When SaaS companies launch AI-powered features, they face an immediate monetization challenge: how to price something whose cost is clear but whose value is not yet clear. Most teams solve for speed by adopting cost-plus credit systems, where customers buy credits upfront, consume them as they use features, and vendors apply a margin to cover costs.

‍Why teams default to cost-plus credit models for AI pricing‍

This is usually a bridge solution, instead of a long-term strategy. The drivers are pragmatic:

• ‍Known costs, unknown value.
  Infrastructure spend on GPUs, inference calls, and API requests is measurable. But how much value a customer derives from an AI summary, agent, or workflow is less tangible, especially early in the product’s life.‍
• Volatile usage.
  Large language model (LLM) consumption is highly variable. Customers may run a few lightweight tasks one day and hundreds of heavy jobs the next. Credits normalize this volatility into a predictable prepayment model.‍
• Operational constraints.
  Many teams lack the metering sophistication to track granular usage across multiple AI features so credits serve as a stopgap until telemetry matures.‍
• Feature sprawl.
  As companies experiment with multiple AI features, credits serve as a unifying unit, helping prevent the confusion of introducing a unique price metric for every AI capability.

The head of product monetization at a leading SaaS company explained this tension candidly in a recent conversation with our team:

“We don’t love credits, but we didn’t have time to define outcomes. This was the fastest way to ship.”

In other words: credits are often less of a strategic choice and more of a means to launch quickly in a market where speed is critical.

From the finance side, credits can feel appealing. They map neatly to cost structures and provide better cash flow. But customers often struggle with the abstraction. A GTM lead at a productivity tool company told us bluntly:

“Our finance team likes it. Our customers don’t know what a credit does.”

This mismatch of internal alignment and external confusion is why so many teams see credits as temporary.

‍How cost-plus credit models work‍

At their core, cost-plus credit models are straightforward: customers prepay for credits, which are consumed when they run AI-powered tasks. Whether measured in tokens, minutes of compute, or specific actions, each task burns down the credit balance at a rate set by the seller.

The benefits are real:

• ‍Simplification.
  Customers don’t have to parse complex infrastructure metrics like GPU time or token counts.‍
• Predictability.
  Prepayment ensures that sellers cover their costs and that customers have a spend cap set in advance.‍
• Flexibility.
  Sellers can adjust how different AI features consume credits without renegotiating contracts

But the risks are equally significant:‍

• ‍Opacity.
  Customers often struggle to understand how credits map to real-world outcomes.‍
• Frustration.
  Expiration rules and surprise overages can erode trust.‍
• Inflexibility in scaling.
  As usage grows, companies may find that credits mask important signals about where customers are finding value.

As one director of monetization at an enterprise productivity company told us:

“Credits gave us breathing room while we figured out the real value metric. But they’re not intuitive to buyers.”

The sentiment is consistent across vendors: credits are useful, but not loved.

‍How companies are using credit models today‍

Several companies illustrate the potentials of credit-based pricing:

• ‍OpenAI. Perhaps the most visible example, OpenAI now uses a hybrid pricing model for ChatGPT that combines fixed fees per user (seat or plan) with credits for usages, and credit packs can be purchased to extend usage caps. Core plans come with a base monthly or annual per-seat subscription that gives access to many features, and when users exceed built-in usage limits for premium tools, credit packs can be purchased to unlock additional capacity. Business plans offer per-seat caps on advanced feature use, while Enterprise and Education plans use a shared credit pool without strict per-seat caps. This setup gives predictable baseline costs via seats while allowing flexibility (and credit-based scaling) when demand for premium capabilities rises..‍
• Miro. Known for collaborative whiteboarding, Miro has layered AI capabilities into its platform using credit-based access. Teams get a monthly allowance of AI credits tied to their subscription, which they can spend on tasks such as diagram generation. Extra credit packs are available for heavy users. This model makes AI an opt-in enhancement rather than a baseline cost, but similar to other credit systems, it risks confusing end users who don’t have an intuitive sense of what a “credit” translates to in terms of value delivered.‍
• ServiceNow. A hybrid approach: seat-based pricing provides predictability for enterprises, while usage tiers ensure heavy users pay more. This model gives buyers familiarity while gradually introducing consumption-based pricing.‍
• Vercel and Bolt.new. These developer-focused platforms increasingly tie pricing to activity, whether it be queries, tokens, or compute minutes. By aligning cost with user success, they make credits or usage feel more natural.‍
• Agentforce. Originally priced per conversation, Agentforce shifted to a flexible credit model that applies across multiple types of agent actions. This move improved transparency and scalability, enabling adoption across different AI workloads.

Each of these approaches reflects a common arc: start with credits, refine with hybrid models, and eventually experiment with outcome-based pricing as trust and maturity increase.

‍Why credit models are transitional‍

Credits work best as a temporary architecture instead of as a permanent strategy. They buy time while teams gather usage data, refine telemetry, and learn what customers actually value.

In 2025, most enterprise AI deals still rely on usage-based or hybrid pricing. Pure outcome-based models remain rare, largely because sellers struggle to directly attribute outcomes to the work their tools are doing. This is especially true when it comes to distinguishing what AI is able to accomplish autonomously vs. what still requires any level of human input. But the direction of travel is clear: companies are experimenting with more intuitive metrics. Some of those include:

• ‍Fireflies.ai and Synthesia. Pricing based on minutes of transcription or video generated. These are tangible, customer-friendly metrics.‍
• Decagon. Charging per conversation or per resolution. Customers often prefer this transparency, even if it means higher per-unit costs.

The key is moving toward value drivers that customers can understand, forecast, and feel confident about.

‍Best practices for teams using credit models‍

For teams that adopt credits as a starting point, a few best practices can improve the experience:

• ‍Cost preview: Allow customers to preview the cost for specific AI action before executing.‍
• Usage dashboards. Give customers real-time visibility into credit consumption.‍
• Rollover and alerts. Be clear with unused credits rollover policies, or notify customers before they run out. This builds trust and prevents unpleasant surprise bills or cutoffs.‍
• Predictable overage caps. Customers want assurance that a spike in usage won’t blow up their budget.‍
• Experiment and stay flexible. Test value- or outcome-based pricing models as you learn to see which models customers respond best to. You may find that pricing tied to meaningful outcomes is most well received, so treat credits as a starting point to venture off from. ‍

The path beyond credits‍

Cost-plus credit systems are often born of necessity rather than preference. They solve for speed-to-market and operational simplicity, but they’re rarely loved by customers.

The companies that will win in the next phase of SaaS monetization are those that treat credits as a bridge, instead of as a destination. With the right infrastructure, measurement, and customer alignment, teams can evolve toward pricing strategies that tie spend directly to value, building trust with customers and healthier revenue streams for themselves.

‍How Metronome help you build AI credit and beyond out of the box‍

This is where Metronome comes in. Our platform makes it simple to launch credit models quickly while ensuring they’re sustainable and flexible enough to evolve. From day one, you get the infrastructure to experiment, iterate, and eventually transition toward more value-based models.

With Metronome, you can:‍

• ‍Launch credit-based architectures out of the box, including seat-based credit pools and individual seat credits. ‍
• Offer real-time dashboards so customers always have visibility into usage and spend.‍
• Show cost previews before execution with credit burndown estimates that build trust and transparency.‍
• Offer finely tuned spend alerts at the seat, project, or account level to prevent billing surprises and protect revenue.‍
• Experiment quickly with pricing by adjusting credit allocations, overage rules, or bundles without rebuilding billing logic. 

By providing both the scaffolding for today’s credit models and the infrastructure to evolve beyond them, Metronome ensure credits can be a foundation for sustainable, scalable growth.

If you’re ready to explore how credit models can work for your business, get in touch with our team. We’re here to help you launch and evolve your pricing.