The Price is AI-ght? A Short Discussion on Pricing Models for AI Startups

In the ever-changing world of software pricing, the rise of AI agent applications could lead to a significant change in how companies determine the value of their products. To grasp this potential shift, it's insightful to examine the history and recent trends in software pricing models,  particularly focusing on seat-based pricing and consumption-based pricing.

Salesforce, for example, has famously employed a seat-based pricing model. This model is characterized by charging per user, with pricing tiers offering varying levels of functionality, starting with around $25 per user per month with incremental creeping up to their Lighting Unlimited package of $325 per user per month​. 

In contrast, consumption-based pricing, as used by companies like Snowflake and Databricks, charges based on the amount of resources or services consumed by the user. This model aligns well with data and cloud computing platforms where the usage can vary significantly across customers.

The introduction of AI agent applications adds a new dimension to the pricing discussion. Agents’ unique value proposition, coupled with diverse applications across various business functions, significantly complicates pricing strategy. Companies must consider the value delivered by the AI, the costs associated with its operation, and customer preferences. So, if you are an AI startup, how should you address these factors and determine pricing? 

In the following, I try to explore three models around pricing for the next generation AI agent companies, analyzing the potential benefits and drawbacks of each approach. 

AI Copilot ➡️ Seat-Based Pricing

If you are building a AI Copilot company, a seat-based pricing model may be a good fit. This approach aligns with the product’s aim to supercharge an individual's performance. Since a copilot product is intended to enhance the productivity and capabilities of specific roles within an organization, the pricing should be linked to each individual user. An obvious example of this is Github Copilot, which charges $10 per user per month and can go up to $39 per user for enterprise features. 

The per-user pricing of GitHub Copilot aligns with the direct benefit it provides to each individual developer’s productivity, and scales directly with an organization’s user base of engineers. The advantages of this pricing model is clear: it encourages users to use as much of the technology as possible, which has downstream benefits of increased user data for product iteration and virality within an organization. However, with essentially unlimited usage, the provider of the product faces less control over its cost structure. Theoretically, the product could continuously call upon the underlying foundation model, leading to potentially unbounded costs. A possible workaround for this would be implementing rate limits on usage, though it might have an impact on the perception of the product by customers. 

AI Agents ➡️ Usage-Based Pricing

Usage-based pricing in AI startups is a trend that aligns closely with the actual consumption of services, offering a more flexible and fair approach to customers. This model is particularly relevant in the field of AI Agents, where the computational demands can vary significantly based on the application and user engagement. By adopting this model, AI startups are able to offer their services in a way that scales with the customer's actual usage, ensuring a direct correlation between cost and value received.

One notable example is Intercom's AI chatbot Fin, introduced in March. Fin represents a shift in AI service pricing, charging customers 99 cents for each customer request it successfully resolves. This pricing model represents a notable departure from traditional flat-rate or subscription models, aligning more closely with consumption-based strategies similar to those employed by  cloud computing platforms such as Snowflake and Databricks. In these platforms, costs are calculated based on the computational resources used – typically measured in credits – and consumed according to data volume or task complexity. Similarly, Intercom's Fin directly ties the cost to the service's effectiveness, charging per resolved request, thereby aligning the cost with the value delivered.

Usage-based pricing models come with distinct advantages. The companies offering the product benefit from predictable costs as charges are tied to each use of the service, making it straightforward to understand the direct cost of calling the foundation model with each use. This transparency and direct correlation to usage make the model appealing to customers, as they only pay for what they actually use.

However, there are challenges to this model as well. For startups, revenue forecasting becomes more complex and less predictable, as income is directly tied to fluctuating customer usage patterns. Additionally, customers might seek workarounds to minimize their use of credits, potentially leading to underutilization of the service. This can discourage full usage of the AI capabilities, as customers may be motivated to conserve credits to save costs.

Autonomous AI Service ➡️ Capacity-Based Pricing

As we enter the emerging world of AI agents and Service as Software applications, I have had a few interesting conversations around a potentially new developing pricing model, one that is closely linked to the cost savings in human labor. This model, which I’ll call “capacity” or “staff augmentation-based pricing”, aligns the value of AI solutions with the labor costs they offset or replace.

For instance, consider a traditional managed service provider who charges based on the staffing needs they fulfill. A similar approach can be applied to AI agents, where the cost of the AI solution can be benchmarked against the full cost of a human employee performing the same task. This includes not just the salary, but also additional expenses like healthcare benefits. If an AI agent can perform the work of a human IT analyst, who, for example, handles 5,000 IT tickets a year, the cost savings become evident. The AI’s pricing could then be set in proportion to the cost of the human labor it replaces.

This pricing model becomes particularly relevant in two scenarios: One is in areas where labor shortages exist and the availability of human resources is just plain limited. The other is a cost discussion for the customer. Organizations might find adopting AI solutions very appealing when the cost of these AI agents is significantly lower than hiring additional staff – potentially at a ratio of 4:1 or 3:1, meaning the AI is four times cheaper than the human alternative.

By tying the value of AI directly to labor cost savings, this model offers a pragmatic approach for organizations to evaluate and adopt AI solutions, making them a viable alternative. 

Conclusion

For AI startups navigating this landscape, the chosen pricing model should reflect the value delivered by their product. It should be simple, easy to understand, and allow for straightforward forecasting. This approach is vital in the AI-driven software domain, where the value proposition can significantly vary based on the application and its impact on business processes. 

Special thank you to Matt Peters, Peter Silberman, Surag Patel, Edward Wu and Scott Gudmundson for providing incredibly valuable input and helping me think through some of these concepts.