7 AI pricing models and which to use for profitable growth

Alvaro Morales

Pricing your AI solution is a high-stakes tightrope walk: Keeping track of API calls, figuring out how much tokens cost, and monitoring usage — one wrong step can lead to lost revenue and unhappy customers.

Worrying about which pricing model would work for your AI solution is natural.

In this article, we'll explore 7 pricing models for AI products. Our goal is to help you find the ideal strategy to boost profits while delivering value to your customers.

We’ll cover:

  • Value-based pricing
  • Usage-based pricing
  • Subscription models
  • Freemium models
  • License fee models
  • Performance-based pricing
  • Hybrid pricing models
  • How to choose the best pricing model for your AI business

Let’s start with the first pricing model.

1. Value-based pricing

Value-based pricing is a strategy in which you set the price of your AI product based on the value it delivers to your customers. This stands in contrast to traditional cost-plus pricing models, which focus primarily on production costs and a standard profit margin.

To use value-based pricing for AI, you need to understand your customers' challenges and how your AI solution helps solve them. Are you saving them time? Improving accuracy or efficiency in their processes? These are the kinds of questions that get to the heart of the value you provide.

Putting a number on that value requires careful research. Look at your industry: Are there benchmarks for how much similar problems cost businesses? You can also survey your customers, seeking to quantify their frustrations with their current process and excitement about what your AI could provide.

The advantages of value-based pricing are noteworthy. When your price accurately reflects the value you're creating, it becomes much easier for customers to justify the expense. This can mean faster sales cycles, higher win rates, and increased customer satisfaction.

For instance, imagine a predictive analysis AI tool for sales teams. If your AI model helps close 10% more deals per quarter, you could price based on the average revenue per deal, factoring in the boost provided by your solution.

Since the price is tied to specific outcomes, it highlights the ROI of your AI product, motivating customers to adopt and fully engage with the solution.

2. Usage-based pricing

Unlike a flat subscription fee, usage-based pricing directly aligns cost with the amount the customer uses your AI solution. 

Customers' use of your AI product is identified as events (think individual API calls), and you add them all up as individual costs, which will show up in your clients' bills. 

How does it work? There are a few common ways to implement usage-based pricing for AI:

  • Data processed: Charge based on the volume of data that the AI analyzes or generates, such as the number of images processed or the amount of text analyzed.
  • API calls: Charge for each query made to the AI's API. This is common for AI services that are integrated into other applications.
  • Compute time: Charge based on the time (and processing power) the AI model uses to perform tasks. Keep in mind these (as well as the data processed from the first example) would show up as individual events in the customers’ bills. 

So, why is usage-based pricing so effective for AI? Here are some key benefits:

  • Fairness: Imagine a customer who only needs your image-recognition AI for a short-term project. With usage-based pricing, they pay for the time and data processing specific to that project, not a monthly fee that continues long after. This feels fairer to them and builds trust in your AI pricing model.

  • Scalability: Say a startup starts using your AI chatbot to augment its customer support. At first, usage is pretty light. But as their business thrives, the chatbot handles tons more interactions. Usage-based pricing means their payment scales in line with their growth, removing that worry of exceeding a plan's limits.

  • Attracts diverse customers: Many businesses, especially startups or those just testing new tools, are hesitant about hefty upfront fees. Usage-based pricing allows them to experiment with your AI solution at minimal risk. This means you might gain customers who wouldn't even consider you if you had traditional subscription models.

3. Subscription models

Subscription models are a tried-and-true way to price products, and they're just as powerful for AI solutions. Subscriptions are convenient because they allow you to create different tiers that align with how customers get value from your solution, make pricing accessible, and foster long-term customer relationships.

Let’s look into how you can adapt subscription models for your AI product: 

  • Feature-based tiers: This is a great way to cater to customers with different needs. Your basic tier could offer core AI functionality, perhaps the most commonly used features. As you go up the tiers, you unlock more specialized features, advanced model variations, or integrations that some users find essential while others won't need. 

    For example,
    a chatbot AI might have a basic tier for handling simple FAQs, while premium tiers unlock sentiment analysis or custom personality development.
  • Usage-based tiers: Even with a subscription model, you can still factor in usage. Offer a set amount of data processing, API calls, or computation time within each tier. Customers who occasionally use the AI can opt for a lower-priced plan, while power users will naturally gravitate towards higher tiers.
  • Service-based tiers: Different businesses might need different levels of hands-on support. Your basic plan could offer online documentation and community forums. Premium tiers could add priority support, dedicated account managers, or even custom development of specific AI features for that client.

Let’s take a look at some benefits of subscription models for AI products:

Subscription models are one of the best AI pricing models because they offer two core benefits for AI products. Firstly, they ensure predictable, recurring revenue streams. This predictability allows for accurate forecasting and budgeting, enabling you to confidently reinvest in developing and improving your AI solution over time.

Secondly, subscription models foster strong customer loyalty. Because customers have committed to a recurring payment, they're incentivized to fully adopt and learn how to best use your AI tools. Since they see continued value, they become less likely to switch to a competitor's solution.

4. Freemium models

This is a popular approach, especially for getting new AI products off the ground. The idea is simple: you offer a free basic version of your AI solution while locking advanced or expanded capabilities behind a paid subscription.

This free tier is incredibly powerful for attracting users. People are naturally drawn to trying things at no cost, removing that initial hurdle to adoption. It's a low-risk way for businesses to dip their toes in the water, experience your AI in action, and see if it solves some of their problems. 

This "try before you buy" approach can be especially convincing for those who are new to AI or skeptical about its potential benefits.

The real challenge with freemium lies in converting those free users into paying customers.  This is where carefully planned up-selling comes in. You need to design the free tier to be helpful but also leave users wanting more. 

This might mean limiting data processing in the free version, offering only the most basic output, or withholding integrations with other tools they might already use.

The main goal is to create that "aha!" moment when someone hits a limit within the free tier and realizes the value of upgrading to unlock the full potential of your AI solution.

5. License fee models

This is one of the AI pricing models that involves customers paying either a one-time upfront fee or recurring licensing fees to access your software. It's a good fit for specific markets and customer needs.

Popular licensing models shine in enterprise or highly specialized markets. Think of scenarios where customers expect long-term, heavy usage of your AI solution. It could be deeply integrated into their workflows and core systems. 

They might have strict data security or compliance needs that are best met with a model that gives them more control over the software. In these cases, paying a recurring fee for guaranteed access makes sense.

However, traditional licensing does come with some logistical considerations. You'll likely need a system to issue and manage individual software licenses.  

This means ensuring customers are adhering to the terms (how many users, how it's being deployed) and providing updates and patches as needed. There's often a compliance aspect, making sure only those who have paid have ongoing access.

6. Performance-based pricing

Performance-based pricing is a model that tightly links revenue with the success of your customer's AI implementation. Rather than paying for the AI solution upfront, the customer's cost is determined by the value it generates, such as increased efficiency and reduced errors.

Establishing the right metrics is crucial for this to work. Before deploying your AI, you'll collaborate with the customer to define their baseline performance. You should ask yourself questions about that baseline performance, like:

Is it the number of tasks their team can process per hour? 

The accuracy of a prediction model? 

The time it takes to identify an issue? 

Then, you'll set clear targets for improvement that justify the cost of the AI solution.

Now, let’s take a quick look at how using this pricing model for AI products can be both challenging and rewarding:

  • Rewards: When successful, this model fosters a strong partnership. Your customer only pays when the AI proves its value, fueling their satisfaction. Meanwhile, you have the potential to earn higher rates since your pricing is tied to quantifiable outcomes.
  • Challenges: Since many AI projects are about improvement, it takes some careful planning to make this model fair. You'll need to factor in a ramp-up time when the AI is learning and being optimized. Also, beware of the costs that come with keeping an AI solution up and running (think API calls and the cost of each one). 

7. Hybrid pricing models

Hybrid pricing models recognize that there's rarely a one-size-fits-all solution, especially in AI. Combining aspects of the pricing models we've discussed allows you to create a truly tailored offering for your AI product.

Hybrid models make a lot of sense for two main reasons:

  • You appeal to more types of customers: Your potential customers likely have very different needs and budgets. A startup just experimenting with AI might love a usage-based option that's low commitment. 

    a large enterprise that plans to integrate your AI might prefer a standard license with predictable ongoing costs. Hybrid models let you serve both.
  • It’s easier to find your fit: Perhaps you start with purely usage-based pricing. As you gather data on how customers interact with your AI, you might realize it makes sense to offer a subscription tier with a base usage allowance included. Using hybrid models lets you find a sweet spot and adapt and change when it makes sense to do so. 

Let's take a look at a few hybrid pricing combination examples well-suited for different types of AI products:

Scenario 1: Image generation AI

  • Base model: Usage-based pricing per image generated, perhaps with different tiers based on resolution or complexity.
  • Premium add-on: A monthly subscription unlocks advanced features like style transfer, image editing, or bulk generation capabilities. This appeals to those who use AI heavily and consistently.

Scenario 2: Predictive analytics AI for sales teams

  • Base model: Subscription model with tiers based on the number of users (sales reps) who need access.
  • Performance element: A performance-based additional component could be added. If the AI leads to proven increases in closed deals above a certain threshold, that triggers a bonus payment, ensuring the AI's value is shared.

Scenario 3: AI-powered customer support chatbot

  • Base model: Freemium model for basic conversational chatbot features.
  • Usage element: Charge for usage above a certain threshold based on the number of customer interactions handled.
  • Feature-based add-on: Paid subscription that unlocks sentiment analysis, custom branding, or integration with the customer's CRM system.

How to choose the best pricing model for your AI business

Picking the suitable pricing model for your AI business is critical, so let's break down how to make the best decision. There's no single correct answer, but a thoughtful evaluation process makes all the difference.

Here's a framework to guide your choice:

1. Understand your market

  • Identify your ideal customers. Are you targeting small startups or massive enterprises? Companies new to AI or those wanting cutting-edge solutions? Their needs and budgets will differ significantly.
  • Analyze the competitive landscape. How do existing solutions in your space price themselves? Understanding if competitors use subscriptions, usage-based models, or something else, gives you a baseline.
  • Gauge customer willingness to pay. This is where things like surveys, interviews, and even careful observation of online communities related to your AI niche can be beneficial. Even preliminary data is better than guesswork.

2. Analyze your AI product itself

  • Define the core value proposition. Does it save time, reduce errors, and increase revenue? This should align with how you price. If pure time savings is the primary value, usage-based or subscription models might make sense.
  • Determine the associated costs. Remember, pricing isn't only about customer value but also about making it sustainable for you. Are there ongoing computational costs that scale with usage? Factor this into your decisions.

3. Test and adapt your strategy

  • Embrace experimentation. Consider offering different pricing options in a limited trial or even A/B testing on your website to gauge customer response.
  • Remember, pricing isn't static. As your AI evolves and you learn about the market, revisiting your pricing is perfectly normal. Perhaps you started with subscription tiers but realize a hybrid model with some usage components would better capture the value you offer.

Next steps

After reading our guide, you should understand the potential benefits and complications of choosing and implementing AI pricing models for your software.

However, manually tracking usage, building flexible pricing models, and ensuring accurate billing can quickly overwhelm in-house billing systems. That's where Orb becomes a vital tool in your arsenal.

Orb is a powerful billing platform that helps you overcome the challenges of AI pricing, giving you the tools to launch, iterate, and scale your AI pricing models with confidence. 

Here's how Orb helps:

  • Granular usage tracking: Orb's robust infrastructure reliably tracks diverse usage metrics standard in AI, such as API calls, compute resource usage, data processed, or any custom metric relevant to your AI solution. This ensures billing aligns precisely with customer value.
  • Flexible pricing models: Orb supports the experimentation you need. Easily configure tiered pricing, volume discounts, prepaid credits, overage fees, and more. This means you can tailor your pricing to specific customer segments and adapt as your AI product evolves.
  • Real-time invoicing and reporting: Visualize usage data on customer-friendly invoices, providing transparency that builds trust. Orb's reporting gives you real-time insights into revenue patterns, allowing you to make data-driven pricing decisions.
  • Frictionless integrations: Orb integrates with your existing tools like data warehouses, payment gateways, and accounting software. This avoids data silos and simplifies financial workflows.

Learn how Orb can help you solve your AI solution’s billing in record time.

May 22, 2024
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