AI Billing Infrastructure: What's required and how to build it

AI is changing how software companies price and deliver value. As usage-based and hybrid pricing models become the norm, billing infrastructure must keep up with the speed and complexity of modern AI services. According to OpenView, nearly 39% of SaaS companies now price primarily on usage, a sharp increase from a decade ago[1].

For developers and product teams, the challenge is clear: how do you build a billing system that is accurate, flexible, and scalable—without slowing down your roadmap or introducing costly errors?

This guide breaks down the technical and business requirements for profitable AI billing infrastructure, with a focus on practical solutions that help you move fast and monetize confidently.

Why AI Billing Demands a New Approach

AI pricing models are more complex than traditional SaaS. You’re not just charging for seats or monthly access. Instead, you might bill per million tokens, GPU-seconds, API calls, or a mix of these. Each event can have a real cost of goods sold (COGS), and customers expect transparency and fairness in how they’re billed[1] [2].

Key challenges in AI billing:

• Real-time metering of high-frequency events (e.g., token usage, GPU time)
• Hybrid pricing (subscriptions plus usage, minimums, overages)
• Multi-currency invoicing and tax compliance
• Developer control and rapid iteration on pricing

Example: An AI API provider offers a $100/month subscription that includes 1 million tokens, with overage at $0.02 per 1,000 tokens. Customers can add GPU-hour packs as needed. The billing system must meter usage in real time, apply overage rules, and generate accurate invoices across currencies.

Core Components of a Profitable AI Billing System

Real-Time Metering for AI and SaaS

Accurate metering is the foundation of any usage-based billing system. For AI, this means tracking every event—tokens, API calls, GPU-seconds—at millisecond speed[3] [1].

Technical requirements:

• Event-driven architecture for high-throughput ingestion (up to 15,000 events per second)
• Minimal data pre-processing, allowing direct use of raw logs or event streams
• Flexible mapping of events to pricing dimensions

Lago’s platform ingests burst traffic and applies overage rules in real time, supporting AI providers who need to meter usage at scale[3] [1].

How to Implement Real-Time Metering

1. Integrate your data sources (logs, event streams) with the billing platform.
2. Define event schemas (e.g., {user_id, event_type, quantity, timestamp}).
3. Use APIs to push events as they occur.
4. Validate and aggregate usage for billing cycles.

Code snippet:

import requests

event = {
   "user_id": "12345",
   "event_type": "token_usage",
   "quantity": 1000,
   "timestamp": "2025-06-12T15:00:00Z"
}
requests.post("https://api.getlago.com/events", json=event)

Hybrid and Usage-Based Pricing Models

Flexible AI Pricing Strategies

AI services often require a mix of pricing models: pay-as-you-go, subscriptions, prepaid credits, and add-ons[1] [2].

Common AI pricing models:

• Pay-per-use (e.g., per token, per API call)
• Subscription floors with usage overages
• Prepaid credits (e.g., buy 10,000 GPU-seconds upfront)
• Tiered pricing with volume discounts

Hybrid pricing example: A customer pays a $50/month base fee, gets 500,000 tokens included, and pays $0.01 per 1,000 tokens above that. They can also purchase add-on credits for GPU time.

Why Hybrid Pricing Matters

• Aligns revenue with actual usage and COGS
• Supports both enterprise and self-serve customers
• Enables rapid pricing experiments without migrations

Automated Invoicing, Tax, and Collections

Handling Global Billing Complexity

AI and SaaS companies often serve customers worldwide. This means dealing with multiple currencies, tax regimes, and compliance requirements.

Key features for global billing:

• Multi-currency invoicing (USD, EUR, GBP, etc.)
• Automated tax logic (EU VAT, US sales tax)
• Dunning, credit notes, and refunds
• Embedded customer portal for self-service

Lago automates invoice generation, applies tax rules, and provides a customer portal so users can view usage and download invoices[3] [1].

Example: Multi-Entity Invoicing

A company with customers in the US and EU needs to invoice in both USD and EUR, apply the correct VAT or sales tax, and handle FX rounding. The billing system must support these requirements out of the box.

Developer Experience and API-First Architecture

Why Developer Control Matters

Billing is a critical system that touches product, finance, and customer experience. Developers need tools that are composable, API-driven, and easy to integrate with existing stacks[2] [4].

Developer-friendly features:

• Open API for all billing operations
• JSON-based configuration for pricing plans
• Real-time analytics via dashboard and API
• No vendor lock-in; keep your payment processor and data warehouse

Lago is designed for engineering teams who want to move fast, avoid feature bloat, and maintain control over their billing logic[3] [2] [4].

For teams managing complex event ingestion and metering pipelines, using continuous deployment platforms ensures scalable, reliable environments. It enables fast rollouts of pricing logic changes, supports isolated staging for usage-based features, and integrates with your existing observability and CI/CD workflows.

Fast Implementation and Iteration

• Go live in weeks, not quarters
• Test pricing changes without migrations
• Integrate with your own payment stack (Stripe, Adyen, etc.)

Security, Compliance, and Open Source Flexibility

Enterprise-Grade Cloud and Optional Self-Hosting

Security and compliance are non-negotiable for billing infrastructure. Lago Cloud is SOC 2 Type 2 certified, GDPR-aligned, and offers a 99.9% SLA[3]. For companies needing full data control, a self-hosted open-source edition is available, but the primary focus is on cloud reliability and compliance[5] [4].

Security highlights:

• SOC 2 Type 2 certification
• GDPR-aligned privacy program
• 99.9% uptime SLA

Key Takeaways for Developers Building AI Billing

• Real-time, event-driven metering is essential for accurate AI billing.
• Hybrid pricing models unlock revenue and flexibility for both you and your customers.
• Automated invoicing, tax, and collections reduce manual work and errors.
• Developer-first platforms like Lago let you move fast, iterate on pricing, and maintain control.
• Cloud compliance and optional open source give you the flexibility to meet enterprise requirements.

The shift to usage-based and hybrid pricing is accelerating. With the right billing infrastructure, you can monetize AI services confidently, reduce errors, and support global growth. To see how Lago can help you build a profitable, developer-friendly billing system, explore the platform’s documentation and start testing your pricing models today[3] [1] [2] [4].

FAQ

Q1. Does usage billing kill revenue predictability?
A subscription floor keeps ARR predictable while usage unlocks upside. Benchmark: >70 % of revenue still comes from the base plan in early stages.

Q2. Can I migrate without double‑billing users?
Yes. Import historical usage and mark it as ‘billed’ so new invoices start clean.

Q3. What about PCI or HIPAA?
Billing data is non‑cardholder; keep payment processing with Stripe/Adyen for PCI scope. Lago stores no PHI, but you can self‑host if policy demands it.

Key Takeaways

• Real‑time metering is non‑negotiable for AI workloads.
• Hybrid pricing aligns revenue with COGS and lets you experiment fast.
• Automated global invoicing removes manual work and tax risk.
• Developer‑first platforms like Lago cut go‑live from quarters to weeks.
• Cloud or open‑source—choose the compliance model that fits each customer.

Citations

[1] https://getlago.io/blog/features-ai-billing-infrastructure

[2] https://hiretop.com/blog2/lago-open-source-billing-platform/

[3] https://www.getlago.com/products

[4] https://www.promptloop.com/directory/what-does-lago-do

[5] https://docs.litellm.ai/docs/observability/lago