When using OpenClaw for AI model training or inference, reducing cloud computing expenses is a top concern. Spot instances typically save 50% to 80% compared to on-demand pricing, but require reasonable scheduling and fault-tolerance strategies. This article shares several practical spot instance cost-saving tips based on real-world scenarios.
Choose Spot Instance Types and Regions Wisely
Different instance specifications show varying price fluctuations in the spot market. Based on your workload needs, prioritize instance types that have ample supply and stable spot prices. For example, for batch data processing or short-lived inference tasks, you can opt for older-generation GPU instances, which often offer higher spot discounts. Meanwhile, compare prices across regions — some regions have cheaper spot resources due to supply and demand, but watch out for the impact of network latency on performance.
Use the OpenClaw console to check historical spot price curves, and avoid peak hours (e.g., weekday mornings) when launching instances. If your task can tolerate interruptions, deploy during low-traffic periods (e.g., late night) to further reduce the risk of preemption.
Combine Auto Scaling with Checkpointing
The biggest issue with spot instances is that they can be reclaimed at any time. To avoid disrupting critical tasks, implement checkpoint functionality at the application layer. For instance, save model weights every 15 minutes during training and persist the data to low-cost object storage. When an instance is reclaimed, the auto scaling group immediately launches a new spot instance, reloads the checkpoint, and resumes computation.
With OpenClaw's Auto Scaling service, set a minimum number of on-demand instances as a safety net (e.g., keep 1 on-demand instance) while using spot instances for the rest. This way you enjoy discounts while ensuring task completion, avoiding wasted costs from interruptions.
