ModelRefs / Batch Inference Stack — Architecture Pattern
Batch Inference Stack — Architecture Pattern
Queued batch jobs, sharded workers, idempotent retries, and cost-optimized off-peak scheduling. Idempotent retries, exactly-once delivery, and SLA dashboards.
Overview
Asynchronous batch inference: a job queue dispatches work to sharded workers using cheaper batch APIs where available. Idempotent retries, exactly-once delivery, and SLA dashboards.
When to use it: You need high-throughput inference where latency is not critical.
Pattern details
| Pattern class | batch |
|---|---|
| Difficulty | intermediate |
| Topology | event-driven |
| Also known as | bulk inference, offline llm jobs |
| Last reviewed | 2026-06-07 |
Known failure modes
- Non-idempotent jobs — Retries produce duplicates. Mitigation: Idempotency key + dedupe on results store.
When not to use it
- Using realtime APIs for million-item nightly jobs.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Batch Inference Stack — Architecture Pattern.
Frequently asked questions
When should I adopt the Batch Inference Stack?
You need high-throughput inference where latency is not critical.
What are common failure modes of Batch Inference Stack?
Non-idempotent jobs
Is Batch Inference Stack production-ready?
Yes when paired with the safety controls and observability hooks documented on the pattern page.