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How to Price Your AI Product

In the ever-evolving landscape of artificial intelligence (AI) products and services, determining the right pricing strategy can be make-or-break for your product. In this article, we will explore various factors and strategies to consider when you're determining how to price AI products, helping you maximize revenue and remain competitive in the market.

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AI pricing
AI pricing
AI pricing
AI pricing

Determining Your AI Vertical

When deciding how to price your AI product, you should first understand the ways AI products are positioned in the market. AI pricing varies wildly depending on how and where the AI is used. Companies like OpenAI focus on multiple categories, from models to APIs. Other companies, such as Notion and Atlassian, try to embed AI as naturally as possible into user workflows. Here are some of the primary verticals that AI products fall into:

Infrastructure: This covers server costs, storage, and networking requirements. For instance, a cloud-based AI software that uses extensive real-time computation may require high-end servers, leading to significant expenditure on infrastructure.

Data: Data collection, cleaning, and labeling are integral parts of building robust AI systems. Some companies, such as autonomous vehicle developers, can spend substantial amounts on gathering and preparing high-quality data.

Tools: Machine learning frameworks and natural language processing libraries are examples of necessary tools in AI application development. The selection of these tools can have implications for your pricing, primarily if you opt for premium, high-performance tools.

Workflows: These represent the sequential steps involved in model building, training, and deployment pipelines. High computational demands of certain workflows, like deep learning, may impact pricing.

Models: The algorithms and trained models are the sine qua non of AI applications requiring considerable expertise to develop, often justifying a premium price.

Outputs: APIs, apps, integrations, etc., built on models present the functional layer that directly interfaces with the user. Their functionality, reliability, and user experience influence perceived value and subsequently, the price.

Outcomes: The business value and return on investment (ROI) from deploying AI applications ultimately determine their worth to customers. A sales prediction model that accurately forecasts demand, thereby reducing inventory costs, offers considerable value, which should be reflected in its price.

Pricing Components of AI Products

After the vertical of your AI product, it's important to decide how your AI product fits into overall company strategy. Some companies use AI features as a way to increase the value of their existing product. Alternatively, AI features or products could be used as a marketing push. Others still make AI the foundation of their company and use it as the primary value driver. We break up the two building blocks of Ai pricing into Strategy and Packaging.

Strategy: You might price Data and Tools products lower as they operate as building blocks, while Models and Outcomes—directly linked to the core value proposition—can command a higher price tag. Look at how tech giants like Google offer their APIs. Translation or map integration APIs are generally priced cheaper than sophisticated machine learning APIs, which offer highly differentiated services.

Packaging: Offering bundles and packaged offerings for the full AI workflow can be an effective strategy. Think of IBM with its AI suite Watson, which provides package deals integrating numerous AI services, from NLP to visual recognition.

AI Startups: Pricing Considerations

For AI startups, the challenge of pricing is made even more difficult by market uncertainty and the absence of established pricing norms. Most companies are still just starting to figure out their AI pricing strategy. Here are some basic considerations for startup pricing:

Maximizing revenue, not just profit: It's key to zero in on pricing that maximizes your revenue stream. For instance, Dropbox found success in a freemium model which, while not maximizing immediate profits, provided a steady flow of income as users got on board and eventually upgraded their plans. Many AI startups are seeing extremely high churn due to high interest and low product-market fit. Be aware of how you can keep more customers around for the long haul.

Differentiated value: Competitive intelligence is useful, but pricing should be based on the differentiated value you provide. If your AI offers unique insights or efficiency gains, your price should reflect this. A case in point is Darktrace's AI-driven cyber defense solution, which commands a premium through its cutting-edge anomaly detection.

Evolving strategies: Many AI SaaS products are working out their pricing strategies, so market alignment is an evolving target, not a fixed one. Don't be afraid to test different pricing philosophies on different customer cohorts.

Getting Started with Pricing Your AI Product

Pricing your AI product is more of an art than a science, requiring a balance of value comprehension, market dynamics, and empathy for new and existing customers.

  1. First, focus on differentiated value. Understand the business impact of your solution. If your AI can cut costs or generate revenue for your customers, quantify it. Use this as a reference in pricing.

  2. Next, understand how your AI product affects value creation. Price for value creation, not just cost recovery. If your AI tool creates significant efficiencies or opens new revenue streams for your customers, your price should reflect that value.

  3. Finally, don't forget to factor in COGS.Take into account both fixed and variable costs, including data, tools, research and development, and personnel. An AI chatbot might have comparatively low COGS while providing high value, justifying higher margins. However, a small number of customers will use a large relative percentage of your API costs to services like OpenAI. If you charge a flat fee, make sure the fee is large enough to cover the total cost.

Putting Customer Willingness to Pay Above Competitive Intelligence

Pricing is about more than matching competition. It's about understanding what your AI product is worth to your customer. When basing your pricing strategy solely on competitive intelligence, you risk getting caught in a race to the bottom. Continually lowering prices to meet or undercut competitors removes the value of an industry. Emphasizing customer willingness-to-pay helps negate such risks and enables the product to maintain or even enhance attractiveness while still generating reasonable revenue.

This is hard to do well in early industries, but as your competitors likely don't have any idea what they're doing either… you have some room for flexibility. Our recommendation: start high on your pricing and see who bites. If you can't get your customer base to adopt your AI product at more than 15% (shameless plug, you can track this easily with Flywheel feature reports), the price might be too high.

These matter less for AI products with high fixed costs but low variable costs. Software firms like Tableau can afford a pricing model based on user count, disregarding the negligible marginal cost because the primary value lies in the analytics and visualization capabilities, not the software distribution.

How SaaS Companies Should Approach AI COGS

Artificial intelligence introduces additional complexities to the standard cost of goods sold (COGS) analysis for SaaS companies. In the AI space, costs are typically skewed towards a high initial investment, with lower ongoing costs. To reap the full benefits of AI-based product offerings requires a comprehensive approach to AI COGS. A quick note that the below section is more important for larger organizations than scrappy startups.


Amortizing Initial Investments

When considering costs over time, it's important to spread the investments across the product's lifecycle. For example, the costs incurred in developing AI models, data acquisition, and initial infrastructure can be amortized by spreading them out over an extended period. This approach softens the impact on costs, while still taking into account the value being delivered to users.

Consider an AI-powered fraud detection tool used by a SaaS company. The initial costs of developing and refining the model may be significant, but subsequent costs related to model deployment and ongoing analytics would be comparatively lower. By amortizing these costs, companies can price their offering in a way that reflects the value provided while maintaining profitability.


Remaining Flexible and Adapting to Market Demands

Unlike traditional, non-AI software, the AI landscape is rapidly evolving. As a result, companies need to be more flexible regarding AI COGS and should continually revise the analysis based on changing market dynamics. As a SaaS company, this might entail investing in upgrading models and training data to remain innovative or adopting new infrastructure solutions that become available.

A good example can be found in Adobe's Creative Cloud suite, which has integrated AI-powered features (such as automatic image editing and content-aware fill) across various applications to streamline creative workflows. By factoring in the ongoing investments necessary to stay ahead in a competitive market, Adobe can maintain a flexible and dynamic pricing strategy that achieves a balance between product value and profitability.

The Pros and Cons of Offering Freemium AI

Adopting a freemium pricing strategy, one that gives broad access to basic features while offering premium value-added add-ons, is currently a common approach for AI companies. However, just like with standard SaaS freemium pricing, there are major negatives to consider.

Pros

Product Discoverability: Freemium models foster product visibility radically. Offering your AI product for free can help amass a pool of users who can explore and understand your product value proposition without financial risk.

Expanding User Base: With little or no barriers to entry, a freemium model can quickly attract users and ramp up adoption. Successful AI products like Slack and Evernote used this approach effectively to scale their user base.

Data Acquisition: From a strategic standpoint, a larger user base equates to access to more data. This can be particularly critical for AI-based businesses, having more data can help refine algorithms, tailor functionalities, and improve product capabilities.


Cons

Revenue Uncertainty: A key risk in the freemium model is revenue uncertainty. Unless properly managed, companies can end up with a business model where many users access the product for free, while few are willing to upgrade to premium, viable, revenue-generating offerings.

Cost Implications: There will be an underlying cost associated with servicing all users, including the non-paying ones. Hence, companies opting for the freemium model need to carefully manage their resources.

Perceived Value: There's a potential risk that users may not perceive the real value of your product if they get used to a free version with substantial features. This might pose challenges in convincing them to upgrade to premium packages.

Integrating AI Pricing with Existing Pricing and Packaging

AI products often do not exist in isolation but as as an additional value-add for existing products and companies. Thus, integrating the pricing for your AI capabilities with your existing pricing and packaging strategies is key. Doing this without angering existing customers or turning off new customers is a fine balance.


Integration with Existing Pricing

If you’re a SaaS company branching into AI services, the most seamless approach often involves meshing your AI offering within your existing pricing strategy. You could offer AI capabilities as add-ons or integrate them within premium packages. Microsoft’s approach to AI within Office 365, which includes AI enhancements at each subscription level, is a fitting example.


Packaging AI Features

Cleverly bundling AI offerings with existing services can allow customers to perceive additional value. Consider how Spotify integrates its AI-generated personalized playlists into its standard memberships, adding value with minimal disruption to their existing pricing structure.


Aligning with Customer Expectations

As AI becomes more mainstream and customers understand its capabilities better, integrating AI pricing will require alignment with customers' evolving expectations. Any disconnect in terms of price, features, and perceived value can lead to customer dissatisfaction, hence, the pricing strategy should offer tangible value commensurate with cost.

Fin

In conclusion, a successful pricing strategy for AI-based SaaS offerings relies on prioritizing customer willingness to pay and considering how changes will affect existing pricing. SaaS companies should focus on creating a compelling pricing structure that truly reflects the value of their AI-powered products and fosters growth in the long term. And, finally, make sure to keep an eye on the lifetime value of your AI products — don't fall victim to the high-volume high-churn paradox.


Published on

Jan 22, 2024

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and the future of growth is here.

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and the future of growth is here.

Track and activate up to 1,000 active contacts a month, for free.