Training large AI models requires managing complex distributed compute jobs across thousands of GPUs. Current solutions are either too general (Kubernetes) or proprietary to big tech.
A SaaS offering could provide pre-configured job orchestration, automatic checkpointing, and cost monitoring specifically for AI workloads. Researchers at mid-sized labs would pay to avoid reinventing these systems.
The MVP could be a simple dashboard showing GPU utilization and costs across cloud providers. Later add automated failover between regions.
The biggest risk is competition from cloud providers building their own vertical solutions.