Who Is Credyt Built For?

Credyt is built for teams whose AI product incurs real, per-call costs that are not yet connected to revenue. That covers three groups in particular: vibe coders shipping AI apps, AI companies with usage-based costs, and teams scaling without knowing per-customer margins. If every user session costs you money and you bill a flat fee (or nothing), you are the target user.

The three audiences Credyt names

Credyt's site segments its audience as "Vibe Coders," "AI Companies," and "Scale." The common thread is not company size but cost structure: each API call, model invocation, or agent action costs real money, and that cost scales with usage while revenue may not.

Vibe coders building AI apps

If you built something people use but haven't settled on a pricing model—per token, per request, per seat, or per output—Credyt is positioned for exactly that indecision. The site describes the failure mode plainly: you either don't charge at all, or you apply a flat fee unrelated to usage, and either way "money is being left on the table or burned."

This group benefits most from the Control capability: hard budget ceilings per customer, per agent, or per team, checked before usage is authorized rather than after an invoice is generated. If the balance is insufficient, the action is blocked. That prevents a single heavy user from running up spend you can't recover.

AI companies with usage-based costs

The site's core framing is that AI businesses accidentally become banks: "every time a user hits your app, it costs you money," and you are fronting AI costs you haven't collected. Unlike traditional SaaS, costs scale with usage but revenue doesn't, because the two aren't connected.

For this group, the relevant capability is Observe: real-time cost streaming for every API call, every model, every customer. The site's example event log shows per-call costs attributed to specific models (e.g., a contract analysis call at $0.0187, a document summary at $0.0412), so you see what each user costs as it happens rather than at month end.

Teams scaling without knowing per-customer margins

The third problem Credyt addresses is per-customer profitability. Without cost attribution, "your biggest customer might be your biggest loss"—the site's example is a customer generating $5K/month while costing $6K in compute. If you can't see that, you are "scaling losses and calling it growth."

This group needs customer-level profitability visibility in real time, plus anomaly detection before cost spikes become problems.

Who it is probably not for

Based on the site's own framing, Credyt is a weaker fit if:

• Your product has no variable AI cost per user. If usage doesn't drive compute spend, the observe-and-control loop has little to act on.
• You already have per-customer cost attribution and hard spend limits in place. The pitch is aimed at teams hitting these problems for the first time ("It's when, not if").
• You only need static subscription billing. Credyt's stated scope is billing across "every pricing model you use today or might use tomorrow," with real-time authorization—not a replacement for a simple recurring-charge setup.

What to check before committing

The site lists pricing at $1 per active wallet and offers a free start, but confirm what counts as an "active wallet" for your model and whether your existing billing stack needs to be replaced or integrated. The team's background (Visa, Checkout.com, TrueLayer, SumUp) is presented as evidence they know where billing breaks at scale—useful context, but verify fit against your own cost structure rather than the pedigree.

A practical test: pick your heaviest-usage customer and ask whether you can state, right now, their monthly revenue against their monthly AI compute cost. If the answer is no, that is the gap Credyt is built to close.