We’ve used platforms that let you prototype workflows with AI models pretty easily, but I’m trying to understand the licensing reality when you move from testing to running these at scale.
In prototype, you’re making maybe 100 API calls a month. Licensing doesn’t matter much. But what happens when you’re making 100,000 calls a month? Or a million?
If you’re paying per-query to multiple different AI model providers, that cost structure changes everything. Suddenly you’re thinking differently about which model to use for each task. You might start substituting cheaper models even if they’re slightly less capable. You optimize query length to save cents. You cache results more aggressively.
But if you had one subscription that covered all those API calls, would you make different choices? Would your workflows be more effective if cost-per-query wasn’t a constant background anxiety?
I’m trying to figure out if there’s a hidden efficiency gain that comes from not having to optimize every single query for cost. Like, do you build better automations if you don’t have to think about licensing overhead?
What’s your actual experience when you moved automation workflows from prototype to production scale?