How to price Generative AI products
Hello Readers,
You already learned how to build your data stack that supports AI use cases from scratch in last series. Now it’s time to put our newly minted AI solution to work that actually generates ROI it had promised.
In the rapidly evolving landscape of artificial intelligence, pricing generative AI products is both an art and a science.
As a business leader, you're not just selling a product; you're selling the future.
But how do you put a price tag on tomorrow?
Let's dive into the different type of pricing model suitable for AI applications and which model is suitable for your business.
1. Usage-Based Pricing
What it is: Customers pay based on the amount of AI resources they consume. This could be per API call, per generated output, or per unit of compute time.
This also positioned against subscription fatigue and allows users to only pay for what they use, while also allowing businesses to scale the pricing as the adoption of the product grows.
How to implement:
Define your unit of measurement (e.g., API calls, tokens processed, minutes of compute time).
Determine your cost per unit, including a margin for profit.
Set up a metering system to track usage accurately.
Implement a billing system that can handle variable monthly charges.
Consider offering volume discounts for high-usage customers.
Best for:
AI-powered text generation services (like GPT-4)
Image generation APIs
Voice synthesis engines
Any AI service where usage can vary significantly between customers
Example: OpenAI's GPT-4 uses this model, charging per token for both input and output.
2. Tiered Subscription Model
What it is: Offer different levels of service at fixed monthly or annual rates, with higher tiers providing more features, higher usage limits, or better performance.
If you already have subscription model in place, chances are implementing usage based pricing will be heavy lift.
Instead consider tiered model where customers can “add-on” AI features as they need, creating upsell opportunity for business, while also keeping it separate from core subscription model already proved to generate steady revenue.
How to implement:
Define 3-5 tiers based on customer segments and needs.
Clearly differentiate features or usage limits between tiers.
Price each tier to encourage upgrades (e.g., make the highest tier appealing for power users).
Implement a system for easy upgrades and downgrades.
Consider offering annual subscriptions at a discount.
Best for:
AI-powered design tools
Content creation platforms
AI-enhanced productivity software
Business intelligence tools with AI capabilities
Example: Jasper.ai, an AI writing assistant, uses this model with tiers like Starter, Boss Mode, and Business.
3. Freemium Model
What it is: Offer a basic version of your AI product for free, with premium features or higher usage limits available for a fee.
This works both for usage based as well as subscription model and most widely used to generate traffic and adoption to your AI product.
For usage based model - give away certain number of credits per month just for signing up. Users can use the credits to play around with the product, get used to it and want to buy more credits to keep using.
For subscription model- standard freemium offering by letting users try on new AI features for 7,14 or 30 days and auto renews subscription by end of trial.
How to implement:
Determine which features will be free vs. paid.
If you are allowing users to test all the feature while in freemium mode, consider usage based credit system.
Ensure the free version provides value but leaves users wanting more.
Set usage limits that allow users to experience the product's value.
Create a clear upgrade path with compelling premium features.
Implement analytics to track conversion rates from free to paid.
Best for:
Consumer-focused AI applications
AI-powered educational tools
Personal productivity AI assistants
AI-enhanced social media tools
Example: Replika, an AI companion app, uses this model with a free basic version and a paid Pro version.
4. Outcome-Based Pricing
What it is: Pricing is tied directly to the business outcomes or value created by the AI, such as cost savings, revenue increase, or productivity gains.
Here you are selling the outcome not the product. Most widely used for enterprise solutions where outcome is clearly defined and attributable and reducing the risk for the enterprise customers as they only pay if they achieved the results they were promised.
How to implement:
Identify measurable outcomes that your AI directly influences.
Develop a system to accurately track and attribute these outcomes.
Establish baseline metrics with customers before implementation.
Create a pricing structure that takes a percentage of the value created.
Implement regular reporting to show the value delivered.
Best for:
AI-powered process optimization tools
Sales and marketing AI that directly influences revenue
AI-driven cost reduction solutions
Any B2B AI product with clearly measurable impact
Example: An AI-powered procurement optimization tool might charge a percentage of the cost savings it generates for a company.
5. Per-Seat Licensing
What it is: Customers pay based on the number of users who have access to the AI tool.
Also good for enterprise solutions, which can be easily scaled if you are selling the solution to 10 people company vs 1000 people.
This also allows potential buyer to control who will have access within their organization to access the AI features and better view of the compliance tracking.
You may combine this with usage based to make it more attractive, (more on hybrid pricing later)
How to implement:
Determine your per-seat price based on the value provided to each user.
Implement user management and access control systems.
Consider offering volume discounts for larger numbers of seats.
Decide on billing frequency (monthly, annually, etc.).
Set up systems to easily add or remove seats.
Best for:
AI-enhanced collaboration tools
AI-powered project management software
Enterprise-focused AI assistants
Any AI tool where individual user access needs to be controlled
Example: An AI-powered code generation tool might charge per developer who has access to the system.
6. Hybrid Model
What it is: Combine two or more of the above pricing models to capture value in multiple ways.
Usage based combined with seat based pricing model.
Subscription model combined with usage based add on.
Core tier subscription with add on premium tiers only for certain seats.
You get the point.
How to implement:
Identify which pricing models best align with different aspects of your product's value.
Design a pricing structure that incorporates these models (e.g., base subscription + usage fees).
Ensure your billing system can handle complex pricing calculations.
Clearly communicate the pricing structure to avoid customer confusion.
Analyze customer behavior to optimize the balance between pricing components.
Best for:
Complex AI platforms with multiple use cases
Enterprise AI solutions with varied deployment options
AI products that serve both small businesses and large enterprises
Any AI tool where a single pricing model doesn't capture full value
Example: An AI-powered customer service platform might charge a base subscription fee per agent, plus usage fees for automation features, and outcome-based fees for measurable improvements in customer satisfaction.
7. One-Time Purchase with Ongoing API Access
What it is: Customers make a one-time purchase for the AI software, but ongoing API access or model updates require a subscription.
Perfect for classic data subscription use cases. If data is constantly getting updated or there is need to maintain the infrastructure on customer side, you may need to consider ongoing access pricing model.
This may also scale without initial setup fees if customer is using only the API to train AI models hosted in house. Also unlocks for industries in highly regulated space who can not afford to let their data leave outside their org.
How to implement:
Set a one-time purchase price that covers initial development costs.
Determine subscription fees for ongoing API access or updates.
Implement a licensing system for the initial purchase.
Set up a separate system for managing API access and subscriptions.
Consider offering different tiers of API access or update frequency.
Best for:
AI-powered software tools that can function offline but benefit from online updates
AI models that can be deployed on-premises but require regular refinement
Products where customers value owning the software but also need ongoing improvements
Example: An AI-powered video editing software might be purchased outright, with a subscription required for access to cloud-based features and model updates.
Pricing as a strategic advantage in the AI era
As we've explored, pricing generative AI is not just about setting a number—it's about creating a strategic framework that drives adoption, aligns with value creation, and maximizes long-term revenue.
Let's recap the key strategies we've discussed:
Price the future, not the features: Sell the transformative power of your AI.
Use problem-solved pricing to align with customer value perception.
Consider pricing for ubiquity to drive network effects and data accumulation.
Implement outcome-based pricing to turn uncertainty into a competitive advantage.
Adopt hybrid pricing to capture value at every stage of the customer journey.
Each of these strategies challenges conventional thinking about technology pricing.
But the AI market is anything but conventional.
By thinking creatively about pricing, you can turn it into a powerful competitive advantage.
Remember, in the AI gold rush, it's not just about having the best technology.
It's about having the best business model.
And at the heart of every great business model is a pricing strategy that aligns company success with customer success.
As you implement these strategies, keep in mind that pricing is not a one-time decision.
It's an ongoing process of experimentation, measurement, and refinement.
Continuously gather data on how customers are using your AI and the value they're deriving from it.
Use this data to refine your pricing strategy, always aiming to better align your pricing with the value you create.
In the end, the winners in the AI market won't just be those with the best algorithms.
They'll be the ones who figure out how to price the future—your future.