Attribution-based billing is a model that breaks down the cost of a complex workflow and assigns it to the specific components or activities that create value. In the context of AI, instead of charging a single flat fee or per API call, you bill based on the individual actions within a larger process, such as an agent’s task, a data retrieval step, or a final analysis.

Modern AI applications are rarely a single call to one model. They are often multi-agent or hybrid pipelines where several components work together:

• An initial agent might interpret a user’s request.
• A second agent might be dispatched to retrieve information (Retrieval-Augmented Generation or RAG).
• A third component might perform data analysis or use a specific tool.
• A final agent synthesizes the results.

This model moves beyond billing for raw resource use (like API tokens) and toward billing for logical business outcomes, giving customers a clearer picture of what they’re paying for.

Implementing an attribution-based system involves treating each step in an AI pipeline as a potentially billable event. This requires instrumenting your application to track and report usage accurately.

The process typically follows these four steps:

1. Identify billable events: First, map out your AI workflow and define the distinct, value-producing actions. These are your billable events. Examples include an agent invocation, a document lookup in a vector database, the use of a specialized tool, or the generation of a report.
2. Instrument the pipeline: Next, add metering logic to your application to track whenever one of these events occurs. When an agent runs or a file is analyzed, your system should emit an event with relevant metadata, like the customer ID and the number of units consumed.
3. Aggregate usage data: A billing platform collects these individual events over the billing period. It keeps a running tally of how many “retrievals,” “analyses,” and “agent invocations” each customer has used.
4. Apply a pricing model and invoice: At the end of the cycle, the system applies a specific price to each type of event. The final invoice presents a detailed breakdown, showing the customer exactly which activities contributed to their bill. For example, an invoice might list 150 x "Research Agent Task" and 450 x "Data Retrieval Action" as separate line items.

As AI systems become more complex, simple billing models fall short. Attribution-based billing offers several advantages for businesses building and selling AI-powered products.

Here are a few key benefits:

• Transparent pricing: Customers can see a direct correlation between their usage and their bill. This transparency builds trust and helps them understand the value they receive from different features.
• Flexible product tiers: It allows you to create more granular and appealing pricing plans. For instance, a “Pro” plan could include a monthly allowance of 100 “analysis agent” runs, while an “Enterprise” plan offers unlimited runs but charges for a premium “forecasting tool.”
• Better business insights: By tracking the usage of individual components, you gain valuable data on which features are most popular. This can inform your product roadmap and help you optimize resource allocation.
• Future-proof architecture: This model is inherently scalable. As you add new agents, tools, or capabilities to your pipeline, you can define them as new billable items without having to redesign your entire billing system.

While powerful, implementing this billing model comes with its own set of challenges that require careful planning.

• Defining the unit of value: The most difficult task is often deciding what to measure. Should you charge per agent invocation, per token processed, per second of compute time, or per successful outcome? The answer depends on your product and how your customers perceive value.
• Engineering complexity: Building a reliable metering and aggregation system from scratch is a significant engineering effort. It needs to be highly available, scalable, and accurate to avoid billing errors and revenue loss.
• Potential performance overhead: If not implemented carefully, the process of tracking and reporting every billable event can introduce latency into your application. Metering events should be handled asynchronously to avoid impacting the user experience.
• Clear communication: Your pricing page, invoices, and user dashboards must be exceptionally clear. Customers need to easily understand what each billable event means and how it maps to the workflow they used. A list of cryptic event names will only cause confusion and support tickets.

Building the infrastructure for an attribution-based billing system is a complex task involving event tracking, aggregation, invoicing, payment processing, and subscription management. This is where a dedicated platform like Kinde can dramatically simplify the process.

Kinde’s billing engine is designed to support sophisticated, usage-based models out of the box. You can define each step in your AI pipeline—such as an agent invocation, a data lookup, or a tool usage—as a distinct metered feature.

As your application runs, your backend can report consumption against each feature using Kinde’s API. Kinde takes care of the rest:

• Usage aggregation: It automatically tracks and sums up the usage for each customer over their billing cycle.
• Invoicing and payments: It generates clear, itemized invoices and handles payment processing through its integration with Stripe.
• Subscription management: It manages the entire customer lifecycle, including plan upgrades, downgrades, and cancellations.

By using Kinde, you can implement a sophisticated, attribution-based billing model without building the underlying infrastructure from scratch, allowing you to focus on developing your core AI product.

• Pricing models
• The Kinde billing model