Generative AI is fast becoming an important product feature for many SaaS companies. As a pricing consultant to SaaS firms, I thought it would be valuable to delve into the process of pricing generative AI for SaaS. As I was contemplating an outline for this article, it dawned on me that I could use generative AI to write a first draft. I asked ChatGPT to write a pricing article in many different ways until it produced a solid draft. In its current state of evolution, it can take a little iteration to optimize generative AI output. The winning question that generated a good 1000+ word article was “write an article on generative AI pricing for SaaS”. Based on more than two decades of pricing strategy experience and extensive research into generative AI best practices, I have some improvements to ChatGPT’s article that follow a verbatim quote. If you would like to speed read this article, skip to my Analysis and Commentary section.
In response to the prompt (in bold above), here’s what ChatGPT had to say:
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Generative AI Pricing for SaaS: Strategies, Models, and Best Practices
Generative AI is revolutionizing the SaaS industry, powering applications that create text, generate images, develop code, and enhance user interactions. However, pricing these services poses unique challenges due to the high computational costs, variable usage patterns, and value that AI brings to end users. This article explores key strategies for pricing generative AI SaaS products, offering insights into different models and best practices that can help businesses balance profitability and customer satisfaction.
The Challenges of Pricing Generative AI SaaS
Generative AI applications are resource-intensive, requiring substantial computing power for real-time processing. These costs must be reflected in pricing models to ensure sustainability. Additionally, users vary widely in how they utilize AI-powered services, from occasional queries to heavy, continuous usage. SaaS providers need to structure pricing to accommodate this variability, making it affordable for small users and scalable for heavy users.
Key Considerations in Generative AI Pricing
Compute and Infrastructure Costs Generative AI models, particularly large language models (LLMs), are computationally demanding. Each query may require multiple processing steps, which translates to higher infrastructure costs. Efficient pricing models balance these backend costs with competitive pricing that meets market expectations.
User Value Perception Generative AI offers distinct value, whether through automating content generation, enhancing productivity, or facilitating customer engagement. Pricing should align with this perceived value—users are often willing to pay more for tools that deliver significant time savings or business impact.
Customer Segmentation Different types of users—from freelancers to enterprises—have varying needs and budgets. By segmenting customers and tailoring pricing plans, SaaS providers can offer relevant, flexible options that cater to a wide range of use cases.
Common Pricing Models for Generative AI SaaS
Generative AI providers use various pricing models to account for differences in user needs, infrastructure costs, and business goals. Here are some of the most effective pricing strategies:
1. Usage-Based Pricing
Overview: This model charges based on the volume of use, often measured in terms of tokens (for language models), requests, or generated characters.
Pros: Highly flexible, allowing users to pay for what they consume. Scales easily with demand.
Cons: Unpredictable costs for customers, particularly if their usage fluctuates. Requires careful tracking and transparency to avoid unexpected bills.
Example: OpenAI charges by tokens processed, with more advanced models like GPT-4 priced higher than smaller models, reflecting the increased computational requirements.
2. Subscription-Based Pricing with Usage Tiers
Overview: Offers a fixed subscription fee for a predetermined level of usage. Each tier includes an allowance of API calls, words, or tokens, with higher tiers providing more capacity.
Pros: Provides predictable monthly or annual costs, which customers appreciate. Allows for tier upgrades as user needs grow.
Cons: Less flexible for light or infrequent users, and can create limitations for heavy users who may exceed their tier limits frequently.
Example: Jasper AI offers monthly packages with varying word limits, allowing businesses to choose the plan that aligns with their content needs.
3. Seat-Based Pricing
Overview: Charges are based on the number of users (seats) within an organization accessing the AI. Seat-based pricing may include pooled or individual usage limits.
Pros: Simple and predictable, especially for team-oriented SaaS products. Aligns well with collaborative use cases in enterprise settings.
Cons: Doesn’t scale with actual usage, so lighter users might find it expensive, while heavy users benefit more.
Example: SaaS tools for teams often adopt this model, with per-user fees plus optional additional charges for usage exceeding the base limits.
4. Outcome-Based or Value-Based Pricing
Overview: Pricing is tied to specific, measurable outcomes like leads generated, content pieces created, or customer resolutions achieved.
Pros: Aligns directly with the value delivered, making it a compelling option for companies with clear ROI expectations.
Cons: Difficult to implement for generalized applications where outcomes are harder to measure.
Example: An AI service for sales or support may charge based on successful conversions or customer resolutions, directly tying cost to impact.
5. Hybrid Pricing Models
Overview: Combines a subscription for basic access with usage-based or outcome-based charges for high-volume use or advanced features. Often includes a base fee with additional fees for premium features.
Pros: Balances predictable costs with scalability, ideal for both steady and high-volume users.
Cons: Can be complex to explain and requires clear communication around how each component impacts the final price.
Example: A platform may have a base subscription for a set number of monthly API calls, with additional charges for exceeding this limit or accessing advanced features like custom models.
6. Freemium and Free Trial Models
Overview: Offers a limited free version or a trial period, allowing users to test features before committing to a paid plan. Freemium versions usually have usage caps or restricted features.
Pros: Allows users to experience the product without an upfront commitment, often boosting conversion rates and user acquisition.
Cons: If too limited, users may not see the full value, leading to lower conversions. High operating costs if free users don’t convert.
Example: OpenAI’s free tier of ChatGPT provides basic access, with paid plans offering access to more advanced models like GPT-4.
Best Practices for Generative AI SaaS Pricing
Optimize Cost-Efficiency Cost-efficiency is crucial, given the computational demands of generative AI. Many SaaS companies optimize by routing low-priority requests to smaller models, which helps keep operational costs down without affecting performance.
Transparent Usage Monitoring Clear dashboards showing real-time usage help customers monitor and manage costs effectively. For usage-based pricing, notifying users as they approach their limits can prevent bill shock and improve satisfaction.
Offer Flexible Plan Upgrades Users appreciate flexible options to scale up or down as their needs change. By allowing easy plan adjustments or add-ons, SaaS companies can retain customers and reduce churn.
Customization Options for Enterprise Customers Enterprises often need advanced features such as dedicated support, fine-tuning, and custom integrations. Offering customizable packages or pricing tiers tailored to large organizations’ specific needs can attract and retain high-value clients.
Provide Freemium or Trial Options A freemium or trial model allows customers to test the product’s value, which can be particularly effective for AI-powered tools with complex functionality. It also helps SaaS providers demonstrate their product’s ROI before requiring a commitment.
Real-World Examples of Generative AI Pricing
OpenAI: OpenAI’s pricing is based on tokens, with costs varying across models, such as GPT-3.5 versus GPT-4. Enterprise customers often receive tailored pricing based on volume.
Jasper: Jasper offers tiered pricing based on word generation limits, with enterprise plans that provide additional features for larger teams.
Copy.ai: Uses a per-user subscription model with word limits, catering to both individual marketers and large marketing teams.
Summary
Setting an effective pricing strategy for generative AI SaaS products requires balancing flexibility with cost coverage. The choice of pricing model—whether usage-based, subscription-based, or outcome-based—should align with the value provided to customers and the operational costs incurred by the SaaS provider. By offering transparent pricing, flexible plans, and options for enterprise customization, generative AI SaaS companies can capture a broad user base and drive long-term growth in this emerging field.
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Analysis and Commentary
That concludes ChatGPT’s response to the prompt “write an article on generative AI pricing for SaaS”. I thought ChatGPT did a good job defining generative AI, highlighting the key challenges in pricing strategy, presenting viable pricing models, listing best practices, giving examples and concluding. Now I’ll add additional commentary and perspective to each section of ChatGPT’s article.
Key Considerations in Generative AI Pricing
ChatGPT listed three key considerations in generative AI pricing: computing costs, user value perception, and customer segmentation. I agree that each of these areas is very important.
Generative AI models are computationally intensive and can require significant GPU (Graphics Processing Unit) resources which are expensive. Customer usage patterns need to be understood to estimate the cost to serve generative AI features. A situation to avoid is where high-usage customers can become unprofitable. These unprofitable situations can be avoided with usage-based pricing, subscription limits, and/or a fair use policy with specific resource limits.
User value and customer segmentation are two areas of importance that can be considered together. Different customer segments can have dramatically different value propositions based on their specific needs. In the pricing process, it’s helpful to specify both the dimensions of value and usage patterns for each important customer segment.
Pricing Models for Generative AI SaaS
I would add two pricing models to ChatGPT’s list:
Add-on Pricing Model An add-on is an optional feature in addition to core software offerings. This is appropriate when a smaller subset of customers would substantially benefit from your generative AI features.
Core Pricing Model Generative AI is simply added to the main product feature set. A core pricing model can be a fit when the vast majority of customers will benefit from generative AI and new customers will also be attracted to the combined offering.
Andreessen Horowitz has an excellent article that investigates add-on and core pricing models for generative AI in B2B SaaS. Ultimately, the selection of an appropriate pricing model depends on how your generative AI features add value, and how value propositions differ across customer segments.
Best Practices for Generative AI SaaS Pricing
Generative AI’s generative AI pricing article lists best practices above. Additional best practices in pricing generative AI are usage modeling and value modeling.
Usage modeling is estimating a range of usage and average usage for each customer segment. This can be calculated using a combination of actual data from beta customers, and modeling with needed assumptions.
Value modeling for each customer segment is an exercise where the expected value to be received by each customer segment is estimated. Economic value in pricing is aptly described in Chapter 2, “Economic Value”, of the foundational book The Strategy and Tactics of Pricing by Thomas Nagle and Georg Müller. Understanding economic value is also an input to ROI calculations as a function of price.
Real-World Examples of Generative AI Pricing
The companies identified as real-world examples in the article are OpenAI, Jasper and Copy.ai. Other instructive examples are Notion and Hubspot:
Notion AI is sold as an add-on to all Notion plans, including free plans, for $10 per member, per month. This gives access to all of Notion’s AI features on a per-seat basis.
Hubspot includes a Blog Post Generator feature in all of its pricing plans. This cool feature is described as: “by giving HubSpot’s AI-driven Content Generator some simple prompts for content, tone, and length, it can produce a blog post for you in minutes. This includes attention-grabbing titles and search-engine-optimized meta descriptions based on the article it has just created.”
Conclusion
Generative AI has the potential to add significant monetized value for SaaS companies. I endeavored to demonstrate the potential of this technology by using it to help write this article. Generative AI differs from typical SaaS features in that high usage can generate significant cost. Understanding the value provided to key customer segments is a critical input to determining a pricing model that works well across your full customer base. In addition, understanding the cost to serve each type of customer is essential to ensure profitable pricing for each segment.
Weighing all of these considerations, synthesizing information, understanding value, mitigating risks and mapping generative AI features into an optimal pricing model is an enormous challenge. You might consider hiring an experienced pricing consultant to help you figure out your generative AI pricing strategy. An advantage to hiring an external consultant over an internal pricing project leader is that the consultant’s top objective and singleness of focus is delivering a successful pricing strategy and implementation. An internal leader can easily get distracted by other priorities resulting in delayed development of your new generative AI pricing.
If you plan to develop generative AI features, there is a distinction between two types. The first type could be called an AI bauble, a nice little value add that works similarly to ChatGPT but is integrated into the software workflow. This is useful because the user can remain in your application and software context to utilize AI. A second more valuable genre of generative AI features can be characterized as fully integrated. Fully integrated AI is a product built from the ground up using large language models (LLMs) and/or machine learning with generative AI at the core of the offering. This second type of generative AI commands a higher price. Often there is a progression for a SaaS company starting with AI baubles then evolving to higher priced fully integrated generative AI.
A final consideration for generative AI feature development is finding product/market fit. Ideally there will be something in your offering that customers love and can’t live without. A feature that is adored and essential is price inelastic and bodes well for both adoption and pricing power.
Generative AI is a new technology, and a dominant design for pricing is in the very beginnings of evolution. I hope the concepts presented in this article have provided some insight helpful in the development and pricing of your generative AI features in the near future.
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