The world of AI is no longer just about text. Modern applications now seamlessly blend text, images, audio, and video, creating powerful multi-modal experiences. But this innovation brings a new challenge for SaaS businesses: how do you price for services when the underlying computational costs vary so dramatically between different types of content?

Billing for a simple text analysis is vastly different from processing a high-resolution video. This guide explains the concepts behind billing for multi-modal AI, explores common models, and offers practical advice for building a pricing strategy that’s fair, scalable, and easy for your customers to understand.

Multi-modal AI billing is a pricing strategy designed for applications that handle multiple types of data, such as text, images, audio, and video. It accounts for the different computational resources required to process each data type, ensuring that the price a customer pays aligns with the actual cost of the service they consume.

For example, generating a short paragraph of text is computationally cheap compared to transcribing an hour of audio or analyzing a 10-minute video. A simple, flat-rate subscription might work for a text-only service, but it quickly breaks down in a multi-modal world. You risk either overcharging users who only use low-cost modalities or undercharging power users and losing money.

Effective multi-modal billing addresses this by tying price to the specific “modality” and intensity of usage.

Most multi-modal billing systems are built on consumption-based or hybrid models. This approach ensures fairness and scalability by linking price to usage. Key models include usage-based pricing, tiered pricing, and hybrid models which are detailed below.

• Usage-based (metered) pricing: Customers are charged directly for what they use. This is the most precise model but requires clear and distinct units of value for each modality.
• Tiered pricing: Customers choose from several subscription plans, each with predefined limits for different modalities. This model offers predictability for both the customer and the business.
• Hybrid pricing: This model combines a recurring subscription fee with metered billing for any usage that exceeds the plan’s allowances (overages). It provides a predictable baseline revenue stream while capturing value from high-usage customers.

Here’s a breakdown of how you might define billing units for different AI modalities:

Multi-modal billing isn’t just a theoretical concept; it’s being used today across a wide range of AI-powered SaaS products.

• Content creation platforms: A generative AI tool might offer a basic plan that includes 100 image generations and 10,000 words of text per month. Creating video clips, a more expensive process, could be reserved for a higher-tier plan or billed on a pay-as-you-go basis.
• Digital asset management (DAM): A DAM system could charge for storing files (a simple GB/month metric) but add metered charges for AI-powered features like auto-tagging images, transcribing video content for search, or generating content summaries.
• Content moderation services: An online community platform could use an AI service to moderate user-generated content. The service might charge different rates for scanning text comments, analyzing user-uploaded images for inappropriate content, and reviewing videos, reflecting the escalating computational cost.

Implementing a robust billing system for multi-modal AI is complex. The primary challenge is the unpredictability of both customer usage and backend costs. A sudden spike in video processing, for example, could lead to a massive cloud infrastructure bill that wasn’t anticipated.

Other common challenges include:

• Cost forecasting: Estimating the infrastructure costs associated with varied and unpredictable user consumption patterns is difficult. This makes it challenging to set prices that ensure profitability.
• Communicating value clearly: A multi-faceted pricing model can be confusing. Customers need to understand what they are paying for and how their usage translates to their monthly bill. A complex pricing page can deter potential sign-ups.
• Technical implementation: Building a reliable metering and billing system from scratch is a significant engineering effort. It requires accurate tracking of usage across different modalities, secure payment processing, and a customer-facing portal for managing subscriptions.

A common misconception is that you need a perfect, all-encompassing pricing model from day one. In reality, it’s often better to start with a simpler model and evolve it as you gather more data on customer behavior and operational costs.

Building a fair and effective multi-modal billing system requires a thoughtful approach. Here are some best practices to guide you.

• Anchor pricing to a clear unit of value: Your customers should be able to easily connect their usage to their bill. “Per minute of video transcribed” is much clearer than an abstract “compute unit.”
• Implement rate limiting and budget alerts: Protect both your customers and your business from unexpected costs. Set sensible default limits on usage and allow customers to create budget alerts that notify them when their spending is approaching a certain threshold.
• Start simple and iterate: You don’t need to build the world’s most complex billing system at launch. Begin with a straightforward model, perhaps a few simple tiers. As you learn more about your customers’ usage patterns, you can introduce more sophisticated options like pay-as-you-go or hybrid models.
• Monitor everything: Keep a close eye on usage data, infrastructure costs, and customer feedback. This data is invaluable for refining your pricing, identifying your most and least profitable features, and ensuring your business model remains sustainable as you scale.

Building a custom billing system to handle the complexities of multi-modal AI is a major undertaking. It involves not just payment processing but also metering, subscription management, and plan logic. Kinde’s billing platform is designed to simplify this process.

With Kinde, you can easily create and manage flexible subscription plans that mix recurring fees with metered, usage-based billing. This allows you to construct the exact tiered or hybrid models needed for your multi-modal AI application.

You can define different features—like “image processing” or “video transcription”—and attach distinct pricing models to each. Kinde allows you to set up metered billing based on usage, which you can report via the API. This lets you focus on building your core AI product instead of reinventing the billing infrastructure.

For more information, see how to set up pricing models and report usage in the Kinde documentation.

• The Kinde billing model
• Pricing models
• Add metered usage for a customer via API