Large AI input payload in n8n
Why sending excessive data to AI nodes wastes tokens and increases costs
What is this issue?
When you pass large JSON objects, full API responses, or unprocessed data directly to AI nodes, you're sending many more tokens than necessary. AI services charge by token, so this directly increases your costs.
Common patterns:
•Passing $json directly to AI prompts without filtering•Including metadata, IDs, and timestamps AI doesn't need•Sending entire API responses instead of relevant fields•Large context windows with unnecessary history
Why is this dangerous?
Increased costs
AI tokens are expensive. Sending 10x more data than needed means 10x higher costs.
Slower responses
More tokens mean longer processing time and higher latency for your workflows.
Rate limit issues
Large payloads consume your API rate limits faster, potentially blocking other requests.
Reduced accuracy
AI models can get confused by irrelevant data, producing worse results.
How to fix it
- 1
Extract relevant fields only
Use a Set node before the AI node to select only the fields that are actually needed for the AI task.
- 2
Summarize large text
For long documents, consider pre-summarizing or chunking before sending to AI.
- 3
Remove metadata
Strip out IDs, timestamps, and system fields that don't add value to the AI prompt.
- 4
Use specific field references
Replace $json with $json.specificField in your prompts to include only what's needed.
Scan your workflow now
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