How do you operate large batch inference jobs reliably and cheaply?

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Design batch jobs to be idempotent, chunked and resumable. Shard the input, track progress per shard (checkpoints), retry failures with backoff and dead-letter persistent failures, and write outputs atomically. Lower cost with provider batch APIs (discounted, asynchronous), self-hosted high-throughput engines on spot or preemptible GPUs (tolerant thanks to checkpointing), large batch sizes, deduplication of inputs, and small models where quality allows. Validate output quality on samples before and during the run.

Pipeline elements

Input preparation: dedupe, filter, estimate the token volume and cost before launching.
Sharding: e.g. 10K items per shard; a manifest tracking status (pending/running/done/failed).
Execution: workers pull shards; concurrency tuned to rate limits or GPU throughput.
Validation: schema checks on outputs; a re-queue for failures; quality sampling.
Checkpointing: completed shards are never redone; spot interruptions resume from the last shard.
Observability: progress %, throughput, error rates, cost so far vs estimate, ETA.
Output: write to a staging table, then swap or publish when done (atomic for consumers).

Cost levers

Lever	Notes
Batch APIs	Often ~50% cheaper with up to 24h turnaround
Spot GPUs	Large discounts; need checkpointing
Smaller / distilled model	Evaluate quality first on a sample
Prompt efficiency	Short instructions; multiple items per call when quality holds
Prefix caching	Same instructions reused across items

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How do you operate large batch inference jobs reliably and cheaply?

Pipeline elements

Cost levers

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