The instinct is to protect the expensive feature. It costs you money per call, so it goes behind the wall, and the cheap stuff is free.
One team did the opposite on purpose. Their most expensive AI feature, a virtual try-on, is the free hook. Their words: the free tier gives you enough to feel the magic. The paywall sits on heavy usage of that same feature, and the social sharing loop stays free because it drives growth.
Read that again, because it inverts two rules at once. The costly thing is the giveaway. The wall is on volume, not capability.
Why the usual instinct fails
If you gate the impressive feature, your free tier is a demo of the boring parts. The user's honest reaction is "I don't get it," and they are right, because you showed them the part that does not matter.
The evidence on trial timing says the same thing from another angle: the specific failure is gating before evaluation. Not "gating." Gating too early. The user has to reach the moment where the thing is obviously good before you ask for money. If your paywall lands before that moment, your conversion rate is measuring your paywall, not your product.
And there is a sharper version. "Free" positioning that hits a paywall reads as bait. A free tier that walls you mid-flow does more damage than having no free tier at all, because you spent the user's trust to get their click.
Three pricing moves with real numbers behind them
A one-time credit pack priced at roughly five months of the subscription. From an operator who shipped it:
"I priced the pack roughly equivalent to ~5 months of the subscription, and it immediately unlocked a different buyer segment: people who use the tool in bursts, or just hate recurring..."
It ended up roughly a 50/50 revenue split against the subscription. That is not a rounding error. That is half your revenue coming from a buyer group a subscription-only page loses silently, because they never complain, they just leave.
A $3 non-refundable priority-access fee instead of a free waitlist.
"I put a tiny $3 priority access filter and 7 people paid so far! Mainly doing this to validate and find the early believers."
Seven people is a small number that tells you more than three hundred waitlist signups. A waitlist measures curiosity. Three dollars measures intent.
Ship free with per-feature analytics, then paywall from data. Instrument everything while free, and once the base is big enough, put the wall where usage says the value is. You stop guessing which feature earns money and start knowing.
The honest caveat
Those three come from a small late addition to the corpus I built this from: 37 cards out of 4,278, and they did not go through the adversarial refutation stage that the rest did. They are strong leads, not verified patterns, and I would rather say that than let them sit next to better-tested claims pretending to be equals.
That distinction matters more than it sounds. When I ran twenty candidate growth patterns through independent re-checking, eleven of them died. Confident-sounding and wrong is the default state of advice in this space, including advice with a quote attached.
I put the full set, verified and unverified clearly labelled, into an MIT Claude Code plugin at why-isnt-it-selling. It includes the eleven that died and what killed them.
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