Pricing AI products presents a unique set of challenges.
Infrastructure costs can vary significantly across models, workloads, and customers. Usage is often unpredictable, shaped by prompt inputs, model behavior, and user experimentation. Additionally, many of the underlying units (like tokens or inference time) aren’t intuitive for end users.
To address these issues, many AI companies are adopting credit-based pricing models. Rather than charging customers directly for raw usage, they offer a pool of prepaid credits that are consumed as the product is used. This approach creates a buffer between backend cost and customer experience, giving vendors more flexibility and customers more predictability.
This article looks at why credit-based models are particularly well suited to AI products, how teams are applying them, and what to consider when implementing one.
If you’re looking for a more general overview of how credit-based models work, check out our guide for SaaS companies.
Pricing AI products introduces challenges that don’t show up in most SaaS categories. The cost of serving users can swing dramatically based on their behavior, and the units involved aren’t always intuitive or easy to explain. That makes it difficult to design a pricing model that’s fair, predictable, and easy to understand.
Here are a few of the core challenges:
The cost of a single request can change based on which model is used, the length of the prompt, the size of the output, and where the workload runs. Two customers might make the same number of API calls, but generate vastly different costs.
Unlike traditional SaaS metrics like seats or reports, AI usage depends heavily on user input. A single prompt might trigger a cheap, fast inference, or a long, multi-step chain across multiple models. Small changes in behavior can have big cost implications.
Tokens, embeddings, and context windows are useful internally, but don’t translate well for end users. Most customers can’t easily understand what they’re consuming, or how to estimate what they’ll spend.
Trying to expose raw usage metrics often leads to confusion. But hiding them entirely can make pricing feel opaque or arbitrary. Striking the right level of transparency is a challenge, especially when pricing needs to scale across technical and non-technical users.
These issues make it difficult to align pricing with value, control costs, and maintain trust—all at the same time.
Given the challenges of pricing AI products—volatile costs, abstract units, and unpredictable usage, many teams are turning to credit-based models as a way to simplify the experience without giving up flexibility.
Credit-based models introduce an abstraction layer: instead of charging for tokens, seconds, or model calls directly, customers purchase a pool of credits and consume them as they use the product. This structure offers several benefits:
Credits give customers a consistent budget to work within, even if the underlying usage varies. That’s especially useful in AI, where costs can spike due to model complexity, retries, or unexpected prompt behavior. Instead of surfacing every technical detail, teams can communicate value through a simpler unit.
AI platforms often support multiple models or tools (e.g. chat completion, embedding, vector search). A shared credit pool makes it easier to unify billing across these without separate pricing for each one. This not only simplifies internal packaging, it also gives customers more flexibility in how they use the product, without locking them into one specific feature or use case.
With a prepaid credit system, customers decide when to top up based on their needs. Instead of getting billed automatically at the end of a period, they can monitor their usage, plan their spend, and buy more credits on their own schedule. This flexibility can be especially valuable for teams with unpredictable workloads or capped budgets.
Because credits are abstracted, teams can adjust how many credits different actions consume as cost structures or product usage changes. This allows pricing to evolve gradually, without overhauling the pricing model. And when handled well, these changes can happen without breaking customer expectations.
In short, credit-based models give AI teams a way to balance infrastructure realities with customer experience—offering a pricing layer that’s more stable, understandable, and adaptable.
The flexibility of credit-based pricing is especially useful in AI, where usage can span models, workflows, and user types. But to make it work in practice, teams need to be intentional about how credits are allocated, surfaced, and managed across the product.
Here are a few patterns that work particularly well:
Different models have different cost profiles. A small open-weight model might cost 1 credit per call, while a proprietary GPT-style model could cost 10. This gives customers choice and aligns credit burn with infrastructure costs, without surfacing low-level details.
Tip: Use tiered or grouped pricing (e.g. “Standard” vs. “Premium” models) to keep things understandable without exposing every model’s raw cost.
Many AI platforms support more than just inference. You might also offer file processing, embedding generation, vector search, or data storage. Rather than creating separate usage meters for each, you can deduct from a shared credit balance to simplify tracking and billing.
Tip: Keep burn rates proportional to underlying cost, but surface everything through a single, unified meter.
Credits are abstract, but they shouldn’t feel arbitrary. In-product feedback like “this prompt used 12 credits” or “embedding this file will cost 20 credits” helps customers understand what they’re spending and why.
Tip: Show credit usage before and after actions, so customers can estimate cost and reflect on value.
Especially for teams with constrained budgets, giving users the ability to pause usage, set monthly limits, or receive threshold alerts can make credits feel safer and more trustworthy, while reducing support load.
Tip: Make it easy to monitor spend and set alerts directly in the UI or via API.
Credit-based models only work if customers know where they stand. Regularly surfacing the remaining balance, and prompting users to buy more before they run out, helps avoid surprises and keeps usage flowing. Ideally, this happens well before usage is blocked or downgraded.
Tip: Set clear thresholds for when to show low balance warnings, and make reloading credits fast and frictionless.
Credit-based pricing isn’t just a billing model, it’s a system that touches your infrastructure, product logic, and internal tooling. To make it reliable at scale, you’ll need more than just a credit balance in Stripe or a frontend usage meter.
Here’s what AI teams typically need to support it:
You need to measure usage as it happens, not hours later. That means logging every model call, embedding request, or file processed, ideally with enough metadata to understand what consumed credits and why.
Tip: Attach a usage event ID to each request so you can trace credit burns and resolve customer questions quickly.
Burning credits shouldn’t happen in a separate billing process. It needs to be part of your core product logic, especially if you're routing requests across models or applying fallback behavior. If a user runs out of credits, that decision has to be enforced in real time.
Tip: Credit validation should be fully integrated with your system for tracking and enforcing entitlements (feature access).
When a customer runs out of credits, what happens? Do you block usage, downgrade quality, or offer a fallback experience? These decisions need to be enforced in your product layer, and reflected consistently across UI, API, and backend systems.
Tip: Design for both soft and hard limits (e.g. warning tiers vs. absolute stop) to avoid abrupt user experiences.
Support teams need visibility into credit usage to troubleshoot customer issues. Finance needs accurate data for reconciling prepaid revenue, handling expirations, and recognizing revenue correctly over time. If credit usage isn’t tracked and surfaced cleanly, it quickly becomes a liability, for both customer trust and financial compliance.
Tip: Make sure credit usage is easy to track, both for customer support and for finance. You'll need to account for credit expirations, refunds, and usage history—not just for clarity, but also for accurate revenue recognition.
Together, these systems create the foundation that makes credit-based pricing trustworthy—for both your team and your customers.
Schematic is built for this. With native support for usage tracking, credit balances, entitlements, and pricing logic, Schematic gives AI teams the tools they need to manage credit-based models—without building a custom billing system from scratch. Learn more here
Credit-based pricing offers a practical way for AI companies to manage variable usage, abstract technical complexity, and give customers clearer control over what they spend.
When implemented well, it supports a wide range of use cases, across models, workflows, and customer types, while keeping billing predictable and scalable. It also gives teams room to evolve pricing as infrastructure costs or usage patterns change.
They’re not trivial to implement, but for many AI companies, they offer a clear path through one of the hardest parts of building and monetizing AI products: turning usage into revenue in a way that scales.
AI workloads are highly variable—costs depend on factors like model type, prompt length, and output size. Usage is often unpredictable, and customers may not understand the underlying units (like tokens or latency), making it difficult to design pricing that feels fair, transparent, and scalable.
Credit-based models let customers prepay for a pool of credits and burn them down as they use the product. This creates predictable spend for customers, while giving vendors flexibility to manage infrastructure costs and pricing complexity behind the scenes.
Yes. Many AI platforms use a shared credit pool to cover multiple model types, endpoints, or features—like chat, embeddings, file processing, or storage. This simplifies billing and gives customers more flexibility in how they use the product.
Surface credit usage clearly in your product. For example, show “this prompt used 12 credits” or “embedding this file will cost 20 credits.” Clear in-product feedback builds trust and reduces support burden.
That depends on your enforcement model. Some teams block access, others degrade functionality or offer a fallback experience. Ideally, you alert users well in advance and make it easy to top up—via the UI or API.
You’ll need real-time usage tracking, integrated credit logic in your product (not just your billing backend), entitlement enforcement, and internal tooling for support and finance. These systems need to work together to keep credit usage accurate and trustworthy.
Because credits are prepaid and consumed over time, you'll need to account for when they’re earned, expired, or refunded. This is key for accurate revenue recognition, especially as usage scales or crosses accounting periods.
Yes. Schematic is designed to handle usage-based and credit-based pricing models, with built-in support for tracking usage, enforcing entitlements, managing credit balances, and evolving pricing logic as your product grows.
