Move batch and fine-tuning jobs to Spot GPUs

The problem

Training, fine-tuning, and batch-scoring jobs run on-demand at the premium GPU rate even when they don’t need to. These jobs are often already interruptible — they checkpoint, they run off-hours, they can retry — which makes them ideal for Spot capacity at up to ~70% off. But spotting which jobs qualify, and doing it safely, is rarely anyone’s job.

How Lizrd fixes it

Lizrd identifies GPU workloads that look batch-shaped and interruptible read-only, and recommends moving them to managed Spot training (SageMaker, Vertex, Azure ML) with automatic resume — with the evidence, the confidence level, and the exact configuration change. On-demand stays the default for anything latency-sensitive; only genuinely interruptible work is flagged.

The outcome

The same jobs finish for a fraction of the GPU cost, with checkpoint-and-resume handling interruptions, and Lizrd validates the realized savings against your bill.