Estd 4.22.2023
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Unlike traditional software, which is WYSIWYG (what you see is what you get) where value is predictable and deterministic, AI models deliver probabilistic outputs. This makes demonstrating AI value a challenge, as the results are not guaranteed to follow a consistent pattern.
At the same time, the cost of developing these models is substantial. Whether it's the computing power required to train a model or the infrastructure to support ongoing operations, costs remain high and do not diminish over time as they might in traditional SaaS models. For instance, a hypothetical B2C company with 10,000 clients, each generating 3.65 million customer service calls annually, would face annual costs of $550 million if using GPT-4o, due to its high per-call processing costs of $0.0145. In contrast, leveraging an open-source model like Llama 3.1 BB would require an initial investment of approximately $377,000 for customization but stabilize annual costs at $12.36 million, as its per-call cost is only $0.00032. These examples highlight that running AI models at scale costs hard money and that technology design choices as of today are non-trivial. With GPT-4o, the inference cost is $55,000 for one client. Open-source models have a much lower inference cost—around $1,200 per year for one client.
Our Generative AI Pricing Services guide your business through a comprehensive process to design pricing models that resonate with your target customers, align with your cost structures, and remain competitive. From understanding your customer segments to market testing optimized price points, we ensure your pricing strategy drives growth, adoption, and sustainable profitability.
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