How Runpod Serverless billing works when a job fails to complete

Runpod Serverless bills per second for GPU worker runtime rather than successful request outcomes, so costs accrue during cold starts, job execution, idle periods, and failed startup attempts, while queued or stopped workers are not billed. Unexpected charges commonly result from repeated startup crashes, hanging handlers, retries, duplicated or abandoned requests, always-on active workers, and aggressive scaling settings that multiply GPU usage during failures. Users can investigate spending by correlating billing history with endpoint metrics, job statuses, worker states, request timings, and logs, especially looking for cold-start churn, timeout-length executions, retries, and workers running without useful completions. Cost controls include limiting maximum and active workers, selecting appropriate GPU types and counts, maintaining execution and job TTL limits, minimizing startup time through caching or baked-in models, validating inputs quickly, and cancelling abandoned work. When spend rises unexpectedly, the recommended response is to capture diagnostic data, set active and maximum workers to zero, purge queued jobs, cancel stuck work, stop incoming traffic, and restore service gradually after testing a fix. Charges may warrant support escalation if they continue with no workers running or after scaling to zero, substantially exceed reconciled worker runtime, or coincide with a platform incident.

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