Pricing Models for AI Products

Pricing and monetization of software is always tricky. It requires a thoughtful approach reflecting the technology’s potential and the tangible value it delivers. As AI disrupts industries, its pricing models must evolve beyond traditional methods, aligning to the new ways customers interact with and derive value from AI tools. This article explores how AI software is monetized through innovative value metrics, pricing models, and various strategies that reflect modern market trends.

Understanding Value Metrics in AI Software

Value metrics define how a customer experiences the value of your product. In traditional software, metrics might include the number of users, usage, credits, licenses, etc. but for AI software, the emphasis shifts to measurable outcomes like tasks completed, problems solved, or efficiency improved. AI is unique in its ability to automate complex tasks, meaning companies must align their pricing with the actual value delivered to the customer.

Many AI companies today still operate, price, and monetize in the hangover of the SaaS era. Not all models are wrong, but may underprice and leave money on the table. Let us examine some models prevalent today.

Subscription Models

Many AI software companies still use subscription-based models, especially for general-purpose AI tools. However, these models don’t always capture the true value the AI delivers. Subscription pricing may work for simpler tools or where AI usage remains steady.

For example, DeepL charges a per-user fee with added cost for editable file translation, layering value on top of basic functionality. This model can work well for companies that want predictable revenue and have achieved predictability in both frequency and usage of the software.

Usage-Based Pricing Models

Usage-based pricing is the most prevalent in AI, offering a flexible alternative to fixed subscription pricing. Customers only pay for what they use, making it appealing for businesses with fluctuating workloads.

A few examples are:

• Intercom's FinAI copilot gives agents 10 free tickets per month and charges after that, letting companies scale their usage as needed.
• Zapier charges per task automated, meaning customers pay based on how much work the AI does, not just access to the tool.

Task-Based and Outcome-Based Pricing

Task-based pricing charges customers per task completed, rather than per-user or per-subscription. This is particularly useful for AI systems that perform discrete actions or tasks, where each task has a clear, measurable outcome.

A few examples:

• 11x charges per task completed by the AI Sales Development Representative (SDR), ensuring companies only pay for results.
• Synthesia charges per minute of AI-generated video, which aligns pricing with the amount of content produced.

Outcome-based pricing, while appealing, remains challenging to implement due to the difficulty in quantifying the exact business outcome attributed to AI. Some companies, like Chargeflow, have attempted outcome-based pricing by charging 25% per successful chargeback, ensuring they get paid based on results. However, this model can cause customers to rethink the value when reaping the larger rewards as AI transforms more of their value chain.

Credits and Token-Based Pricing

Credits are nothing new, they are a simple manifestation of future usage converted into cashflow quickly. Credits or token pricing gives customers flexibility and control over how much AI they use. They buy a certain number of credits and spend them based on the AI actions they need.

A few examples:

• Clay charges per credit, where one credit equals a data point or action processed by the AI.
• Bardeen AI and Captions use a credit system, charging per automation or video generation credit, offering flexibility and clear cost transparency.

The system is effective because customers know exactly what they are paying for and can scale usage without committing to large, upfront costs. It is also good for the software company as they can realize cashflow even against future usage.

Trends in AI Monetization

The Shift Toward Units of Work

The most disruptive trend in AI pricing is the shift toward selling "units of work" completed by the AI. The model ties directly into the value customers receive and moves away from traditional SaaS pricing. Companies are now paying for output, not just the access or ability to use a tool.

A few examples:

• Salesforce charging $2 per conversation for its Agentforce product.
• Zendesk AI charging per successful autonomous resolution.

This shift mirrors broader trends in AI software monetization, where value is tied directly to the results produced, not just buy and access.

Hybrid Pricing Models

Some AI companies are experimenting with hybrid pricing models, combining subscription fees with usage-based or outcome-based pricing. This lets businesses have predictable base costs while scaling usage and payment according to results.

For example, Kittl offers a high watermark of AI credits per day, enabling customers to predict costs while enjoying the flexibility of additional credits for higher workloads.

The above figure measures the complexity of the monetization model against the customer value delivered. Businesses can determine if they are using overly complex models that don’t correlate to customer value, or if they’re underpricing high-value AI tools.

Barriers to Adoption of Outcome-Based Pricing

While outcome-based pricing holds enormous potential, it also presents unique challenges. Despite the clear advantages, true outcome-based pricing remains rare because of the difficulty in measuring outcomes consistently and aligning them with AI pricing. AI that affects long-term business results, such as improving customer satisfaction or boosting sales, may not yield results that can be easily quantified and priced in the short term.

However, as AI technologies and data analytics improve, outcome-based pricing will probably become more feasible. Offering more transparency and accountability lets companies align their pricing with the actual business outcomes from AI, delivering a win-win!

Conclusion

Monetizing AI software requires careful consideration of value metrics, pricing metrics, and choosing the right monetization model. As AI technology continues to disrupt industries, its pricing models will also evolve, focusing more on tasks completed and outcomes delivered, rather than traditional access-based pricing. Aligning pricing with value will let companies better reflect the true worth of AI software and drive sustainable growth.