Usage-based and tokenized billing are pricing models where customers pay for what they consume. Unlike traditional flat-rate subscriptions, this approach directly links the cost of a service to its usage, such as the number of API calls made, data processed, or tokens generated by an AI model.

AI companies, in particular, favor these models because their own operational costs are often tied to resource consumption. For example, an AI service’s expenses fluctuate based on the computational power required for processing user requests. Aligning revenue with these variable costs is not just a pricing strategy—it’s a crucial component of a sustainable business model. This guide explores how these models work, their benefits, and how to approach implementation.

At their core, usage-based and tokenized billing require a system that can accurately track consumption, associate it with a specific user, and then calculate the cost. This process typically involves a few key components.

Here’s a simplified breakdown:

• Metering: The first step is to measure usage. For an AI product, this could be tracking the number of words generated, images created, or API requests handled. This data needs to be collected reliably and in real-time.
• Rating: Once usage is metered, a price is applied. This is where the “rating” engine comes in. It could be a simple per-unit cost (e.g., $0.002 per token) or a more complex tiered structure where the price per unit decreases as volume increases.
• Billing: The rated usage data is aggregated over a billing cycle (e.g., monthly). An invoice is then generated, detailing the consumption and the total amount due.
• Payment: Finally, the customer pays the invoice. This step requires a robust payment processing system that can handle recurring, variable payments securely.

Tokenized or credit-based systems add a layer of abstraction to this process. Instead of billing for direct usage, customers purchase a bundle of credits or tokens upfront. They then consume these credits as they use the service. This model improves predictability for the customer and cash flow for the business.

The flexibility of usage-based models makes them suitable for a wide range of AI products. Below are some common applications that highlight their versatility.

• Large Language Models (LLMs): Companies providing access to LLMs, like OpenAI, often charge based on the number of input and output tokens. This directly correlates the cost with the computational workload.
• AI-powered Content Creation: Services that generate text, images, or code can bill per item created or per word/character generated. This allows casual users to pay very little while heavy users contribute proportionally more.
• Data Processing and Analytics: AI platforms that analyze large datasets can charge based on the volume of data processed (e.g., gigabytes) or the number of records analyzed.
• API as a Service: For developers building on top of an AI API, a pay-per-call model is intuitive and easy to understand. It scales with their application’s growth.

While powerful, implementing usage-based billing is not without its challenges. One common misconception is that it’s simply a matter of counting events and multiplying by a price. The reality is more complex.

Here are some of the key challenges:

• Complexity in tracking: Accurately metering usage across a distributed system can be technically challenging. Events can be missed, duplicated, or delayed, leading to billing inaccuracies.
• “Bill shock” for customers: Customers can be surprised by unexpectedly high bills if they are not able to easily monitor their consumption. This can lead to churn and support overhead.
• Revenue predictability: For the business, forecasting revenue can be more difficult than with fixed subscriptions, as it depends on fluctuating customer usage.
• Infrastructure costs: Building and maintaining a reliable and scalable metering and billing system requires significant engineering investment.

A well-designed system mitigates these challenges with transparent usage dashboards for customers and sophisticated forecasting models for the business.

A successful usage-based billing system prioritizes fairness, transparency, and a great customer experience. Thoughtful planning around how you communicate pricing and consumption is as important as the technical implementation itself.

Consider these best practices:

• Provide real-time usage monitoring: Give customers a clear and accessible dashboard where they can see their consumption in near real-time. This helps prevent “bill shock” and builds trust.
• Set spending limits and alerts: Allow users to set budgets or receive notifications when their usage reaches certain thresholds. This empowers them to control their costs and feel more secure.
• Offer a free tier or trial credits: Let potential customers experiment with your service without financial commitment. This is a powerful way to demonstrate value and drive adoption.
• Consider a hybrid model: You can combine a base subscription fee with usage-based charges. This provides a predictable revenue floor while still capturing value from high-usage customers.
• Keep pricing simple: While it’s tempting to create complex, multi-dimensional pricing, simple and predictable models are easier for customers to understand and trust.

Implementing a usage-based billing system from scratch is a significant undertaking. Kinde’s billing infrastructure is designed to help you launch and scale sophisticated pricing models without the heavy engineering lift.

With Kinde, you can define plans that include both fixed subscription fees and metered, usage-based components. This allows you to create flexible pricing that aligns with your business model, whether it’s pure pay-as-you-go or a hybrid approach.

Kinde’s APIs and webhooks are central to this process. You can use the API to report customer usage for any metered feature you define. For example, every time a user generates an image or processes a document, your application can send that event to Kinde. Kinde then handles the aggregation and invoicing based on the pricing model you’ve configured.

Furthermore, webhooks can notify your application of billing events like customer.meter_usage_updated or customer.payment_failed. This allows you to build automated workflows, such as sending usage alerts to customers or gracefully handling payment issues.

By integrating with Stripe for payment processing, Kinde provides a complete solution that handles the entire billing lifecycle, from plan creation to revenue collection. This lets you focus on building your core AI product instead of reinventing the billing wheel.

• Pricing models
• Add metered usage for a customer via API
• Respond to customer activity using webhooks
• About plans