Billing for services that use AI and Large Language Model (LLM) APIs presents a unique challenge. Unlike traditional SaaS products with predictable, seat-based pricing, AI consumption can be highly variable and difficult to forecast. A user might generate 100 words one day and 10,000 the next, leading to huge swings in cost.

This guide explains how to think about billing for consumption-based AI services. We’ll cover the core mechanics, common challenges, and practical strategies you can implement to provide customers with transparency and control while protecting your business from runaway costs.

At its core, consumption-based billing ties a customer’s cost directly to their usage of a resource. For AI and LLM APIs, this is typically measured in tokens, which are pieces of words used to process and generate language.

Here’s a simplified breakdown of the process:

• Unit of value: The provider (e.g., OpenAI, Anthropic) defines a unit of consumption. For LLMs, this is usually a number of tokens (e.g., 1,000 tokens). For other AI APIs, it could be API calls, seconds of audio processed, or images generated.
• Price per unit: Each unit has an associated cost. Often, the cost for input tokens (what the user sends) is different from the cost for output tokens (what the model generates).
• Metering: Your application must keep a running tally of each customer’s consumption. Every time a user makes an API call, your system records how many tokens were processed.
• Billing cycle: At the end of a billing period (e.g., monthly), the total consumption is multiplied by the price per unit to calculate the final bill.

This model is direct and transparent, but its unpredictability is a significant hurdle for both businesses and their customers.

Controlling costs is crucial in several common AI application scenarios. Each presents a different usage pattern and requires a thoughtful approach to billing.

• SaaS with AI features: A project management tool that adds an AI assistant to summarize tasks or generate reports. Usage might be infrequent but can spike when a team is up against a deadline.
• Content creation tools: A marketing platform that uses LLMs to draft blog posts or social media updates. A single power user could potentially generate enough content to drive up costs significantly.
• Developer platforms: A service that provides API access to fine-tuned models for other developers. Usage is entirely dependent on the success and traffic of the applications your customers build.
• Internal tools: A company-wide chatbot that helps employees query internal knowledge bases. While not billed to an external customer, a lack of control can lead to massive internal costs that are hard to attribute and budget for.

Simply passing on the raw, pay-as-you-go cost to users is often not a viable strategy. It creates uncertainty for your customers and can lead to support issues and churn.

The main challenges include:

• Bill shock: Customers receive a much higher bill than they anticipated, leading to disputes and dissatisfaction.
• Lack of predictability: Businesses struggle to forecast their own revenue, and customers cannot budget effectively for your service.
• The “noisy neighbor” problem: In a multi-tenant system, a single customer’s high usage can strain system resources and, if not billed correctly, eat into the profit margins of your entire customer base.

To address these challenges, you need to build a billing system that offers flexibility and control. This involves implementing a combination of pricing models and user-facing tools.

Here are some of the most effective strategies you can apply, from simple to complex.

• Set clear usage tiers: Package your service into predictable monthly or annual plans (e.g., Starter, Pro, Enterprise) that include a generous allowance of tokens or credits. This is the most common and user-friendly approach. Users who exceed their allowance can be prompted to upgrade.
• Offer pay-as-you-go as an add-on: For users who occasionally exceed their plan limits, offer the ability to purchase additional tokens or credits on a consumption basis. This prevents service disruption while still capturing revenue for excess usage.
• Implement usage alerts: Automatically notify users via email or in-app messages when they have consumed a certain percentage of their allowance (e.g., 75%, 90%, and 100%). This simple step prevents surprises and builds trust.
• Provide spending caps and budgets: Allow customers to set their own monthly spending limits. You can offer two types:
  • Soft caps: Trigger an alert but do not stop the service.
  • Hard caps: Temporarily suspend API access until the next billing cycle or until the user manually increases their limit.
• Use rate limiting: As a technical safeguard, implement rate limiting on your API to prevent runaway scripts or abuse from causing a massive, unintended spike in consumption.
• Give users a dashboard: Create a simple analytics page where customers can see their current usage, view their history, and forecast their future consumption. Visibility is key to helping users feel in control of their spending.

Implementing a robust, usage-based billing system from scratch is a significant engineering effort. Kinde’s billing engine is designed to handle the complexity of modern pricing models, including those needed for AI services.

Kinde provides the tools to implement the cost-control strategies discussed in this guide. You can structure your plans using different pricing models that suit your product and customers.

• Flat-rate and tiered pricing: Kinde allows you to create plans with a fixed monthly fee that includes a specific set of features. This is ideal for establishing predictable subscription tiers.
• Usage-based pricing: For features with variable consumption, you can use Kinde’s metered billing. You can define per-unit pricing (e.g., a price per 1,000 tokens) or volume-based pricing where the cost per unit decreases as usage increases. This gives you the flexibility to charge for overages or build a fully pay-as-you-go model.
• Self-serve plan management: Kinde provides components for customers to upgrade or downgrade their plans themselves. When a user’s consumption starts to exceed their current plan’s limits, they can easily move to a higher tier with a larger allowance, directly from their account settings.

By combining these features, you can design a billing experience that is fair, transparent, and adaptable to the dynamic nature of AI consumption.

• Pricing models
• Plan upgrade and downgrade policies