Serverless computing is increasingly used to run complex DAG workflows across diverse applications, and individual function execution times have grown to support them, pushing workflow makespans to hours. Open-source serverless platforms, deployed on a pool of VMs, typically use on-demand instances, and their cost increases with the makespan. Spot instances are far cheaper, but a preemption is likely before the workflow finishes. A preemption at a fan-out node or a task feeding a fan-in node stalls every parallel branch, cascading a single preemption into a workflow-wide failure. Prior systems address this challenge by migrating high-impact tasks to reliable, non-spot resources, sacrificing spot's cost advantage. We present FaaSpot, a serverless workflow execution system that runs every workflow task on spot instances with no on-demand fallback. Our key insight is that cascade-prone positions are determined by the DAG's topology and can be identified before execution. Therefore, FaaSpot analyzes the workflow DAG once at submission time and classifies each task by the impact of its preemption. Selective checkpointing, survival-aware scaling, and lineage-based retry then act on this single classification, with no runtime coordination. We evaluate FaaSpot on a trace-driven spot emulator with eight DAG workflows against four state-of-the-art baselines. FaaSpot completes 89.03% of runs at $0.018 per successful run, 21pp (percentage points) above the average baseline success rate and at a lower cost per success than every baseline, while reducing mean latency by 2.3x.

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