You can validate most LLM product ideas before spending meaningful money. The trick is separating "does this feature work" from "does this feature work at production quality."
Stage 1: Does the feature work at all?
Use free or gift-credit models. You're testing whether the shape of the product makes sense — does the prompt produce plausible output, does the interaction flow feel right, do users understand what to do.
Quality at this stage doesn't matter much. You're looking for obvious breakage, not production readiness.
Stage 2: Does it hold up?
Run your eval set against two or three candidate models, including one mid-tier paid model. Compare on your own data, not public benchmarks — benchmarks measure someone else's tasks.
Build a small eval set early: 30-50 examples with expected outputs, scored by exact match where possible and by rubric where not. It takes an afternoon and pays for itself the first time you consider switching models.
Stage 3: What does it cost at scale?
Only now do per-token rates matter. Compute cost per successful task: tokens consumed × rate, divided by success rate, including retries. A cheaper model with a 10% higher failure rate can be more expensive per successful outcome.
Keeping prototype and production aligned
The cleanest setup keeps both stages on the same interface. Prototype against a gift-credit model, then swap the model id for a paid one when you promote the feature. No rewrite, no second integration.
That's the workflow our rate card is built for: gift-credit models for stage one, transparent paid rates from $0.01/M tokens for stage three, all behind one [OI]-compatible key. See compare [OI] API pricing for the full list.
Spending money before stage one is validated is the most common way AI projects die.
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