What is the most cost-effective solution for running a fault-tolerant, batch processing application at scale?

Using Spot VMs for data processing is considered a cost-effective solution for running a fault-tolerant, batch processing application at scale because Spot VMs are significantly cheaper than regular on-demand instances. They allow users to take advantage of discounted pricing for spare compute capacity in the Google Cloud platform. Since batch processing workloads often can tolerate interruptions—meaning that jobs can be retried if a Spot VM is reclaimed—this approach can optimize costs while still leveraging the cloud's scalability.

This model fits well with the inherent nature of batch processing, where jobs can be executed independently and are tolerant to failure. The ability to scale up or down quickly based on workload demand combined with the cost savings from using Spot VMs makes this option particularly attractive for a wide range of applications.

On the other hand, using sole tenant machines is generally more costly as it involves dedicated physical resources, which doesn't align with the goal of minimizing expenses for large-scale batch processing workloads. Establishing a managed instance group with autohealing offers robustness, but could incur higher costs due to continuously running VMs. Setting up accelerator-optimized VMs, while capable of handling intensive workloads efficiently, would also typically result in higher operational costs, which may not be necessary for a fault-tolerant batch