AI node in loop without batching in n8n
Why processing items one by one through AI nodes wastes money and time
What is this issue?
When an AI node is inside a loop (SplitInBatches with batch size 1), each item makes a separate API call. This multiplies API overhead, increases latency, and can quickly exhaust rate limits and budgets.
Inefficient patterns:
•SplitInBatches(1) → OpenAI node processing one item at a time•Loop Over Items with AI call inside•100 items = 100 separate API calls instead of batched request•Sequential AI processing when parallel is possible
Why is this dangerous?
Excessive API calls
Each item triggers a separate API call with its own overhead and latency.
Rate limit exhaustion
AI providers rate-limit by requests per minute. Single-item loops hit limits fast.
Higher costs
Some AI providers have per-request minimums. Batching reduces effective cost per item.
Slow execution
Sequential calls take N × (latency) instead of log(N) × (latency) with proper batching.
How to fix it
- 1
Increase batch size
If using SplitInBatches, increase batch size to process multiple items per AI call.
- 2
Combine inputs into single prompt
Format multiple items as a single prompt and parse the structured response.
- 3
Use parallel processing
When items are independent, use n8n's parallel execution to run multiple AI calls simultaneously.
- 4
Use batch endpoints
Some AI providers offer batch/bulk endpoints—use them when available.
Scan your workflow now
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