How to Price Your AI-powered SaaS: Usage Calculations and Pricing Strategies

When pricing your AI tools, you should:

• Understand your costs.
• Be aware of your product’s perceived value.
• Know the competitive landscape you are working in.

To be able to do this, break down the process into practical steps:

Think about how your customers are using your product and how you would measure that. In other words, the electric meter in your house tracks the power you use, then you are billed accordingly.

With SaaS, it could be examples such as:

→ If your service is driven by API calls (e.g., natural language processing, image generation), each API call represents a unit of consumption. Count those interactions.

→ If your service involves data storage or processing (e.g., video analysis, large-scale machine learning model training), then bandwidth or storage usage is a relevant metric. Charges could be based on how much space is used.

→ If your AI product has features with varying value propositions (e.g., basic vs. advanced analytics), track the usage of individual features. 

→ For AI services processing text or code (e.g., translation, code generation), the number of characters or tokens processed are an indicator of usage.

→ If your service demands computational resources (e.g., complex simulations, deep learning models), tracking CPU/GPU usage time is a suitable metric.

Your goal is to choose a metric that reflects the value your users receive from your product. Think about:

• List core value propositions and brainstorm the benefits the AI tool offers to your users. Does it save time, improve precision, produce feedback, or something else?
• Map value to measurable units by identifying the comparable unit and determining value.

• Consider technical constraints by assessing the technical pieces of your AI service. Does it hinge on API calls, storage, specific features, or computational power?
• Choose the most relevant metric(s) based on your value propositions and technical constraints. Choose the unit(s) of measurement that reflect the customer’s perceived value. You may have more than one metric if they represent different parts of your product’s value.

Figuring out how much to charge is where things get more complex. This entails balancing your costs, the perceived value of your product, and the competitive landscape. 

Consider these points: 

• Cost Analysis: Identify the cost for you to provide the service.  Take into account the servers, development and support. Add your profit margin goal and you’ll have your ideal selling price.
• Value Metric: What is the main benefit of your AI tool? Is it a time saver; does it improve accuracy or does it provide great insights?Time saved? Improved accuracy?  Put a price tag on that value.
• Competitor Research: Investigate what your rivals are charging to get a sense of the market. This helps you position your product accurately. However, strategies of undercutting competitors can lead to a price war and devalue your product.
• Tiered Pricing: Create pricing groups with varying feature sets or usage limits. This gives options that increase your revenue by making a clear distinction between tiers.

Since your pricing structure influences how users perceive your product’s value and therefore their willingness to pay, selecting the right model is a big decision. Each one comes with its own set of benefits and drawbacks, so choose one that aligns with your product, and is right for your target market and business goals. We want to make sure you get this right – so follow these suggestions:

• Survey your target market with questionnaires or interviews to collect their feedback. Understand what your customers’ preferred pricing model is and what they think is a fair price for features and functionality. Are they interested in usage based billing, one time payments or subscription plans?
• Evaluate product-market fit by assessing how your product aligns with the needs of your target market. Depending on their urgency for the solution, you may have pricing options.
• Forecast revenue with financial modeling to make your projections. With various pricing models, consider factors such as customer acquisition, churn, and upsell potential.
• Select the best fit based on analysis by choosing the pricing model that balances customer preferences, product complexity, revenue predictability, and market dynamics. Here are some advantages and disadvantages of some typical pricing models for AI tools: 

Take a deeper look into SaaS pricing strategies with our additional detailed resources: 

Blog: Extensive Guide on SaaS Pricing Models and Strategies 

Video: 6 SaaS Pricing Models and Strategies | YouTube

Even after you launch your AI SaaS and set your prices, the work’s not over. You need to keep an eye on how people are using it, gather data, and be prepared to tweak your pricing if needed:

• Collect data by tracking everything. Note how often your product is being used, the popular features and if users are churning.  
• Ask for customer feedback to understand what they think. Are they satisfied with the pricing? Is it clear and easy to understand?
• Run A/B Testing to experiment with pricing options to see what is working. 
• Monitor the market by keeping an eye on competition and trends. You’ll need to adapt in the changing market.

Pricing is an ongoing process, not a one-time decision. By testing, iterating, and adapting your pricing strategy within an ongoing process, you’ll know your AI-powered SaaS is competitive, profitable, and providing value to your customers.

A sample breakdown of AI tools, their pricing models, and some key points in lessons learned: