Fan-out

Fan-out is the workflow pattern where one event spawns many parallel jobs / steps — process N records in parallel, call N APIs concurrently, generate N variants. The complementary 'fan-in' joins them back.

Fan-out unlocks parallelism without writing concurrency code. A single event ('user uploaded 50 images') spawns 50 parallel jobs that all run; fan-in waits for all 50 to complete + aggregates. Workflow engines (Inngest, Temporal, AWS Step Functions, Trigger.dev v3) provide first-class fan-out primitives that handle retries per-branch, partial-failure aggregation, and concurrency limits. AI use cases in 2026: parallel image generation (10 variants of one prompt), bulk classification (10K records at 50 concurrent), agent self-consistency (run the same agent 5 times, take majority vote). Trade-offs: fan-out can overwhelm downstream APIs without concurrency caps; debugging gets harder as parallel branches diverge.

When to use fan-out

• Parallel batch work, independent jobs.
• AI variant generation, self-consistency sampling.

Common mistakes

• No concurrency limit — fan-out hammers downstream APIs.
• Missing fan-in — workflow proceeds without waiting for all branches.

FAQ

Related terms

• Self-consistency — Self-consistency runs the same prompt multiple times at non-zero temperature and picks the most common final answer, raising accuracy on reasoning tasks.
• Background job — A background job is the unit of work in a [[task-queue]] system — a small piece of code that runs out-of-band from a request, may be retried on failure, and reports back via webhook / dashboard / database update.
• Workflow engine — A workflow engine is the orchestration runtime — n8n, Make.com, Zapier, Temporal, Airflow — that executes multi-step business processes, handles retries, manages state, and integrates with external systems.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/fan-out.md.