably handle, because nobody's gone back to check since the initial deployment. Inference workloads rarely get resized after launch the way training jobs get reviewed before a big run.
Training and inference have fundamentally different hardware needs - A100s and H100s make sense for large training runs, while L4 or L40S instances are often more cost-effective for inference. Using training-grade hardware for inference workloads is a common and expensive mismatch.
Reserved capacity and committed-use pricing can cut 30 to 50% off baseline GPU costs, but committing requires confidence in your baseline usage - confidence most teams don't have without granular utilization data to back it up.
How Zolix Responds to Each Layer
Zolix monitors GPU utilization in real time and flags instances allocated but not actively computing for extended periods - the kind of waste that typically goes unnoticed until a monthly bill review, if it's noticed at all.
Zolix compares your current inference GPU allocation against actual request volume and latency requirements, flagging over-provisioned deployments and recommending a sizing that matches real traffic rather than a launch-day estimate.
Because these two workload types have completely different cost profiles and optimization strategies, Zolix tracks them separately from the start - so a spike in one doesn't get lost inside a single blended GPU line item.
Zolix flags workloads running on hardware that doesn't match their actual compute pattern - training-grade GPUs serving inference traffic, or inference-optimized instances straining under a training job they weren't sized for.
Zolix analyzes your actual GPU usage baseline over time and recommends where Reserved Instance or committed-use coverage would pay off - backed by real utilization data, not a guess about future usage.
Multi-Cloud GPU
Most teams don't run GPU workloads on a single provider, and GPU pricing varies significantly across AWS, Azure, GCP, and specialized GPU cloud providers for the same hardware class. Zolix's GPU Calculator gives real-time cross-cloud pricing comparisons, so a training run or inference deployment decision starts with actual current pricing data, not a stale internal spreadsheet.
What teams see after connecting GPU infrastructure to Zolix
24 Hours
to a first cost visibility report across every connected GPU-backed environment.
Training/Inference Split
separate cost lines from day one, instead of one blended GPU total.
Zero Agents
Read-only access - Zolix connects through read-only IAM roles, with no agents installed inside training or inference environments.
Yes. Utilization is monitored continuously, and instances allocated but not actively computing for extended periods are flagged automatically.
Yes. These workload types are tracked separately from the start, since their cost drivers and optimization strategies are fundamentally different.
Yes. Zolix flags mismatches between hardware and workload pattern, such as training-grade GPUs running inference traffic that a smaller instance could handle.
Yes. The GPU Calculator provides real-time pricing across AWS, Azure, GCP, and specialized GPU cloud providers for the same hardware class.
No. Zolix connects through read-only IAM roles, with no agents installed inside GPU-backed environments at any point.
Most teams receive a first cost visibility report within 24 hours of connecting their GPU infrastructure.
Connect your GPU infrastructure through read-only access and see exactly which instances are idle, which are mismatched to their workload, and where committed pricing would actually pay off.