A flat per-call endpoint for summarize / classify / extract in your n8n and Make automations
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If you run automations that summarize, classify, or pull fields out of text at volume, the LLM step is where per-token pricing turns budgeting into a guessing game: one batch of long inputs and the bill spikes. For these bounded-output jobs, a flat price per call fits better than a per-token frontier model. Here is how I wire it into n8n / Make, and when not to. Why flat-per-call fits automation Automation runs are repetitive and high-volume, and the outputs are short by nature: a su