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What is Credyt?
Credyt is real-time monetization infrastructure for AI products. It connects what your AI usage costs you to what you bill your customers, so you can watch spend as it happens, enforce budget limits before usage is authorized, and charge across different pricing models rather than relying on a flat fee.
What it actually does
The product is organized around three jobs, per Credyt:
- Observe: real-time cost streaming for every API call, model and customer, so you see what each user costs as it happens rather than at month end. The page shows a live event log (timestamp, task, model, cost) and customer-level profitability.
- Control: hard budget ceilings per customer, per agent or per team. Balances are checked before usage is authorized, so insufficient balance blocks the action instead of producing an overrun later.
- Bill: support for multiple pricing models, so you can charge per token, per request, per seat or per output.
Who it suits
The page names two audiences: "vibe coders" and AI companies. In practice, the strongest fit is a small team shipping an AI feature whose per-call costs scale with usage while revenue is still a flat subscription. If your biggest customer could quietly cost more in compute than they pay, per-customer cost attribution and pre-usage balance checks address that directly. Teams that already have solid metering and billing in place, or whose AI costs are trivial relative to revenue, have less to gain.
Trade-offs to weigh
Adopting a monetization layer means your authorization path depends on it: if the balance check fails or lags, usage gets blocked or mispriced. That is the intended behavior for stopping runaway spend, but it makes reliability and latency of the check worth testing before you route production traffic through it. It also adds a system between your app and your customers' payments, which is worth it mainly if usage-based or hybrid pricing is central to your model.
Next step
List your current pricing model, your per-customer compute cost, and whether you can block usage mid-session today. If you cannot answer the second or third, start with the observe-and-control side on a single high-usage customer before changing how you bill everyone.
How does Credyt help me decide how to price my AI product?
Credyt helps you decide how to price your AI product by connecting what each customer costs you to what you charge them, so pricing becomes a decision you can measure rather than guess. Its core move is real-time cost streaming: every API call, model and customer is tracked as it happens, so you can see per-customer profitability live instead of at month end. That lets you test a pricing model—per token, per request, per seat, per output—against actual usage costs and see whether it holds up.
What that looks like in practice
- Pick a model with evidence. If a customer generates $5K/month but costs $6K in compute, the page's own example, you can see that before it becomes a pattern and adjust the model or the customer.
- Set exposure limits. Hard budget ceilings per customer, agent or team are checked before usage is authorised, so a pricing experiment can't turn into runaway spend.
- Bill across models. Credyt supports billing across the pricing models you use today or might adopt later, which suits teams still unsure whether to charge flat, per seat or per usage.
Trade-offs to weigh
This is monetization infrastructure, not pricing advice. It gives you the data and controls to decide; you still bring the judgment about willingness to pay and positioning. It also makes most sense once you have real usage flowing—if you're pre-launch with no customers, there's little to observe. The page signals a simple model ("$1 per active wallet"), so check the pricing page for current terms rather than assuming it fits low-volume products.
A useful next step
Instrument one customer segment first: stream their costs for two weeks, compare against what they'd pay under two candidate models, and let the margin picture pick the winner. Teams weighing build-versus-buy for this layer often compare against general billing tools such as Stripe for payments and OpenMeter for usage metering, since Credyt's differentiator is the real-time cost-versus-revenue link rather than checkout itself.
How can I stop a single customer from running up runaway AI API costs?
The most reliable way to stop runaway AI API costs from one customer is to check their balance or budget before each costly call is authorized, not after the invoice arrives. Credyt describes exactly this model: hard budget ceilings can be set per customer, per agent or per team, and if the balance is insufficient the action is blocked rather than allowed to overrun a threshold.
Practical setup
- Set a hard ceiling per customer, not just an alert. Alerts tell you after the money is spent; a pre-call block prevents it.
- Meter every billable event (each model call, each agent run) against that ceiling in real time, so the limit reflects actual consumption rather than a monthly estimate.
- Decide what happens at the limit: block the request, degrade to a cheaper model, or prompt the customer to top up. Blocking is safest for unknown or new accounts; degradation preserves the experience for trusted ones.
- Watch cost per customer against revenue per customer. Credyt's own example is a customer generating $5K/month while costing $6K in compute. A ceiling won't fix a negative-margin account, but per-customer cost attribution will surface it.
Trade-offs to weigh
Hard caps protect you but can frustrate legitimate heavy users mid-task, so pair them with a clear top-up path or tiered limits. Real-time pre-authorization checks add a step to every call, which matters for latency-sensitive products; batching checks per session rather than per request is a common compromise.
If you want to compare approaches, Credyt focuses on real-time cost streaming plus pre-usage authorization across pricing models. General API gateways and cloud budget tools typically offer alerts and post-hoc caps, which are useful backstops but weaker against a single customer spiking spend within a billing period.
Next step: identify your most expensive call type (often a large-model or long-context request) and put the first hard ceiling there, since that is where a single customer can do the most damage fastest.
How do I see which customers are actually profitable after accounting for compute costs?
Track profitability per customer by joining usage-level cost data to revenue at the same granularity, then reviewing margin by customer rather than in aggregate. A customer is only "profitable" once you subtract the real compute cost of their activity from what they pay—ideally continuously, not at month end.
What to measure
- Revenue per customer across every pricing model you use (subscription, usage, seats, credits).
- Direct compute cost per customer, attributed per request, model and agent—not averaged across all users.
- Contribution margin = revenue − direct compute, per customer, per period.
- Cost intensity: cost as a share of revenue, so a $5K account costing $6K stands out next to a $500 account costing $100.
A practical workflow
- Tag every call. Attach customer, agent/team, model and event type to each API call at the point of use.
- Stream cost as it happens. Price each call with current model rates so cost is visible in real time, not reconstructed later.
- Join to billing. Map invoices, credits and subscriptions to the same customer IDs.
- Review a margin table weekly. Sort by margin, then by cost intensity; investigate the top spenders first.
- Act. Reprice, add limits, or renegotiate the accounts where cost outruns revenue.
How Credyt fits
Credyt's page frames this as its core problem: without per-customer cost attribution, "your biggest customer might be your biggest loss," citing an account generating $5K/month while costing $6K in compute. Its Observe capability streams cost per API call, model and customer in real time, with customer-level profitability and anomaly detection; Control adds hard budget ceilings checked before usage is authorised, so a customer can't run past their balance. It supports billing across multiple pricing models. The team cites backgrounds at Visa, Checkout.com, TrueLayer and SumUp. A concrete reader scenario: a founder running a document-analysis agent who discovers their top enterprise account is margin-negative only after the quarterly invoice—streaming cost per customer would have surfaced it in week one.
Decision criteria
| Situation | Better fit |
|---|---|
| Usage-based AI product with variable per-call costs | Real-time cost attribution plus pre-authorisation limits |
| Flat-fee SaaS with predictable infrastructure | Standard analytics and billing may suffice |
| Many customers, mixed pricing models | Unified view joining cost and revenue per customer |
| Occasional large accounts you can review manually | Spreadsheet reconciliation is workable short-term |
If you already use a billing platform, check whether it exposes per-customer usage events you can join to model costs; if not, a dedicated layer like Credyt is the alternative. Start by instrumenting one high-spend customer end-to-end this week—if you can't produce their margin on demand, that's the gap to close first.
How does Credyt bill customers in real time across different pricing models?
Credyt bills in real time by checking a customer's balance before usage is authorized, then charging against it as each AI call happens. On Credyt, the page describes this as the core mechanism: if the balance is insufficient, the action is blocked rather than allowed to run up a debt. That inverts the usual SaaS pattern, where usage accrues and you invoice at month end. Instead, every API call, model invocation or customer action is metered live, and the charge is settled against the wallet at that moment.
What "across pricing models" means here
The page says Credyt lets you bill "across every pricing model you use today or might use tomorrow," and cites per-token, per-request, per-seat and per-output as the choices builders are weighing. The practical reading: the metering layer captures the underlying event (which model, which customer, what it cost), and the pricing model is a layer on top that converts that event into a charge. That separation is what lets you change how you charge without rebuilding the billing plumbing.
The page's own example table shows the granularity: a timestamped stream of events (contract analysis, document summary, chat response) each with a model and a dollar cost, feeding a live P&L. That same event stream is what would drive per-token billing, and the customer-level rollup is what would drive per-seat or flat-fee billing with usage caps.
Where real-time billing changes the trade-off
| Approach | When money moves | Main risk |
|---|---|---|
| Traditional invoice-at-month-end | After usage, on a billing cycle | You front the AI costs; a heavy user can cost more than they pay |
| Real-time balance check (as described) | At or before each usage event | Users hit a hard stop mid-session; needs a top-up path that doesn't frustrate them |
The second model solves the "accidentally become a bank" problem the page names — you stop financing your users' compute. The cost is friction: a customer who hits zero mid-task gets blocked. Whether that's acceptable depends on your audience. A developer tool or internal agent can tolerate a hard ceiling; a consumer app probably needs auto-recharge or a grace buffer so a blocked call doesn't read as a broken product.
A concrete scenario
Say you run a document-analysis product and your heaviest customer generates $5K/month in revenue but $6K in model costs. Under monthly invoicing you find out at month end. Under the described model, the per-customer cost stream shows the margin eroding in real time, and a per-customer ceiling stops the loss from compounding before you've noticed. That's the case the page builds its pitch around, and it's the strongest argument for this design.
Next step: before adopting it, map your intended pricing model to the events you can actually emit. If you want per-output pricing, confirm you can attribute each output to a customer and a cost at the moment it's produced; if you want per-seat with fair-use caps, decide what happens when a seat exhausts its allowance. The billing layer is only as real-time as your event instrumentation. For background on usage-based billing patterns more broadly, Stripe documents metered and usage-based models, which is useful for comparing the design before you commit.
How much does Credyt cost and what counts as an active wallet?
Credyt's published pricing is $1 per active wallet, per the pricing link on its site. The page does not define what "active" means—whether it's any wallet with a balance, one that transacted in a billing period, or one that merely exists in your account. Treat the per-wallet rate as the only firm number and confirm the activity definition with Credyt before you model costs.
What to check before committing
- Activity trigger: Ask whether a wallet counts as active on creation, on first funded balance, or only after usage. This changes your bill most when you pre-provision wallets for every signup.
- Billing period: Confirm whether active wallets are counted monthly, and whether a wallet that goes dormant mid-period still counts.
- Scale shape: At $1 per wallet, cost scales with your customer count, not your AI usage. For a product with many small accounts, that's a predictable line item; for a few large accounts, it's negligible.
A concrete scenario
Suppose you run an AI writing tool and create one wallet per customer. With 2,000 customers, the wallet fee is roughly $2,000 per period at list rate—before any AI compute costs. If half those customers never use the product in a given month, the definition of "active" decides whether you pay for 1,000 or 2,000 wallets. That single definition can outweigh the per-wallet rate itself.
Next step
Pull your last three months of signups and usage, count how many accounts actually transacted each month, and multiply by $1. Compare that to your current flat-fee revenue to see whether per-wallet pricing fits your customer mix. Then ask Credyt directly how it classifies a wallet as active, and whether there's a minimum or volume tier—the page doesn't say.
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