How to price your AI product: 8 helpful tips

Pricing a SaaS product can be very complex. Pricing an AI application can be even more difficult. There are many variables to consider, and finding the right strategy can mean hours of lengthy discussions and research. 

But it doesn’t need to be like this. 

We’re sharing 8 AI pricing tips — here’s a quick preview:

• The importance of understanding your value proposition
• Looking at your AI product costs
• Why researching the market is so important
• Reasons why value-based pricing can work
• The importance of offering various pricing tiers
• Implementing usage-based pricing
• A quick look at some psychological pricing techniques
• Why you should be ready to adapt and iterate constantly

Let’s get started with the first tip.

1. Understand your AI product’s value proposition

Knowing how to price AI products successfully hinges on one crucial point: Clearly understanding the value they bring to the table. This sounds simple on paper but involves going beyond buzzwords and digging into the heart of what your AI application offers.

Here are some tips to do precisely that:

• Pinpoint the problems it solves: Identify the pain points your AI product addresses. Does it reduce time-consuming tasks? Does it uncover insights that would be difficult for a human to find? Is it about making processes smoother or removing bottlenecks? 
  
  Be very specific here.

• Focus on customer outcomes: Go deeper than just identifying problems. Think about the results your customers achieve by using your product. Does it help them save money? Does it drive higher customer satisfaction scores? Does it open up new revenue streams? 
  
  Quantify the benefits whenever possible.

• Zero in on what makes you unique: The AI market is more crowded than ever. What sets your AI solution apart? Maybe you have a proprietary algorithm that delivers exceptionally fast results. Or perhaps you've trained your model on a niche dataset, giving you an edge in a particular industry. 
  
  Pinpoint these differentiators — they will absolutely factor into your pricing.

Pro tip: Knowing your value proposition inside-out isn't just about marketing. It becomes the foundation of your AI pricing strategy. When you can articulate exactly what your AI solution does for its users, you gain the confidence needed to charge prices that reflect that value.

2. Keep an eye on your costs

Think of pricing your AI solution as an equation. The value you offer is on one side, and your costs are on the other. Understanding your expenses is vital for setting a price that guarantees both profitability and sustainability.

Start by examining the direct costs involved in building and running your AI product. This includes paying for servers, data storage, or even licensing specialized datasets.

Remember that if you use third-party APIs (like OpenAI for text generation), those API calls translate directly into costs.

Remember your indirect costs. These include the salaries of your expert AI team, the expenses associated with research, and the ongoing investment you make in improving your models and features. 

These costs might be less obvious, but they're still part of the bigger financial picture.

Pro tip: Particularly for AI products, initial investments can be high, while ongoing costs may be lower. Consider strategically spreading out those initial costs over the product's lifetime.  

The key is to have a realistic grasp of your total expenses — this allows you to build an AI pricing structure that keeps your business healthy for the long haul.

3. Research and analyze the market and its trends

Knowing where your AI solution fits within the broader market is essential. Do your homework first, and don't start off by simply guessing what customers would pay.

Take the time to thoroughly analyze the competition. Are there similar products out there? What are their pricing models (per user, per task, by data usage)? Are there industry-specific AI solutions you could learn from?  

The answers to these questions will give you valuable context.

Understanding your ideal customers is just as important. Different market segments might value your solution differently. A small startup trying to automate basic tasks likely has a lower budget than an enterprise seeking sophisticated analytics. 

Tailor your research and analysis to the specific types of customers you hope to attract.

Pro tip: The AI industry landscape is constantly changing. Stay up-to-date on new trends and emerging competitors. This will allow you to adjust your pricing over time, keeping you competitive without sacrificing value.

4. Consider using value-based pricing

Shift your focus away from covering your costs or keeping up with everyone else's charges. Instead, consider AI pricing strategies that emphasize the value you deliver. 

This is known as value-based pricing.

Think about the concrete outcomes your AI solution helps customers achieve. Does it save them hours of work each week? Does it detect errors in their processes with far greater accuracy than humans? Does it find insights in their data that boost their revenue numbers?

Try to measure the value your solution brings. If a task that used to take someone half a day now takes an hour with your help, you can calculate how many work hours that translates into.  

If you know how much a client pays their staff, you can start putting a dollar figure on those saved hours.

Remember: Value-based pricing gives you the power to charge a premium confidently.

Why? Because you're highlighting what the customer gains, not just what it costs them to use your AI solution.

It's a win-win: Customers get a solution that makes a real difference, and you get paid fairly for the value you create.

5. Give users more options by offering multiple pricing tiers

Tiered pricing is a great way to meet the needs of a broader range of customers while maximizing your revenue. Think of it less as a rigid system and more as a way to offer different levels of value within your AI solution. 

Here's how to approach tiered pricing strategically:

• Target different budgets and needs: Consider offering a basic tier for those just starting out, a mid-tier for growing businesses, and a premium tier for those needing maximum capacity and priority support.
• Features: Think carefully about the specific functionalities that differentiate your tiers. some text
  • For example, the basic tier should include your AI solution’s core features (e.g., text summarization or image tagging). 
  • The mid-tier could expand on this with additional features (e.g., sentiment analysis and custom model training). 
  • The premium tier might offer access to all your AI product’s capabilities, potential for integrations with other tools, and white-glove customization options.
• Usage limits: Tiers can be clearly differentiated by what a customer is allowed to use. some text
  • For example, the basic tier might have a limited number of API calls or data requests per month. 
  • The mid-tier would offer higher allowances for API calls or requests, perhaps with the option to purchase more usage if needed. 
  • The premium tier might have very high limits on usage, potentially even unlimited for certain features.
• Support levels: Offering varying degrees of customer support can be another way to differentiate your tiers. For instance, premium tiers might include faster response times or personalized onboarding.

6. Think about implementing usage-based pricing

Usage-based pricing models are a tempting alternative to models such as per-seat subscriptions. Instead of charging a flat fee, your price changes based on how much customers use your AI solution. Here's why this might be a great fit:

• It allows you to align price with value: With usage-based pricing, customers pay in proportion to the benefits they receive.
  
  If a customer has a particularly data-heavy month and needs to make many API calls, their cost reflects that increased value. Conversely, during slower periods, their costs go down, too.

• It draws in more potential customers: Usage-based pricing can lower the barrier to entry. New customers might be hesitant to commit to a significant monthly fee — but a model where they start small and pay as they go can feel less risky.
• It offers scalability: Usage-based pricing automatically scales along with your customers. As a business grows and needs more from your AI product, their payments naturally increase alongside the value they're getting.

Some examples of usage-based pricing for AI solution:

• Charging per API call: Common when working with services like OpenAI.
• Charging based on data processed: This may be a good metric if your AI analyzes large amounts of customer data.
• Outcome-based pricing: This is challenging but potentially very powerful. Could you tie your price to a measurable metric your AI influences? (For example, a percentage of sales leads generated by your tool.)

Keep in mind: Usage-based pricing might not be a good fit for everyone. If your customer's usage is difficult to predict or measure, it can lead to unexpected charges and irregular bills.

7. Use psychological pricing techniques

Don't underestimate the power of a well-presented price tag. Humans are not purely logical when making buying decisions, and there are ways to nudge customers toward your AI product. 

Let's explore a couple of techniques:

Decoy pricing

Decoy pricing is a bit more intricate but can be very effective. The idea is to introduce a third pricing tier that's slightly more expensive than your desired mid-tier option. 

This seemingly irrelevant addition can make your middle tier seem like a much better deal. Customers who might have hesitated over the mid-tier price alone are now more likely to choose it because it appears to be the most reasonable option among the three.

Anchoring

Anchoring plays on the tendency for people to rely on the first piece of information they receive. In the context of pricing, you can leverage anchoring by initially showcasing a higher price point (such as the individual price of a specific feature).  

Then, reveal that this feature is actually included in a lower-priced bundle. By anchoring the customer's perception of value with a higher starting point, you make the bundled price seem like a much better deal.

8. Adapt and iterate to keep your pricing relevant

Think of your AI pricing strategy as a living and breathing being that is constantly evolving. The AI market is always changing, your product will improve, and your customer base will shift. Be prepared to adjust your pricing to stay competitive and maximize your value.

Here are a few things you can do to make sure your product and its pricing remain relevant:

• Don't just guess, ask: Gather feedback directly from your customers. Conduct surveys or interviews to find out what aspects of your pricing resonate with them and where there might be pain points.
  
  
• Analyze your data: Dig into your sales data. Are there specific tiers that are outperforming others? Are customers frequently hitting usage limits in your mid-tier, suggesting it might be priced too low? Your data holds valuable information.
  
  
• Don't be afraid to change when you need to: The insights you gain from feedback might lead you to make adjustments. Perhaps a feature you thought was premium material is better suited for the basic tier. Always be ready to adapt.

Next steps

Now you have a solid understanding of the strategies and best practices for pricing your AI solution. But, implementing a complex, usage-based pricing model while ensuring accurate billing can be a major obstacle. 

There’s no sugarcoating it: The constant need to track, calculate, and adjust invoices based on highly variable usage data is resource-intensive and error-prone.

Thankfully, specialized billing platforms designed for the unique requirements of AI pricing can simplify the process. Orb is a done-for-you billing platform with a powerful suite of tools built to help you implement and maintain an effective AI pricing model:

• Flexible usage tracking: Orb's robust usage tracking system seamlessly ingests complex data, allowing you to price based on granular metrics like API calls, credits, tokens, or any custom metric relevant to your AI product.
  
  
• Effortless pricing changes: Orb lets you implement pricing changes quickly and confidently. Model different scenarios, pre-schedule price migrations, or apply updates in real time — all without complex code changes.
  
  
• Transparent invoicing: Orb provides clear, visual invoices built on accurate usage tracking. This builds trust with your customers, reduces billing disputes, and improves financial reporting.
  
  
• Revenue reporting: Orb connects your usage data directly to financial reporting, offering valuable insights and ensuring accurate numbers. Gain a precise understanding of your MRR, ARPU, churn, and other key metrics that are vital for your growth strategy.

Orb's adaptability ensures that it can effectively support your specific AI pricing model and business requirements, giving you the freedom to evolve and experiment without billing limitations.

Learn how Orb can help you solve your AI product’s pricing and billing in no time.