Which AI model you prefer or use the most for your automation flow

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I have a quick question for community:

Which AI platform or model is being used the most for building automation workflow.

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depends heavily on what "automation" means to you. for pure API-call orchestration (webhooks, data pipelines, scheduled jobs) I lean toward whatever's cheapest and fast enough since the task rarely needs real reasoning. for anything where the workflow has to make judgment calls mid-run - deciding how to handle an edge case, writing something a human will read - that's where the model quality actually shows up and it's worth paying for the better one. what's your automation flow actually doing?

 Thanks Gal, this is honestly one of the sharpest ways I've seen this framed. The distinction between "cheap and fast enough for API orchestration" vs "worth paying for judgment calls mid-run" is exactly the mental model more people should be using instead of picking one model for everything.

To your question, our automation flow mostly handles enterprise use cases where mid-run judgment matters a lot: routing tickets, extracting structured data from unstructured docs, and multi-step decision chains that touch systems of record. So we've been leaning on stronger models (Claude Sonnet 4 and GPT-4o class) for the reasoning-heavy steps, and cheaper ones for the mechanical bits. Would love to hear which model you land on when the judgment-call moments come up in your flows.

I like Zapier or gumloop

 Thanks Aaron, honestly Zapier and Gumloop are both solid picks depending on the use case. Zapier for anything where you already have a mature app ecosystem to integrate with, and Gumloop when you need more AI-native workflow branching without going full custom.

Do you tend to stick with the built-in AI actions in each platform, or do you plug in your own model behind the scenes? I've seen teams go both ways, and the trade-off between simplicity and control is real. Curious what worked better for you in practice.