Start with the service, not the device
AI infrastructure spans more than accelerators. A usable service also depends on compute, network, storage, images, clusters, identity, policy, observability and lifecycle operations.
Enterprises may package these capabilities as internal AI-factory services. NeoClouds may expose them as customer-facing GPU or AI cloud products. The underlying operating problem is similar: convert infrastructure into a governed consumable service.
Day 0, Day 1 and Day 2 must connect
Day 0 defines architecture, standards and service blueprints. Day 1 provisions the approved environment. Day 2 handles health, incidents, capacity, upgrades, optimization and policy drift.
When those phases live in separate systems, context gets lost at each handoff. A shared operating layer keeps design intent connected to the running environment.
Operate capacity as a product
For providers, tenant isolation, quotas, catalog, metering and lifecycle automation become as important as provisioning. For enterprises, governance, team self-service and cost accountability become central.
In both cases, autonomous operations can coordinate routine actions while preserving human control for higher-risk changes.
What to remember
- GPU infrastructure is a multi-domain service, not a single resource type.
- Connect architecture intent to provisioning and Day-2 operations.
- Provider models need tenant, quota, catalog and usage controls.
- Enterprise models need governed self-service and cost accountability.