What is credit-based pricing?

Pricing a product is hard. You need to protect your margins, stay competitive, and still make the product appealing to customers. When sales are slow, the instinct is often to assume that the price is too high. But in most cases, the real issue lies in the pricing model and whether it reflects the true value your product delivers.

With the rise of AI, the cost structure for many companies has changed significantly. It’s no longer just about storage or bandwidth. Running AI workloads often involves expensive infrastructure like large language models (LLMs), GPU time, memory-optimized instances, vector databases, and model inference APIs, among others. These are not fixed or predictable costs, and they tend to scale differently based on what you’re building and how your customers are using your product. As a result, many AI companies have adopted usage-based pricing. Today, close to 85% of AI and SaaS companies have adopted some form of usage-based billing.

However, no pricing model is perfect. While usage-based pricing helps align revenue with actual consumption, it introduces a major challenge which is unpredictability. A customer’s workload can vary significantly from month to month, leading to billing surprises. At the same time, companies struggle to forecast revenue and manage their infrastructure margins.

Another issue is that usage is often measured using a single metric like tokens or API calls, which doesn’t reflect the true cost or value of the action. Two actions might consume the same number of tokens but have very different underlying infrastructure requirements and business impact.

For example, Customer A might be generating high-quality sales leads using complex sentiment analysis models that require intense GPU processing. Meanwhile, Customer B could be extracting basic metadata from emails which is a far less demanding task with minimal compute requirements. Even though both customers use the same number of tokens, the actual infrastructure cost, latency needs, and business value are completely different.

To address these challenges, many companies have started exploring credit-based pricing. In this model, customers purchase a set number of credits upfront, either as part of a subscription or through one-time payments. Each action within the product is mapped to a fixed credit cost. These costs are not necessarily tied to raw usage metrics like tokens or API calls, but instead are designed to reflect the true underlying infrastructure cost, the complexity of the task, and the value it provides to the end user.

Advantages of credit-based pricing:

1. Transparency for customers: If the product makes it clear that generating a sales prospect costs 10 credits while extracting email metadata costs 2 credits, the customer gains a clearer understanding of what they are paying for and can make more informed decisions about how to use the product.
   
   

2. Margin protection for companies: By assigning credits based on actual cost and business value, companies can ensure that high-cost or high-impact features are priced appropriately without having to expose their infrastructure or financial models.
   
   

3. Predictability for both company & customers: Customers can purchase credits in bulk and consume them over time, much like prepaid mobile data or cloud usage credits. This reduces billing surprises and gives finance teams more consistent usage patterns to model against.

Disadvantages of credit-based pricing

1. Learning curve for customers: Credits are not always intuitive. New customers may struggle to understand how credit allocation works or how quickly credits will be consumed. Without clear in-product guidance, this can create friction and hesitation during onboarding.
   
   

2. High calibration effort: Every action in the product needs a well-thought-out credit cost. Assigning values that reflect infrastructure usage, perceived value, and customer expectations requires careful iteration. Getting it wrong can hurt margins or lead to perceived unfairness.
   
   

3. Harder to forecast revenue: When customers purchase large volumes of credits without using them predictably, it becomes difficult to map credit consumption to monthly recurring revenue (MRR). Rollovers, expiration windows, and top-ups all add complexity to financial modeling.
   
   

4. Managing credit validity windows is tricky: Defining how long credits are valid for and what happens when they expire can be a source of confusion and support overhead. Allowing indefinite rollovers may give customers flexibility but makes your revenue less predictable.

Credit-based pricing has gained visibility in recent years, especially among AI infrastructure platforms, API-first products, and developer tools. However, despite its advantages, widespread adoption is still limited.

Based on research from industry reports, less than 15% of companies have implemented a full-fledged credit-based pricing system. Among AI and infra-heavy companies, particularly those offering APIs or pay-per-use features, the number is slightly higher, estimated between 20% to 25%.

Why hasn’t credit-based pricing seen broader adoption

One reason is that the implementation complexity is high. Companies need to build or integrate systems for real-time usage tracking, credit accounting, customer-facing dashboards, and internal reporting. This often means writing custom logic, setting up streaming pipelines, maintaining a credit ledger with rollback and audit support, and designing UI components to surface credit usage. For early-stage teams, this is a heavy lift.

Another challenge is around communication and comprehension. While credits provide a flexible abstraction layer, they are also unfamiliar to many end users. If not explained clearly, credit-based pricing can feel arbitrary or opaque. Customers may ask questions like: What exactly am I buying? How long will it last? What happens if I run out?

Even internally, credit-based pricing introduces friction. Product teams need to assign rational credit values across a wide range of features. Finance teams must figure out how to map credit usage back to revenue. GTM teams need to learn how to sell and support a pricing model that can’t be boiled down to a simple “per-user, per-month” number.

As a result, many companies opt for partial or hybrid approaches:

• They offer usage packs (e.g., 10,000 API calls for $99) without calling them “credits.”

• They use credits only in specific parts of the product, such as pay-as-you-go plans, while keeping enterprise accounts on custom pricing.

• Some companies design their plans around bundled quotas for specific features, but don’t unify usage into a shared credit pool.

These models are easier to implement, but they give up some of the flexibility and clarity that a well-designed credit system can provide.

Still, credit-based pricing is gaining traction. The shift toward modular products, API-based consumption, and AI-powered features is driving more teams to rethink how they align cost, value, and pricing. In communities like Hacker News and Reddit, conversations around billing often surface the same themes: unpredictability, lack of alignment between pricing and value, and the desire for more control.

As products become more dynamic, the need for flexible, cost-aware pricing infrastructure will only increase. And credit-based pricing, despite its challenges, remains one of the most promising paths forward.