2026/01/21
Internal AI Platforms for IT Teams: Multi-Tenant GPU Chargeback in Practice
A case study on how enterprise IT teams built an internal AI platform with transparent GPU cost allocation.
Customer Profile
A global enterprise IT department serving 18 internal teams (R&D, marketing, support automation). They launched an internal AI platform to standardize access to GPU resources.
The Business Problem
- No cost visibility by team or product.
- Long queue times during shared GPU demand spikes.
- Security concerns when multiple teams shared the same infrastructure.
Baseline:
| Metric | Baseline |
|---|---|
| GPU queue time P95 | 18–25 min |
| GPU utilization | 30–38% |
| Internal chargeback accuracy | <50% |
| Compliance audit effort | 4–5 weeks |
TensorFusion Solution
- Multi-tenant isolation with policy-based GPU pools.
- Chargeback tags and usage reporting per team.
- Priority lanes for production automation workloads.
