AI teams often wait on GPU quotas, networking exceptions, and one-off sandbox accounts. That friction pushes experiments into shadow IT—and security finds out last.
A strong internal platform offers approved templates: a GenAI sandbox with identity, logging, and spend caps; a training job pattern with shared storage; and a model promotion path into staging with evaluation evidence attached.
Keep one control plane. Reuse the same Terraform modules, GitOps repos, and observability stack the rest of engineering already uses.
When paved roads are clear, AI teams ship faster and platform teams sleep better.
Publish a service catalog entry for GPU pools, vector stores, and model registries with clear request SLAs.
Build cost showback per team early; AI spend surprises destroy trust in the platform.
Security review should be embedded in templates—prompt injection and data egress controls included—not bolted on after a demo goes viral.
Key takeaways
- Paved roads beat ticket queues for GPUs, secrets, and evaluation environments.
- AI teams still need identity, networking, and cost guardrails like everyone else.
- Treat the AI platform as a product with a backlog, SLAs, and docs.
FAQ
What is a golden path for AI teams?
A supported way to get a training or serving environment with logging, identity, secrets, and cost quotas—without opening five ops tickets.
How do we avoid a shadow AI cloud?
Offer a fast internal path that is safer and cheaper than personal cloud cards, and make exceptions time-boxed and visible.