Built for AI
“Just track it in Postgres.”
It's a 6-month trap.
Whether you're building an AI-native product or adding AI features to an existing one, every API call costs you money. Credyt gives you real-time billing, per-customer cost visibility, and a customer portal. Live in days, not months.
Revenue
$8,412.60
Costs
$6,104.38
Gross margin
27.4%
| Customer | Events | Cost | Revenue | Margin |
|---|---|---|---|---|
| Acme Corp | 18,420 | $294.72 | $460.50 | 36.0% |
| Northwind AI | 9,814 | $176.65 | $245.35 | 28.0% |
| Initech | 32,071 | $513.14 | $384.85 | -25.0% |
| Globex | 4,206 | $67.30 | $105.15 | 36.0% |
| Umbrella Labs | 12,388 | $198.21 | $223.00 | 11.1% |
The billing arc nobody warns you about:
Track A
Building an AI-native product
Start with Stripe Checkout. It handles subscriptions well, but metering and credits are entirely DIY. You’re building custom aggregation pipelines, webhook handlers, credit balance tracking, rollover logic, and access gating. What you expected to take a few days has already taken weeks.
Decide to build the credit system in-house. 3 months in, you’re deep in credit expiry rules, authorization logic, and custom asset types. 6 months in, every pricing change is a fire drill across Stripe configs, backend overrides, and internal tracking docs.
Potential investors ask “what does each customer cost us?” You can’t answer. You have aggregate provider invoices and no way to attribute costs per customer. Revenue looked fine. Margins were invisible.
Track B
Adding AI features to an existing product
Ship an AI feature behind your existing subscription. Users love it. Then you realize your margins are being swallowed by AI inference costs that were never factored into the pricing model.
Try to add usage-based pricing for the AI tier. Your billing system was built for flat subscriptions. Retrofitting takes two engineers off product work for a quarter.
VP asks “what is our per-customer AI cost?” You have aggregate provider invoices and no way to attribute them.
Neither path is unusual. Both end in the same place: billing becomes the bottleneck, cost visibility is missing, and engineering time goes to plumbing instead of product.
Credyt short-circuits both at step one.
Why AI monetization is a different problem:
Every API call has a direct infrastructure cost.
SaaS compute is marginal. AI compute is not. When your user sends a prompt, you pay $0.10 to $0.50+ for the inference before you collect a cent. At 10,000 requests per day, you’re financing $1,000 to $5,000 of your users’ AI consumption daily. Post-usage invoicing means you absorb that cost now and bill for it later. Some customers never pay.
Concurrent requests create credit depletion races.
User has $5 in credits. Two requests fire simultaneously, each costing $3. Without atomic balance checks, both pass. You’ve just extended $1 in unsecured credit. At volume, these races compound. This is not a theoretical edge case. It’s the first bug every team hits when they build their own wallet.
Your margins change every time you switch models.
Route a query from GPT-4 to Claude 3.5 Sonnet and your cost basis changes mid-session. Your billing needs to reflect per-call costs, not monthly averages. Aggregate invoices from your LLM provider tell you what you spent. They don’t tell you who spent it.
The root cause
These problems share a root cause: billing happens after the cost is incurred. You pay for inference now and hope to collect later.
Credyt inverts that. Every usage event hits a wallet balance check before the work is authorized. Costs and revenue are recorded at the same moment. No cash flow gap. No depletion races. No invisible margins.
Post-usage invoicing
You pay infra costs daily. You invoice at cycle end. Payment arrives days later.
Peak exposure
$8,500
Days at risk
34
Breakeven
~day 35
Net, day 45
+$3,750
Real-time billing with Credyt
Customers fund wallets upfront. Revenue collected before infra costs are incurred.
Peak exposure
$0
Days at risk
0
Cash positive
Day 0
Net, day 45
+$11.3k
Assumes 50% gross margin. Infra cost = 50% of what you charge the customer. Invoice payment arrives 5 days after cycle end.
Know customer profitability in real time
Every call to OpenAI, Anthropic, Google, or your self-hosted models has a cost. Credyt records cost events alongside revenue events at the moment they happen. Not at month end.
What you get:
- •Per-customer cost attribution across all AI providers
- •Real-time gross margin per customer, per plan, per model
- •Cost anomaly alerts before a single heavy user becomes a financial event
- •Board-ready reporting on unit economics
The question “which customers are profitable?” becomes a dashboard lookup, not a quarterly forensics project.
Recent events
| Time | Customer | Event | Model | Cost | Rev | Margin |
|---|---|---|---|---|---|---|
| 14:32:07 | Meridian | data_extracted | claude-haiku-4 | $0.04 | $0.08 | 50.0% |
| 14:31:44 | Kovan | audio_generated | gemini-tts | $1.12 | $0.85 | -24.1% |
| 14:31:12 | Parallax | research_completed | claude-sonnet-4 | $0.18 | $0.30 | 40.0% |
| 14:30:58 | Meridian | script_generated | claude-opus-4 | $0.42 | $0.55 | 23.6% |
| 14:30:31 | Parallax | cover_image_generated | nano-banana | $0.87 | $1.20 | 27.5% |
Available balance
$73.41
+$50.00 todayIncluded credits
$100.00 / $100.00 used
Usage — last 12 days
Recent transactions
A billing portal your customers can self-serve
Your customers expect the same billing experience they get from OpenAI or Anthropic. A branded portal where they see their balance, track usage, and top up without filing a support ticket.
Credyt ships this as a drop-in component. No frontend engineering required.
Every pricing model.
Changeable without a deployment.
Configurable via portal, API or your favourite AI tool.
Switch models as you learn what works. No code changes required.
73% of AI companies are still experimenting with pricing models (PricingSaaS, Q1 2026). When your margins change because you switched from GPT-4o to Claude 3.5 Sonnet, your pricing needs to adapt without an engineering sprint.
The cost of building it yourself (even with AI coding tools)
6 months and ~$78K before a line of product code ships
At $150K/year fully loaded for a single engineer.
AI coding tools compress the implementation time on individual components, but they don't eliminate the design work (understanding how billing integrates with your existing customer model), the migration effort (moving existing customers onto the new system while keeping your current billing running), or the ongoing maintenance load. The weeks in this table already account for AI-assisted development. Without it, double them.
Built by people who have done this before.
The Credyt team built billing and payments infrastructure at Checkout.com, SumUp, Visa, and TrueLayer. We've seen what breaks when billing meets real-time variable costs. We built Credyt so you don't have to learn the same lessons.
AI costs fire in real time. Your billing should too.
Free to start. Live in days.