The decision to run AI workloads on private cloud infrastructure rather than public cloud is increasingly common in enterprise and telco contexts — driven by data sovereignty requirements, latency sensitivity, cost at scale and the need for operational control.
But private AI cloud is not a single architecture decision. It is a family of decisions, each shaped by the type of workload you are running.
Training Workloads
Model training requires high-memory GPU clusters, fast interconnects and storage systems that can sustain the throughput of large dataset reads. In private cloud, this typically means dedicated GPU nodes — often bare-metal — with InfiniBand or RoCE networking.
Private cloud training is cost-effective at scale when GPU utilisation exceeds approximately 60–70% sustained. Below that threshold, reserved public cloud capacity may be more economical depending on workload predictability.
RAG Workloads
Retrieval-augmented generation requires a vector database, an embedding pipeline and an inference endpoint. The placement decision here is primarily driven by data governance: if the documents being retrieved contain sensitive operational data — such as network topology, customer records or incident histories — they should remain on-premises.
RAG is where private AI cloud earns its keep in telecom. The retrieval corpus is operational data. Operational data is sensitive. It belongs on infrastructure you control.
Inference Workloads
Inference has different characteristics: lower memory requirements per request, higher throughput sensitivity and latency requirements that vary dramatically by use case. A closed-loop network operation might need sub-second inference. A report generation task can tolerate seconds.
Designing for All Three
The most effective private AI cloud architectures treat training, RAG and inference as separate planes — with independent scaling, separate resource pools and clearly defined data paths between them. This separation makes it easier to optimise each workload type independently and to introduce public cloud burst capacity where appropriate.