Outcome as a service: How to price your AI product
Define Metrics and Non-Metrics!
In the rapidly evolving world of artificial intelligence, startups face a unique challenge when it comes to pricing their products.
Traditional pricing models often fall short in capturing the true value of AI solutions, especially for outcome-as-a-service (OaaS) products - and on the other hand, you don't want to invent new ways of pricing and in the process, confuse the customers.
A look at AI startups website and you won't find any pricing details (they all ask to 'get into a demo') as I believe most of them are still iterating on pricing.
What exactly is Outcome-as-a-Service (OaaS)
Before diving into pricing strategies, it's crucial to understand what OaaS means in the context of AI products.
OaaS is a business model where customers pay based on the outcomes or results delivered by the AI solution, rather than for the technology itself or the time spent using it.
Key factors to consider in AI product pricing
- Value Delivered: The most critical factor in pricing an AI product is the value it delivers to customers. This is especially true for OaaS models, where the focus is on outcomes.
- Cost Structure: Understanding your costs, including development, infrastructure, and ongoing maintenance, is essential for setting a price that ensures profitability.
- Market Dynamics: Research your competitors and understand the market's willingness to pay for similar solutions.
- Scalability: Consider how your pricing model will scale as your customer base grows and your product evolves.
- Customer Segment: Different customer segments may derive different levels of value from your product, which could influence your pricing strategy.
