How Credyt Helps AI Companies Observe Spend and Control Exposure

Updated

Credyt is real-time monetization infrastructure for AI products. It connects what each API call costs you to what each customer pays you, so you can see per-customer cost as usage happens, enforce hard budget ceilings before usage is authorized, and bill across the pricing model you choose. It fits teams building with AI—from solo "vibe coders" to AI companies at scale—who are fronting model costs and need visibility and control rather than month-end reconciliation.

The problem it addresses

With traditional SaaS, serving an extra user is nearly free. With AI, every request has a real cost that scales with usage while revenue often doesn't. Credyt's own framing puts it plainly: every time a user hits your app, it costs you money. Three failure modes follow:

  • No pricing model. You don't know whether to charge per token, per request, per seat, or per output, so you either don't charge or apply a flat fee disconnected from usage.
  • Fronting costs you haven't collected. You've effectively become a bank financing your users' AI consumption.
  • No per-customer profitability. Your biggest customer may be your biggest loss—for example, a customer generating $5K/month while costing $6K in compute.

Observe: see cost as it happens

Credyt streams cost in real time for every API call, every model, and every customer. Instead of waiting for month-end, you see what each user actually costs you as it occurs, spot anomalies before they compound, and view profitability at the customer level live.

The product's live event model illustrates the granularity: each row pairs a timestamp, task, model, and cost—for example, a contract analysis on gpt-5.2 at $0.0187, a document summary on claude-4-opus at $0.0412, or an image caption on gemini-2.5 at $0.0063. That per-event detail is what makes per-customer attribution possible.

Control: enforce limits before spend happens

Credyt lets you set hard budget ceilings per customer, per agent, or per team. The key mechanism is timing: the balance is checked before usage is authorized, not after an invoice is generated. If the balance is insufficient, the action is blocked—so there are no threshold overruns.

This ordering matters. Post-hoc alerts tell you that you've already overspent; a pre-authorization check prevents the spend from occurring at all. For AI products where a runaway agent loop can burn budget quickly, that difference is the core of exposure control.

How observe and control work together

The two capabilities reinforce each other:

  1. Observe establishes what each call, model, and customer costs in real time.
  2. That per-customer cost picture feeds Control, which sets and enforces ceilings against the same balance.
  3. Billing then charges customers across whichever pricing model you use today or might adopt tomorrow—per token, per request, per seat, or per output.

Because observation and enforcement share the same real-time cost data, the ceiling you set reflects actual usage rather than a stale estimate.

Pricing and getting started

Credyt lists pricing at $1 per active wallet, described on its site as "simple pricing, no surprises." The site also offers a free start path and a "see how it works" walkthrough. The page does not state further plan tiers or usage terms beyond the per-wallet rate, so confirm details on the pricing page before committing.

The team behind Credyt comes from Visa, Checkout.com, TrueLayer, and SumUp—background the company cites as experience building monetization infrastructure at scale.

Who should consider it

Credyt is a reasonable fit if you:

  • Are building an AI product where per-request model costs are material relative to revenue.
  • Don't yet have a pricing model, or have a flat fee disconnected from usage.
  • Need per-customer margin visibility before scaling further.
  • Want spend blocked at authorization rather than discovered after the fact.

If your product has negligible per-request costs or you already have mature usage metering and billing in place, the value is less obvious. The clearest case is an AI product scaling usage faster than its ability to attribute and collect costs.