How to price complex AI workflows that go beyond single API calls to deliver powerful, multi-step results.

Modern AI applications are rapidly moving beyond simple, one-shot queries. Instead of just answering a single question, sophisticated systems now execute multi-step “agentic” workflows. These AI agents can retrieve information, use external tools, and chain together multiple models to solve complex problems. While incredibly powerful, they introduce a new challenge: how do you fairly and sustainably charge for a process rather than a single event?

This guide explains how to think about pricing for these hybrid AI chains. We’ll cover the mechanics, common challenges, and best practices for creating a billing model that aligns with the value you deliver.

An agentic AI workflow is a sequence of actions orchestrated by an AI to achieve a goal. Unlike a standard API call that performs one task (like generating text), an agentic workflow might involve several distinct steps.

Think of the difference between asking a calculator to solve “5 x 10” versus asking a research assistant to “find out the market size for electric bikes in Europe and summarize the key trends.” The calculator performs a single, predictable operation. The research assistant, however, must:

1. Interpret the request: Understand the user’s intent.
2. Plan the work: Decide which sources to check (e.g., market research databases, news articles).
3. Execute tasks (use tools): Perform web searches, query a database, or access a specific API.
4. Synthesize findings: Analyze the gathered information, identify key trends, and filter out noise.
5. Generate a response: Formulate a concise, human-readable summary.

This entire sequence is the “agentic workflow.” Each step consumes resources—LLM tokens, tool API calls, computation time—that need to be accounted for.

Pricing an agentic workflow means moving from billing for a single event (like one API call) to billing for an entire orchestrated sequence. The core idea is to capture the total cost and value of the entire chain of actions.

A typical workflow might look like this:

• Initial Prompt: The user makes a complex request.
• Orchestration & Planning: An initial LLM call interprets the prompt and creates a plan (e.g., “Step 1: Search the internal knowledge base. Step 2: If nothing is found, search the web. Step 3: Summarize findings.”).
• Tool Use & Retrieval: The agent executes the plan, making one or more calls to external tools, databases (retrieval-augmented generation, or RAG), or other services.
• Processing & Generation: Another LLM call synthesizes the information from the tools and generates the final answer.

Instead of charging for just one of these steps, you need a model that can accommodate the variable costs of the entire process.

This model is essential for any application where the AI does more than just generate text from a prompt.

Charging for multi-step workflows introduces complexity that single-event pricing doesn’t have.

• Cost unpredictability: A simple user query might be resolved in two steps, while a complex one could take ten. This makes it difficult to predict the exact cost of any given job, which can be risky for both your business (runaway costs) and your customer (surprise bills).
• Attributing value: The final LLM-generated text is only one piece of the puzzle. The real value often comes from the retrieval and tool-use steps. A pricing model based only on token count fails to capture the value of accessing proprietary data or executing a powerful tool.
• Preventing runaway workflows: An improperly configured agent could get stuck in a loop, repeatedly calling an expensive tool. You need “circuit breakers” or other cost-control mechanisms to prevent a single query from generating a massive bill.

To succeed, your pricing model needs to be clear, predictable, and aligned with the value your users receive.

Start by offering plans that combine a recurring subscription with a usage-based component. This hybrid approach provides predictable revenue for you and flexible, fair pricing for your customers.

Here are some best practices:

• Create value-based tiers: Structure your pricing around outcomes, not raw metrics. Instead of selling “1 million tokens,” sell “100 advanced reports” or “500 support incidents resolved.” This ties the price directly to the value your product creates.
• Combine subscriptions with metered usage:
  • Flat-rate subscription: The base of your plan. This grants access to the platform and might include a certain number of agentic “tasks” per month.
  • Metered features: For usage beyond the base subscription, charge for specific, high-value events. You could charge per workflow execution, per data source connected, or per report generated.
• Set clear limits and communicate them: Be transparent about the limits of each plan. Implement hard caps or “pay-as-you-go” overages to ensure users are always in control of their spending.
• Use tiered pricing for metered features: Incentivize higher usage by making it cheaper at scale. For example, the first 100 reports might cost $1 each, but the next 500 could cost $0.75 each.

Implementing a sophisticated billing model for agentic AI requires a flexible and powerful billing engine. Kinde is designed to handle exactly these kinds of complex, hybrid pricing structures.

With Kinde, you can easily build and manage a billing system that combines recurring subscriptions with granular, usage-based components. This allows you to price your AI services based on the true value they deliver.

Here’s how you could model it with Kinde:

1. Create your plans: Set up different subscription tiers (e.g., Free, Pro, Enterprise) with a flat-rate monthly or annual fee.
2. Define metered features: Add specific, chargeable features that represent your agentic workflows. You can define these with different pricing models, such as per-unit or tiered pricing, to match the best practices above.
3. Report usage via API: As your AI agents complete their workflows, you use a simple API call to inform Kinde of the usage. For example, after a report is successfully generated, you would make an API call to increment the “reports generated” metric for that customer.

Kinde’s billing system handles all the complexity of invoicing, proration, and subscription management, allowing you to focus on building your AI application.

To learn more, see how you can define different pricing models and report metered usage in the Kinde documentation.

• Pricing models
• Add metered usage for a customer via API