A per-seat subscription charges you for who has access. A credit system charges you for what actually gets used — which fits how AI usage actually varies.
Most software is priced per seat — you pay for the number of people who have access, regardless of how much any individual person actually uses it. This model works reasonably well for tools where usage is fairly uniform across users. It fits AI usage much less naturally, because AI usage varies enormously from one person, and one task, to the next.
A quick question and a long, document-heavy analysis consume genuinely different amounts of underlying compute, even though both are "one interaction" from a seat-based perspective. A person who uses the platform for a handful of quick lookups a day and a person running extensive multi-step agent workflows are using dramatically different amounts of the underlying system, but a per-seat model would charge them identically. That mismatch either overcharges light users to subsidize heavy ones, or undercharges heavy usage in a way that isn't sustainable.
9xAI is priced in credits: every message, every generated image, every digitized page has a specific, transparent credit cost, shown before you confirm the action. Subscription tiers include a set number of credits per month at a given price point; additional credits can be purchased as top-ups, at a discount that increases with the amount purchased; and metered features like document digitisation and voice draw from the same credit balance rather than requiring a separate account or subscription.
Every account starts with a full-credit trial period, moving to a lighter ongoing free tier afterward — enough to keep using the platform meaningfully without a subscription, while making it clear what a paid tier actually unlocks in terms of volume.
A credit system only works well if the cost of every action is genuinely clear before it happens — otherwise it just replaces one kind of pricing confusion with another. This is why every action in 9xAI shows its credit cost up front, rather than after the fact. The goal isn't just fair pricing in the abstract; it's pricing you can actually predict and plan around, whether you're an individual managing a monthly budget or an enterprise forecasting usage across a team.