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Top 5 Usage-Based Pricing ChallengesThe Road to Adopt Usage-Based Pricing Successfully

Peter Marton
Peter Marton@slashdotpeter

Usage-based pricing is increasingly recognized as the go-to model for AI products. It perfectly aligns pricing with customer value, facilitating organic growth and keeping costs in margins—a critical combination for hyper-growth AI startups that rely on costly LLMs and GPUs. In our journey at OpenMeter to empower the next wave of AI companies with monetization strategies, we've identified that many teams primarily focus on the billing aspect, underestimating the broader challenges of adopting a usage-based pricing model.

In this article, we'll share the top five challenges we've observed when adopting usage-based pricing:

1. Billing after Consumption

Metering and billing are the most recognized challenges in adopting usage-based pricing models. Billing customers after consumption requires the extraction, aggregation, and delivery of usage data across various systems to the billing platform for invoice calculation. To support business growth, this metering infrastructure must be accurate, scalable, and efficient. Metering must also happen close to real-time to enforce limits and communicate usage to customers in time. You need to calculate the invoice to recognize revenue, which can involve complex pricing models like volume and tier-based pricing.

In our previous article, you can learn more about metering challenges.

2. Implementing Usage Limits and Entitlements

Defining what customers receive as part of their subscription through entitlements creates new challenges with usage-based pricing. While static entitlements, such as access to specific features, are straightforward to implement, dynamic entitlements, like usage limits and quotas, require real-time metering and low latency enforcement. These challenges often lead to a mismatch between advertised and enforced limits.

Not enforcing usage limits may be acceptable for traditional high-margin SaaS but is a real challenge for the new generation of AI companies, where limits are in place to protect expensive LLMs and GPUs. As a recent example shows, even well-funded companies like OpenAI switched to prepaid billing to better control costs.

3. Communicating Consumption to Customers

In the era of AI, APIs, and automation, it is crucial to communicate consumption to customers. A tight feedback loop between customers interacting with your product and the usage reflected on their usage dashboard is not just important; it's a responsibility. Offering threshold notifications and forecasting can further improve the experience. This proactive communication is vital for controlling spending and ensuring customer satisfaction. As a recent example, Vercel implemented soft and hard limits to help customers better manage their spending, demonstrating their commitment to responsible consumption communication.

We recommend that companies adopting usage-based pricing pay attention to communicating consumption to their users. The lack of instant usage feedback can lead to unexpected customer costs, overspending, and, eventually, churn.

4. Maximizing Sales with Usage Insights

Leveraging consumption data offers a golden opportunity for revenue teams. It signals when a customer is ready for expansion, indicating periods of hypergrowth or potential churn. Usage data can also help in quoting new deals. By analyzing usage patterns and surfacing opportunities in CRM directly, sales teams gain a new perspective, aligning their efforts with customer value realization—a strategy early adopted by companies like Confluent to streamline their go-to-market operations.

We recommend that revenue organizations use surface consumption data and insights directly in CRM so sales teams can work from it without juggling multiple systems.

5. Understanding Cost and Margins

As the shift towards usage-based pricing accelerates, companies increasingly pay vendors based on consumption. The variability in user consumption patterns can result in significant cost-per-user discrepancies. Understanding these costs at the per-user and per-feature level is crucial to maintaining healthy margins and strategically pricing products. This is especially important for AI companies that are building on top of costly resources.


Adopting a usage-based pricing model helps align pricing with customer value, facilitating organic growth and keeping costs within margins. However, the transition comes with new challenges, such as accurate metering, complex billing models, communicating usage to customers, surfacing opportunities for sales, and managing costs. Adopting usage-based pricing spans the entire company, from engineering, product, and sales to customer success. Thinking early on about what each team needs to succeed in this new model ensures success and strong go-to-market motion.

Explore our use cases to gain deeper insights into managing a usage-based company efficiently.

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