10 Questions to Answer Before Launching a Credit Model

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Each week, we break down real pricing, packaging, and product moves from SaaS and AI leaders and share the ideas worth stealing.

Credit models remain the most common topic we’re asked about at PricingSaaS (and in Pulse Agent). This week, we're breaking down 10 questions to answer before actually launching a credit model yourself.

If you’re actively working on credits, we’re hosting an Office Hours session next week with two credit model experts. Register here.

Let’s get to it.

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Credits are eating SaaS pricing. When executed well, they deliver the flexibility of usage-based pricing with the predictability of a subscription.

But launching credits isn't one decision. It's at least ten. Most teams answer two or three of them, ship, and then spend the next year cleaning up the rest in public.

If you're considering a credit model, here are the ten questions to answer before you launch — with examples from leading SaaS and AI companies.

Customers will do this math whether you help them or not — so help them. The moment someone can't figure out what they're actually paying per unit of work, trust erodes. Notion makes this easy with a clear conversion of $10 per 1,000 credits.

We’ve seen the full spectrum of rate cards, and they range from completely obfuscated to exhaustive. Figma is somewhere in between. I really like how they offer a range for several of the features on the list below (I wish they stuck to it for Make Images and Edit Images as well).

Figma also includes a disclaimer at the bottom that highlights the last date the table was updated. This gives them space if underlying costs change (more on that below).

If you have multiple products, decide early: one universal currency or per-product pools? ElevenLabs offers 3 products: ElevenCreative, ElevenAgents, and ElevenAPI, each of which draws from a unified credit pool. While a universal currency simplifies expansion, it should be paired with clear documentation and a robust rate card.

Most companies are adding credits to their existing plans, not swapping their existing model for credits. Descript is a great example. They have a seat-based model, with specific AI credit allocations per plan. They grant a one-time allotment for free users, with escalating limits for Hobby, Creator, and Business plans. They also use Credit Top Ups as an upgrade feature for Creator and Business users.

The moment of credit exhaustion can be highly contentious. Do customers hit a paywall? Get billed automatically? Buy a pack? Ideally, customers are given optionality, and aren’t automatically upgraded to a new tier they never agreed to.

One solid example here is Krea, which sells one-time compute packs — applied instantly, valid for 90 days — which keeps the top-up feeling like a natural continuation, not a penalty. (Note: It’s okay if the top up rate comes at a premium, but I’d be cautious about making that premium too high).

The best credit models make upgrading feel like the obvious next step, not a shakedown. Lovable shows how it's done — an upgrade UI that meets you at the moment you need more, with dropdown options and clear pricing for each tier of added credits.

From our data, most companies do not offer rollovers when they initially launch credits, but the seasoned credit models typically do. It seems any company that offers credits long enough eventually hears enough pushback from customers that offering rollovers becomes a no-brainer. A great example is Clay, which has different rollover policies for monthly (2x monthly limit cap) and annual (15% credit rollover) customers.

A credit model without a usage dashboard is a problem waiting to happen. Most companies that offer credits offer visibility and tracking in a clear dashboard. Monday.com is a great example. They have an AI Governance section in their Admin portal breaking down credit usage by feature.

(Note: Credit dashboards are table-stakes, and increasingly so are usage controls for the end customer so they can govern how they use their commits by users, roles, orgs, and yes, agents. If you're looking to deliver both usage telemetry and control to your buyers, talk to our friends at Schematic).

One of the biggest concerns around credits is unpredictability. One way to fix that is to allow customers to set credit caps. A great example is Hubspot. Hubspot allows customers to set credit caps at the Account or Product Level.

Credits are partly a hedge — an abstraction layer between your price and your costs. When underlying model costs shift (and in AI, they shift monthly), you don't touch the headline price — you adjust the rate card.

Ideogram shows the mechanism in action: image generation is priced by model and quality tier — 2 credits for Turbo, 4 for Balanced, 6 for Quality — so when a new model ships with different economics, it simply gets a new line on the rate card. The subscription price never moves. The alternative here would be doing what Figma does: offering a range of credits per action, which gives a buffer as underlying costs change.

Look at the arc of the ten questions: the first four design the currency, the next three handle its lifecycle, 8 and 9 hand customers visibility and control, and 10 protects your margins. A credit model isn't just a pricing decision — it's an operating system that touches product, finance, and customer teams.

Trust is the thread running through all ten. Credits ask customers to buy an abstraction, and they'll only keep buying it if the conversion is clear, consumption is clean, and the meter never surprises them. If you can answer all ten before launch, credits can deliver the holy grail: usage-aligned revenue with subscription predictability.

Thanks for reading! If you’re working on AI monetization and want to learn more about how we help, book time here.

Until next time,

Rob