The game has changed. For years, AI pricing was synonymous with pay-per-use inference—charging fractions of a cent per thousand tokens. But as businesses move from using generic models to building differentiated products on custom AI, that model is breaking down. The real value is no longer just in the API call; it’s in the customization, the specialized knowledge, and the dedicated performance.

This guide explains how to build a modern pricing strategy that captures the true value of your AI services. We’ll cover how to charge for fine-tuning, dedicated model hosting, Retrieval-Augmented Generation (RAG), and the infrastructure required to scale it all. This is for founders, product leaders, and engineers who are building the next wave of SaaS-scale AI products.

Custom AI pricing models are strategies that go beyond simple per-token billing to charge for the entire value chain of delivering a specialized AI model. This means assigning a price to the processes and infrastructure that make the model unique to a customer, reflecting the real costs and value delivered.

This approach requires you to separately consider and price several core components:

• Fine-tuning: The computational work required to train a base model on a customer’s specific data.
• Model hosting: The cost of keeping a unique, fine-tuned model running on dedicated or high-priority infrastructure.
• Data services (for RAG): The cost of ingesting, vectorizing, and storing the proprietary data used to give a model its specialized knowledge.
• Specialized inference: The usage costs associated with running queries against the custom model, which often carries a higher price than a generic model.

To build a robust pricing strategy, you need to understand the distinct cost drivers and value propositions of each component. Think of these as a menu of options you can bundle into tiered plans.

This is the process of creating a new, specialized model. You take a powerful base model (like Llama 3 or an open-weights vision model) and train it further on a customer-specific dataset.

• Cost Drivers: GPU time is the primary cost. A fine-tuning job can take anywhere from minutes to many hours on expensive hardware. Other costs include data validation, storage, and the engineering effort to manage the process.
• How to Price It:
  • One-Time Setup Fee: A flat fee for an initial fine-tuning job. This is simple and works well as part of an onboarding package.
  • Per-Job Pricing: Charge for each new fine-tuning run. This encourages customers to continuously improve their models and provides you with recurring, usage-based revenue.
  • GPU Hour Credits: Sell packs of GPU hours that customers can consume for training, giving them more control and predictability.

A fine-tuned model isn’t useful unless it’s running and available to serve requests. Unlike generic models that run on shared, multi-tenant infrastructure, a custom model often requires its own dedicated endpoint.

• Cost Drivers: Reserved GPU/CPU instances, memory, and the uptime guarantees (SLAs) you provide. A long-running endpoint provisioned for a single customer is a significant, ongoing operational expense.
• How to Price It:
  • Tiered Monthly Fee: Offer different hosting tiers based on performance (e.g., requests per second, model size, GPU type). For example: “Test,” “Production-Small,” and “Production-Large” endpoints.
  • Environment-Based Pricing: Charge a different rate for a staging/testing endpoint versus a production one. A production endpoint comes with higher availability and performance, justifying a higher price.

RAG customizes a model’s output without retraining the model itself. It works by retrieving relevant information from a customer-specific knowledge base and feeding it to the LLM as context with the prompt.

• Cost Drivers: The main costs are related to the data pipeline and vector database. This includes the computational cost of embedding the data (turning it into vectors), the storage costs of the vector database, and the cost of performing retrieval queries.
• How to Price It:
  • Per-GB Stored: A simple, predictable model based on the size of the customer’s knowledge base.
  • Per-Document Processed: Charge for the initial ingestion and embedding of each document.
  • Bundled Platform Fee: Include RAG capabilities as part of a higher-tier subscription plan that also includes fine-tuning and hosting.

Moving to a custom pricing model is a strategic decision that aligns your revenue with the value you create, leading to a more sustainable and scalable business.

• Aligns Price with Value: Customers who need a fine-tuned model are solving a high-value problem. Your pricing should reflect the uniqueness and performance of the solution, not just the raw cost of tokens.
• Creates Predictable Revenue: Pure usage-based pricing is volatile. Monthly or annual fees for model hosting and platform access create a stable, predictable revenue stream (MRR/ARR).
• Improves Margins: You can accurately price for the high-cost components of your service, like dedicated GPUs and engineering support, ensuring healthy margins.
• Increases Customer Stickiness: A fine-tuned model integrated into a customer’s workflow is a deeply embedded asset. It’s much harder to switch from a custom-trained solution than from a generic API.

While powerful, this approach introduces complexity that you must manage carefully.

• Cost Attribution: Accurately calculating the cost of a single training job or the hourly cost of a specific endpoint can be complex, especially in a multi-tenant cloud environment.
• Communicating Value: Customers are accustomed to per-token pricing. You must educate them on why a custom solution that costs hundreds or thousands of dollars per month is more valuable than a generic model that costs pennies.
• Billing and Metering Complexity: Building the infrastructure to track GPU hours, data storage, and active endpoints, and then invoice for them correctly, is a significant engineering challenge. A flexible billing system is essential.

Adopting a custom AI pricing model requires thoughtful planning and clear communication.

• Start with Tiers: The easiest way to start is by bundling your services into clear tiers. For example, a “Pro” plan might include one fine-tuning job per month, hosting for one production model, and RAG for up to 50GB of data.
• Use a Hybrid Model: The most effective strategy often combines a recurring flat fee with a usage-based component. Charge a monthly subscription for platform access and model hosting, and then add usage-based charges for inference overages or additional training runs.
• Separate Test and Production: Offer a low-cost “developer” or “testing” plan. This lets customers experiment with fine-tuning and integration with a smaller, less powerful endpoint before committing to a costly production deployment.
• Be Transparent: Clearly break down what a customer is paying for on your pricing page. Show the value of each component—the training, the hosting, the support—to justify the price.

Implementing a sophisticated pricing strategy for AI services requires a billing system that can handle more than just simple subscriptions. While Kinde isn’t designed exclusively for AI pricing, its flexible billing engine provides the core components needed to build these models.

Kinde’s architecture allows you to combine different pricing structures to create hybrid models. For instance, you can use Kinde to:

• Create Tiered Subscription Plans: Set up flat-rate monthly or annual plans that correspond to your service tiers (e.g., Test, Production-Small, Production-Large). This can cover the base cost of model hosting and platform access.
• Incorporate Metered Usage: For billable components like fine-tuning jobs, GPU hours, or token usage, you can add metered charges on top of a subscription. Kinde’s API allows you to report usage events, which are then billed to the customer on their next cycle.
• Define Custom Features: You can define specific features within each plan, giving you granular control over what each customer tier has access to.

This allows you to construct a pricing model that includes a stable, recurring fee for hosting while also billing for the variable costs of training and inference, all within a single, manageable system.

• Pricing models
• Create plans
• Add metered usage