AI Prices Are Dropping Fast: How Can Vendors Keep Prices High?

As the costs of AI technology continue to fall, vendors are grappling with a growing concern: the risk of commoditisation. DeepSeek AI, for example, has successfully reduced its prices due to lower operational costs, a trend that many AI vendors fear will push their prices down and threaten profitability. According to James Wilton, one of the world’s leading experts on SaaS pricing, while AI is often credited with “ripping up the SaaS pricing rulebook”, many vendors are overlooking the fundamental principles that maintain higher prices in a competitive market.

James, Managing Partner and Founder of Monevate, a premier pricing and monetisation consulting firm, offers his insights on how AI vendors can navigate the evolving pricing landscape. He is also the author of Capturing Value: The Definitive Guide to Transforming SaaS Pricing and Unshackling Growth, and has previously led the pricing service line for McKinsey & Company’s practice for startups and scaleups.

Here, James outlines five key strategies AI vendors can adopt to overcome the pricing challenge:

1. Pick a Value-Aligned Metric

Many AI vendors continue to price based on users or input usage, which rarely correlates with the actual value provided. “No wonder buyers push back on buying more units,” says James. Pure outcome-based pricing can be difficult to implement, but vendors can use outcome-oriented usage metrics to offer the benefits of outcome-based pricing without the operational complexity. These metrics help ensure customers only pay for what they value.

2. Give Customers Pricing Predictability

According to Monevate’s research, enterprises are willing to pay 10-15% more for services that offer clear pricing forecasts. “Customers will pay more for predictability,” James confirms. Even with variable usage metrics, AI vendors can offer price predictability through volume commitments, usage tiers, and caps. This approach provides customers with short-term price stability while still enabling the vendor to monetise long-term growth.

3. Differentiate on What Matters, and Communicate It

Differentiation is the best defence against commoditisation. AI buyers prioritise aspects such as security, accuracy, and reliability. “Being better on important attributes—or even just ‘different’—is often enough to charge a premium,” James explains. Unfortunately, many AI providers fail to effectively communicate these differentiators, leading to a lack of perceived value in the eyes of their customers. If an AI product looks indistinguishable from others in the market, vendors will likely have to compete on price alone.

4. Monetise Premium Elements

While feature-tiering may not be as popular as it once was, James believes it can still be an effective strategy. By offering premium features—such as faster processing speeds, enterprise-grade security, and advanced analytics—in higher-priced tiers, AI vendors can shift the conversation from cost to value. “Many AI buyers will pay more for these ‘premium’ features,” he adds. This approach helps defend higher prices by making premium options desirable.

5. If All Else Fails, “Muddy the Waters”

When customers focus solely on price, they tend to compare vendors head-to-head, driving prices lower. To counteract this, James advises vendors to structure their pricing in a way that makes direct comparisons more difficult. “Pick a different pricing metric. Tier differently. Change included volumes,” he suggests. By making their pricing structure unique, vendors can prevent price comparisons and set themselves apart in a crowded market.

In conclusion, as AI vendors face the challenge of falling prices and increasing competition, it is crucial to stay focused on delivering value and differentiating offerings. By aligning pricing with the value provided, ensuring predictability, and strategically tiering features, vendors can continue to thrive without resorting to price cuts that erode their profitability.

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