When should AI companies think about their pricing?

13 Mins Read
Smuruthi Kesavan
Published On : 12/07/2023

Innovation and technological prowess frequently take front stage in the fast-paced area of Artificial Intelligence (AI). Aside from cutting-edge technology and complex algorithms, the pricing strategy of an AI company is critical to its success. The price that an AI firm sets for its product or service has a significant impact on its market position, client acquisition, revenue, and long-term growth.

The interplay of pricing strategy with the business model and the larger marketplace is what makes it so exciting. It's about more than just determining how much customers will pay; it's about the value an AI solution provides and how it's viewed in the market.

Price is not merely a financial decision, but also a strategic one that includes marketing, sales, and customer service.

Despite its importance, pricing is an aspect of corporate strategy that is overlooked. This delay is mostly the result of a misunderstanding that price should come after product development, market penetration, and user growth. However, such a delay can occasionally result in a misalignment between the product's perceived and actual value, affecting the company's growth trajectory.

When a new AI product or service is conceptualized, alongside the discussions of development, marketing, and sales strategies, pricing should also hold a seat at the table.

The reason is straightforward - an effective pricing strategy provides a clear direction for revenue growth and a framework to scale the business. Furthermore, it helps position the AI offering accurately in the competitive landscape, reinforcing its value proposition, and attracting the right customers.

So, the first and foremost question that every AI company should address at the earliest stage of their business is - What should be our pricing strategy? To answer this question, we must delve deeper into the intricacies of pricing models and their suitability for AI companies.

Want to see why you should change your pricing strategy? Read more to find out how much your AI infrastructure and maintenance costs can affect your profitability here.

In the subsequent sections, we will explore why traditional SaaS pricing models might not be the best fit for AI businesses and why a usage-based or a hybrid approach could offer a more promising route.

Why Traditional SaaS Pricing Doesn’t Work for AI

The common seat-based pricing model employed by many Software as a Service (SaaS) companies fails to deliver the desired results for AI companies. This is primarily due to the fundamental differences in the value propositions of AI and traditional SaaS products.

  1. Seat-based pricing is less viable: SaaS products are generally straightforward, allowing customers to evaluate the utility based on the number of users. In contrast, AI services often provide a cumulative benefit, making it challenging to attribute value to individual users.
  2. The nature of usage differs: AI solutions are typically integrated into larger systems, making the number of individual users a less relevant metric for deriving value.

To illustrate this, consider the example of Salesforce, a SaaS company. Their pricing is tied to the number of users, and it works because the value derived from their service is directly correlated with the number of users.

However, an AI company like OpenAI generates value not from the number of users, but from how they use their AI models, making seat-based pricing unsuitable.

Embracing Usage-based Pricing

As AI companies grapple with the challenge of pricing, one model emerges as a front-runner: usage-based pricing. This strategy diverges from traditional seat-based pricing, favoring a more customer-centric approach.

With usage-based pricing, customers pay for the actual consumption of a service, rather than the number of users. But why is this model gaining so much traction in the AI industry? Let's delve deeper to understand its allure.

1. Aligned with Value Creation

The primary strength of usage-based pricing lies in its ability to align the value proposition of an AI service directly with its pricing. This model is inherently fair, ensuring customers feel they’re getting their money’s worth, and are more likely to continue using the service.

Companies can price their services based on the value they provide, eliminating the friction that often comes with per-seat pricing models, especially when user-based metrics don't correlate with the value provided.

2. Encourages Innovation and Experimentation

Usage-based pricing also encourages customers to experiment and innovate with the services on offer. It allows for flexibility and scalability, making it a particularly attractive option for businesses at different stages of growth.

Startups with limited budgets can start small, only paying for what they use. As their needs and usage grow, they can scale up without worrying about paying for unused seats or services. This model also caters well to larger enterprises that have variable demands, giving them the flexibility to adjust usage based on their needs.

For enterprises, they can start trying out new features and using them before actually buying or signing up for them. This will help them guage how the tool will actually help them inside their techstack.

3. Success Stories in the Real World

Twilio, a cloud communication platform that has successfully adopted the usage-based pricing model. Twilio's customers pay for what they use, be it voice, messaging, video, or other communication services.

This pricing model has resulted in Twilio's astounding growth because it aligns directly with the value customers receive. The flexibility offered encourages businesses to experiment with Twilio's services without having to worry about fixed, high-cost commitments.

Since AI is a new tech that needs more adoption and trials, usage based can be a nudge for potential customers/prospects to try it out.

4. A Sustainable Model for Growth

For AI companies, the adoption of usage-based pricing can drive sustainable growth. This model allows for predictable revenue that scales with customer usage, and it can help align the growth of the AI company with the success of its customers.

Additionally, this model is often favored by investors, as it reflects real value delivery and can lead to more predictable and recurring revenue streams.

When should companies think about hybrid pricing?

In some cases, the most effective strategy might be a hybrid pricing model, which combines elements of both seat-based and usage-based pricing. This can accommodate the unique characteristics of AI services, where some elements may align with the seat-based model, and others with the usage-based model.

Let's take a more in-depth look at how this strategy works and how it's applied in the real world of AI.

1. The Rationale Behind Hybrid Pricing

Hybrid pricing models aim to maximize the advantages of both seat-based and usage-based pricing. This approach allows AI companies to capture value from individual users while also monetizing the overall usage of their service.

By customizing pricing to match the unique attributes of their service, these companies can better align the value perceived by customers with the prices they charge.

2. Real-world Examples of AI Companies Using Hybrid Models

One AI company employing a hybrid pricing model is Snowflake, a cloud-based data warehousing company. Although Snowflake is not strictly an AI company, its pricing strategy offers valuable lessons for AI-based businesses.

Snowflake uses a hybrid model where customers pay a base cost for storage (which is akin to seat-based pricing), along with a consumption cost for compute resources used (akin to usage-based pricing). This hybrid approach enables Snowflake to capture the value provided by both their storage and computing capabilities, appealing to a wide range of customers with varying needs.

Another AI company utilizing a hybrid approach is C3.ai, a leading provider of enterprise AI software. Their pricing model incorporates both a subscription fee, similar to seat-based pricing, and additional charges based on the amount of data processed and the complexity of AI computations, which aligns with usage-based pricing.

This model ensures customers pay for the true value they receive, encouraging them to maximize their use of C3.ai's services.

3. Creating a Customized Pricing Solution

The hybrid pricing model's strength lies in its flexibility and its ability to be tailored to a specific product offering. AI companies should consider the different facets of their product when crafting their pricing strategy.

For example, if an AI company's product includes both a software platform and AI processing, a hybrid model could allow them to capture the unique value provided by each component.

Conclusion

Choosing the right pricing strategy is a pivotal step in a company’s monetization roadmap, especially for AI companies. The dynamics of AI offerings don't necessarily align with traditional SaaS pricing models, necessitating innovative approaches like usage-based or hybrid pricing models. The emergence of advanced billing platforms like Togai has revolutionized the process of launching and implementing diverse pricing models, making it easier and more cost-effective than ever before. In deciding when to establish a pricing strategy, the answer is clear: the sooner, the better. As AI companies grow and evolve, their pricing strategies should adapt to reflect the value they offer, positioning them for sustained success.

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WRITTEN BY
Smuruthi Kesavan
Product and Content Marketing at Togai
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