Cloud Cost Optimization for ML Workloads

Start with cost visibility. Tag resources with project, owner, model, and environment; associate cloud bills with training jobs and inference traffic. A GPU's hourly rate is only part of total cost. Include data storage, snapshots, image registries, logs, network transfer, idle endpoints, orchestration, and engineering time. Track cost per completed experiment or prediction alongside quality and latency. Right-size compute to the workload. A larger GPU may have higher hourly cost but finish a job sooner; conversely, a smaller device may take so long that total cost rises. Benchmark time-to-quality, not just time per step. For inference, measure cost at realistic batch size and traffic. Scaling to zero can reduce idle spend but introduce startup latency, while a fixed minimum capacity may be appropriate for strict service objectives. Schedule development and training resources to run only when needed. Use idle shutdown, autoscaling, and queueing where they match work patterns. Temporary resources can still leave disks, snapshots, endpoints, or logs behind. Define retention and cleanup policies, but preserve required checkpoints and data provenance. Storage tiering can lower long-term costs while increasing retrieval time or request fees. Interruptible capacity may reduce compute cost for jobs that can checkpoint and resume. It is not appropriate for every workload, and restart overhead must be included. Data movement can also dominate: colocate compute and data when feasible, reuse cached datasets, and avoid unnecessary cross-region transfers. Follow access and privacy rules while doing so. Use current provider pricing because regions, instance types, discounts, and service terms change. Set budgets and alerts, then review real bills after experiments. Cost optimization is an iterative measurement process, not a one-time selection of the cheapest machine.