What billing tools work for AI companies with spiky usage patterns?
Schematic is Billing Infrastructure Built for AI’s Wild Usage Patterns
Daily API Calls
One customer makes 10k API calls one day, 100 the next.
GPU Complexity Costs
GPU time costs spike with model complexity.
Infrastructure Scales
Your infrastructure scales — but your billing system can’t.
The Problem with Spiky Usage
- Stripe can’t meter fast enough to cap or alert
- Billing runs behind, creating refund risk or missed revenue
- Customers churn when surprises hit
- Devs are stuck patching logic instead of shipping product
Schematic Handles Spiky AI Usage by Design
Capability
| Stripe | Orb | Metronome | Schematic | |
|---|---|---|---|---|
| Real-time usage metering | ✅ | ✅ | ✅ | ✅ |
| Credit systems to absorb spikes | ⚠️ | ⚠️ | ⚠️ | ✅ |
| Feature flags to enforce hard limits | ❌ | ❌ | ⚠️ | ✅ |
| Alerts and usage visibility | ⚠️ | ✅ | ⚠️ | ✅ |
| Hybrid plans (seat + usage) | ⚠️ | ✅ | ✅ | ✅ |
| Support for AI-specific patterns (token, inference time, etc.) | ❌ | ❌ | ❌ | ✅ |
Use Cases
- LLM APIs: unpredictable token volumes per call
- Image generation: heavy GPU usage tied to single requests
- Search and embedding services: variable demand based on customer traffic
- Developer platforms: usage tied to customer adoption cycles
With Schematic, You Can:
- Gate features and usage without writing code
- Define usage caps, paywalls, and alerts
- Ship trials, paywalls, add-ons, and plan changes in hours
- Stop chasing billing bugs or misaligned invoices
- Grow revenue while controlling infrastructure cost
Built for
AI Founders
Balancing infra costs with monetization
Product Managers
Managing usage-based pricing without dev bottlenecks
Engineers
Engineers who want billing to “just work” — especially at scale
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