ai saas usage credit architect
Architect usage-based billing, credit systems, and quota logic for AI SaaS products.
$5
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Claude Code
Cursor
Codex CLI
Muse
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Grok
Grok Bot+18 agentsai saas usage credit architect
Example session with this skill installed
Design a credit-based billing system for a video generation AI tool. It uses different models for script generation, image synthesis, and final video rendering with varying costs.
- Read your context and instructions
- Compiled the ai saas usage
This architecture defines the metering and quota logic for a multi-stage video generation pipeline, ensuring cost attribution across different model providers while maintaining a simple user credit balance.
Usage & Credit Architecture
1. Metering Model
| Action | Provider Cost Driver | Credit Cost | Logic |
|---|---|---|---|
| Script Draft | LLM Input/Output Tokens | 1 Credit | Fixed per successful generation |
| Image Asset | Diffusion Step/Resolution | 5 Credits | Per image generated |
| Video Render | GPU Seconds / FPS | 25 Credits | Charged post-success |
2. Usage Event Schema
{
"event_id": "evt_550e8400",
"account_id": "acc_98765",
"workspace_id": "ws_43210",
"action_type": "video_render",
"status": "pending | success | failed",
"provider_metadata": {
"model": "gen-3-alpha",
"duration_sec": 10
},
"credits_reserved": 25,
"credits_charged": 0,
"idempotency_key": "user_req_abc123"
}
3. Quota & Failure Logic
- Pre-flight Check: Verify
account.balance >= credits_reservedbefore invoking external APIs. - Idempotency: Use
idempotency_keyto prevent double-charging on network retries or client-side refreshes.
Refund Policy: If status == failed due to provider error, release credits_reserved back to balance. No charge for system timeouts.
- Team Pools: Credits are drawn from a shared
workspace_idbucket with optional per-user monthly caps.
4. Abuse & Rate Limits
- Burst Protection: Limit to 2 concurrent video renders per Pro seat to prevent GPU cost spikes.
- Threshold Alerts: Webhook trigger at 20% and 5% remaining balance to prompt top-up.
Next steps
- Implement the
usage_eventstable in your primary database with the structured JSON schema. - Integrate the pre-flight credit check into your API middleware.
- Configure Stripe Metered Billing to sync the
credits_chargedtotal at the end of each period.
Connects securely to your tools. The creator never sees your data.
About this skill
AI products often fail at the billing layer because they lack a robust strategy for mapping variable API costs to customer-facing plan limits. This skill architected the technical and logic layers required to turn raw model usage into a profitable, enforceable credit and quota system.
What it does
- Metering architecture defines how to track chargeable actions and cost dimensions across various providers.
- Credit normalization designs stable units of account that translate complex token costs into simple user-facing balances.
- Event schema design structures immutable usage logs to ensure auditable billing trails and prevent double-charging.
- Quota enforcement builds the logic for pre-flight checks, team sharing, and real-time usage monitoring.
- Error handling specifies rules for refunds on failed generations, retries, and overage management.
How it works
- Cost mapping identifies every chargeable action and its underlying provider cost drivers.
- Logic design defines how credits are consumed, shared across teams, and reset at billing boundaries.
- State management outlines the requirements for immutable event trails and account-level quota states.
- Resilience planning addresses edge cases like retries, abuse prevention, and reconciliation.
Frameworks & tools
This skill is framework-agnostic but follows best practices for distributed systems, event-driven architectures, and Stripe-style usage-based billing models.
Why this beats prompting it yourself
Generic prompts often overlook the complexities of idempotency, race conditions in quota checks, and the need for auditable event trails. This skill enforces a professional-grade architecture that prevents revenue leakage and customer disputes.
Use cases
- Designing a multi-tenant credit system for a SaaS using multiple LLM providers.
- Drafting a usage event schema that supports both prepaid credits and postpaid overages.
- Mapping complex token-based pricing to simple "seats" and "tasks" for a non-technical audience.
- Creating refund and retry logic for agents that occasionally fail mid-process.
Known limitations
Does not provide implementation code for specific SDKs or perform actual database migrations. Focuses on architectural logic and schema design.
Requires defined product actions and cost drivers to be effective. Focuses on architectural design rather than writing language-specific driver code.
How to use
- 1
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- 2
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- 3
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