AI Monetization: How to Approach AI Pricing

The sheer number of AI tools available is growing. Be it stand-alone products or add-on functionality to existing technology, we’re entering a space where consumers in every industry aren’t just ready to welcome AI, they’re expecting it. AI has become the norm, and as you implement AI into your own product, or build a new one based on AI, you’re going to need to figure out your AI monetization strategy: how will you decide on your AI pricing?

Now this is actually a more complicated topic than you may think. As a PM, particularly if your product has existed long before AI came along, you’ve already figured out your product pricing strategy and model, so why can’t your AI feature sit within that? Well, it can, but you need to think about it first and make sure it’s right for your product and the right approach for your industry. 

Plus, some common AI monetization strategies that customers are getting used to can be pretty different from what you’re using. 

So, let’ me walk you through all this and have a look at AI monetization, and how you go about deciding how to price your AI features or products. 

What is AI monetization? 

AI monetization is the process of turning AI-powered features or products into revenue-generating assets. As AI becomes more embedded in software and services, companies are exploring different ways to charge for its value, whether as a standalone product, an add-on, or a core part of an existing offering.

At its core, AI monetization revolves around how businesses capitalize on AI’s capabilities to drive growth. Some companies charge directly for AI features, making them a premium upgrade or a pay-per-use function. Others bundle AI enhancements into existing plans to increase adoption, retention, and customer lifetime value.

AI monetization and the way you integrate AI into your product or service can fall into two distinct categories: 

• Direct Monetization – Charging explicitly for AI functionality.
• Indirect Monetization – Using AI to improve engagement and retention without charging for it separately.

Let’s cover those in more detail:

What are the different types of AI Monetization? 

When monetizing your AI product or service, there are two primary approaches: 

Direct and Indirect AI monetization strategies.

These dictate the overall, top-level strategy you’re going to follow regarding where AI sits within your current product. The right choice depends on how integral AI is to your offering and how your users perceive its value.

Direct AI monetization 💰

Direct AI monetization means explicitly charging users for AI-driven functionality. This approach makes sure that AI generates direct revenue, whether as an optional upgrade, a standalone product, or a core part of a pricing shift.

Here are the three main strategies within direct AI monetization:

• AI as an add-on – Here, users pay extra to access AI-powered capabilities on top of their existing plan. This is ideal for features that provide distinct, high-value enhancements.
  Best for: Products where AI delivers a clear competitive advantage without needing to be core to the main offering.
• Standalone AI product – With this, the AI itself is the primary product, separate from what you already have and users subscribe or pay based on usage. These offerings are built entirely around AI functionality.
  Best for: Products where AI is the main value driver, rather than an enhancement to an existing tool.
• Bundled with a price increase – With this option, AI features are incorporated into existing plans, but prices are adjusted to reflect the added value. This ensures AI-related costs are covered while maintaining a seamless experience for users.
  Best for: Products looking to enhance their value proposition while avoiding the friction of separate AI-based upsells.

Indirect AI monetization 🔄

Indirect AI monetization focuses on leveraging AI to improve user experience, engagement, and user retention rather than charging for it explicitly. Here you’re utilizing AI as a way to make your product more compelling in order to drive growth. While not a direct revenue driver, this strategy can encourage more new customers, increase product stickiness, lower customer churn, and boost customer lifetime value.

Here are three common approaches to indirect AI monetization:

• Bundled without a price increase – Here, AI features are included in standard plans at no extra cost, serving as an incentive for acquisition and differentiation in a competitive market.
  Best for: Companies prioritizing long-term growth, customer loyalty, and differentiation over immediate monetization.
• Freemium AI – With this approach, a basic version of AI-powered features is available for free, while premium or advanced capabilities require a paid upgrade. This model encourages adoption while creating a natural upsell path, just like regular freemium.
  Best for: Companies that want to showcase AI’s value upfront and convert engaged users into paying customers.

Completely free AI features – AI tools are provided at no extra cost as a value-add, helping increase product usage, user activation, customer satisfaction, and brand loyalty.
Best for: Platforms looking to enhance user engagement and retention while keeping AI as a competitive differentiator.

Choosing the right AI monetization pricing model

Now here’s where things can get tricky. The above strategies define how AI fits into your product offering, but the next step is determining how you charge for it. This is where different AI pricing models come into play. 

Now I know what you need to think about when monetizing your AI. I had to figure out the monetization strategy we used for our advanced AI, CoPilot, and make sure it suited our overarching product and our users.

We’ve gone for an indirect monetization strategy, so all users of ProdPad have complete access to CoPilot at no extra cost. Learn more about our AI for Product Managers or jump into the Sandbox to try it out.

Once you’ve figured out if you’re monetizing AI directly or indirectly, there are two core options you can choose in terms of how you actually charge your users for it: 

Subscription-based or outcome-based.

Subscription-based AI monetization 📅

This is the most common model in SaaS, where AI features are included in a recurring pricing structure. This model works well for businesses looking for predictable revenue and a scalable growth strategy.

When using a subscription-based AI pricing model, you can choose:

• Seat-based pricing – Pricing is determined by the number of users accessing AI features. For example, charging $50 per user/month for AI-powered automation tools.
• Skill-based pricing – Pricing varies based on the complexity or level of AI capabilities offered. For example, basic AI assistance is included in lower-tier plans, but advanced machine-learning capabilities require a premium upgrade.

Outcome-based AI monetization 🏆

Instead of charging upfront or per user, outcome-based pricing ties AI costs to measurable results. This aligns value with customer success but requires clear performance metrics.

When charging by outcome, this can come in the form of:

• Usage-based pricing – Customers pay based on how much they use AI features (e.g., API calls, queries, or data processed). This could be a chatbot platform charging $0.01 per AI-generated response.
• Output-based pricing – Customers pay based on the volume of AI-generated outputs (e.g., reports, content, predictions). This might be a generative AI tool charging per 1,000 words generated, or blocking access once you’ve used a number of credits.
• Outcome-based pricing – Customers pay when AI delivers a tangible business result, like increased revenue or cost savings. This one is specifically suited to B2B businesses and can be like an AI-powered hiring tool charging per successful hire.

Pricing AI isn’t just about picking a number, it’s about aligning monetization with perceived value, cost structures, and customer expectations. 

So, to get things right, start by choosing whether AI is a direct or indirect revenue driver, then refine your approach with the right pricing model and structure. The right strategy will depend on your product’s role in the market, your users’ willingness to pay, and how AI enhances their experience.

How are most companies handling their AI monetization? 

Research from Lenny Rachitsky shows that 59% of AI companies bundle AI features into their existing subscription-based plans instead of charging separately. Sometimes, this is done with a price increase. 

This makes sense. AI is expensive to build and maintain, and bundling avoids the friction of an additional paywall. By doing so, you can provide immediate value without deterring adoption.

For many, this strategy balances recouping high investment costs by making AI widely adopted in your product, and not just used by a select few specifically seeking AI functionality. Bundling ensures that you aren’t reliant on a small group of early adopters and allows you to integrate AI without the risk of sticker shock for customers.

However, just like flat-screen TV prices in the early 2000s, AI’s high infrastructure costs won’t last forever. Advances in AI models and hardware are already driving prices down. Companies like DeepSeek, for example, have gained attention for their impressive cost efficiency. As AI becomes cheaper and more accessible, businesses may need to reconsider their AI pricing model.

So what happens when AI becomes cheaper?

Right now, AI’s high infrastructure costs justify bundling – it offsets the expense while driving adoption. But that justification won’t last forever. As AI’s development and operational costs decline, the economics of AI monetization will shift.

When cutting-edge technology becomes more affordable, it also becomes less special. Just like cloud storage and streaming services, AI will transition from a premium add-on to an expected baseline feature. Companies that once charged a premium for AI-powered capabilities may find customers unwilling to pay extra for something they now see as standard.

This raises an important question: How will businesses continue to monetize AI when it’s no longer a differentiator?

Some may shift toward usage-based pricing, charging for AI-heavy workloads while keeping basic AI features free. Others might introduce tiered AI offerings, where advanced capabilities remain exclusive to higher-priced plans. Alternatively, businesses could pivot toward AI-powered services – providing consulting, automation, or specialized AI models tailored to specific industries.

The key takeaway? The AI pricing model that works today may not work tomorrow. As AI’s cost curve trends downward, companies need to plan for a future where bundling alone won’t cut it.

What do customers expect from AI monetization? 

With so many companies bundled AI into their existing plans early on, when customers see AI included across multiple tools without an extra charge, it sets a new norm: AI isn’t a luxury, it’s just part of the product.

I think this shift in expectation has major implications for AI pricing. If AI is now “just part of the package,” customers may resist paying extra for it. They’re only going to part with their cash if it delivers clear, tangible value beyond the basics. While foundational AI-powered enhancements (like autocomplete, search recommendations, or basic chatbots) are increasingly expected to be free, more advanced AI capabilities – such as predictive analytics, complex automation, or industry-specific AI tools – can still command a premium.

Crucially, expectations differ by industry. In software platforms where AI is embedded into everyday workflows (think productivity tools, CRMs, and customer support platforms), users expect AI to be included. But in industries where AI tools are more specialized or standalone, like financial modeling, healthcare diagnostics, or creative AI tools, customers are more accustomed to paying separately for advanced AI capabilities.

This means AI pricing isn’t one-size-fits-all. The right strategy depends on what your users expect and how they perceive AI’s value within your product. If your customers see AI as table stakes, bundling makes sense. If they view it as a premium service, a separate charge might be viable. Either way, aligning with customer expectations is critical. Once AI becomes an assumed feature, trying to charge for it after the fact could be an uphill battle.

How do I choose the right AI monetization strategy for my product? 

When it comes to monetizing AI, you need to pick the option that’s right for you, not just the most popular. Just because most companies are bundling AI into their core offerings doesn’t necessarily mean it’s the right choice for your product. To determine the best AI monetization strategy, consider the factors below as we compare direct and indirect AI monetization.

Is direct monetization right for my product?

Charge customers directly for AI when it delivers unique, high-impact value that extends beyond your core product. If AI is the main event – not just an enhancement – users are more likely to accept paying for it.

Direct monetization is best suited for:

• Standalone AI capabilities – If AI is a distinct, high-impact feature (e.g., AI-generated content, predictive analytics, workflow automation), direct pricing makes sense.
• High operational costs – Running AI models comes with expenses like computing power, storage, and security. Plus, if you’re using someone else’s AI model to power your AI feature, you’ll have usage costs for that. Charging for AI features can offset these costs.
• Measurable ROI for users – If customers can directly attribute time or cost savings to your AI, they’ll be more willing to pay for it.

Is indirect monetization right for my product?

An indirect monetization strategy can be effective if your AI is designed to enhance core functionality rather than provide a standalone capability. If your AI features are aimed at boosting user engagement or improving essential aspects of the product (like smarter recommendations or better search), you might opt to bundle them into existing plans without an additional charge.

Companies like Zoom and Shopify use this strategy, offering AI as part of their core offerings to drive more usage, conversion, and customer retention.

Indirect monetization is best suited for: 

• Core product enhancements – If AI improves the core functionality of an existing product, customers may expect it to be included.
• Customer expectations favor bundling – If competitors are offering AI as a built-in feature, charging separately could put you at a disadvantage.
• Retention and engagement play – AI that drives frequent usage (e.g., smarter workflows in productivity apps) can be more valuable in the long run when bundled rather than sold separately.

The third way…

While direct and indirect monetization strategies each have their advantages, there’s another way. Don’t think of these options as binary, one or the other. They can be merged into a hybrid model.

I think the hybrid model can offer the best of both worlds. By giving away some AI features for free (to boost adoption and provide value), while charging for premium features, you can avoid alienating customers while still capturing the value your product provides.

A hybrid model works well when:

• You want to quickly build adoption without charging upfront.
• Your product’s core functionality benefits from AI, but some advanced features offer unique, higher-value capabilities worth paying for.
• You’re in a competitive landscape where offering AI for free can help you stay ahead, but charging for premium features allows you to capture revenue.

I prefer a hybrid model – offering some AI for free (to boost adoption) while charging for premium AI features. It avoids customer backlash while still capturing value. The best approaches usually do cater to multiple facets rather than being a blanket style.

What’s the best pricing model for my AI? 

So you’ve chosen your AI monetization strategy, you know that you want to either charge directly for it or add it in as a value driver to your existing plan. Sweet, but your work isn’t done. 

How do you decide what pricing model works best for your AI tool, be it subscription-based, outcome-based, and everything else in between? 

With your approach to AI monetization ticked off, here’s how you decide the particulars of what you’re going to choose: 

Subscription-based pricing

If you’re looking to get predictable revenue from your AI, then subscription-based pricing is probably the way to do it, especially when your AI features are so deeply integrated with the rest of your product experience. 

Of course, you still need to decide if you want skill-based or seat-based pricing. Here’s how to pick them: 

• Seat-based pricing – Works well for AI tools where value scales with the number of users (e.g., AI-powered collaboration software). However, this model suits direct monetization better since it explicitly charges for AI access, making it less ideal for indirect monetization, where AI is a background value driver.
• Skill-based pricing – Best when AI capabilities vary by plan, allowing customers to pay for the complexity they need. It works for both direct and indirect monetization, as AI can be used to differentiate pricing tiers without requiring a separate AI charge.

Outcome-based pricing

Outcome-based pricing is most effective when the value derived from usage or business impact is both clear and measurable. Here’s a breakdown of how to choose the right model within this category:

• Usage-based pricing – Ideal for AI APIs, chatbot platforms, or AI-powered analytics, where customers expect to pay based on consumption. This model supports direct monetization but can also be blended into an indirect strategy if AI is used to drive engagement (e.g., offering a free allowance before charging).
• Output-based pricing – Works well for content generation, predictions, or AI-driven automation where customers are paying for tangible deliverables. This fits direct monetization strategies but may not align well with an indirect approach, where AI is embedded rather than sold separately.
• Outcome-based pricing – Suited for AI that delivers measurable business results, like cost savings or revenue generation. This model is inherently tied to direct monetization since customers are charged based on the impact AI delivers, making it less applicable for bundled AI features.

Still unsure of what combination suits you best? Okay, let me hammer this home with a few examples:

If your AI tool is a core feature enhancing the user experience, indirect monetization with skill-based subscription pricing might be the best fit.

If your AI requires significant computational resources, usage-based or output-based pricing within a direct AI monetization strategy can help recoup costs efficiently.

If your AI delivers measurable business outcomes, outcome-based pricing ensures alignment between the value delivered and the price charged.

If you want to track the ROI of your AI investments, seat-based pricing offers predictability but works best with direct monetization.

Making money from AI 

In conclusion, AI monetization presents a unique challenge for Product Managers, but also a great opportunity to adapt and innovate pricing strategies. Whether you choose direct, indirect, or a hybrid approach, the key is to align your AI features with customer expectations, business goals, and the evolving landscape of AI technology.

By understanding your product’s value, user needs, and the dynamic costs of AI, you can craft a strategy that drives growth, customer satisfaction, and long-term success. The right approach will depend on your specific product and market, but with thoughtful planning and adaptability, your AI monetization strategy can be a powerful tool for sustaining your business’s competitive edge.

Here at ProdPad, our in-built AI, CoPilot, is included with any plan. If you have ProdPad, you have the full functionality of our advanced, Product Management AI. Come see what CoPilot can do, and discover how it enhances our tool and helps you become a more effective Product Manager. Start a trial to learn more.