Their own AI cloud, on their own metal.
Standing up a private AI cloud across three regions
Their roadmap leaned on six different model vendors and the data was leaking in every direction. We built a private AI cloud across RunPod, AWS, and on premise GPUs so the company runs the models, not the vendors.
Six vendors, each with their own SDK, billing model, rate limits, and data policy. Engineering hours were going to integration churn instead of features. The CFO could not predict next quarter's spend within thirty percent. Compliance flagged data egress on three of the six. The system worked, but only by accident.
Designed a private AI cloud with three tiers. Bare metal GPUs in their own data center for the sensitive workloads. RunPod elastic GPU pools for the spiky inference. AWS managed services for the agents that needed full vendor support. A unified internal serving layer routes every request by sensitivity, latency budget, and cost ceiling. Same SDK across the company. Failover between tiers is automatic. Model registry, eval harness, and observability ship with it on day one.
Spend became predictable inside ten percent. Egress on the protected workloads went to zero. Engineering stopped writing vendor adapters and started writing features. The platform survives any single vendor going dark, and three of the six are already gone from the bill.