You’ve just launched an AI feature. It’s powerful, it’s sticky, and users love it. Now comes the part no one warned you about: pricing it.
You’re not alone. The Wall Street Journal reports that ‘More than two years after the public debut of ChatGPT, software companies still haven’t found a compelling way of charging for AI tools.’ Not just unsure of optimal numbers – unsure of the whole approach. Subscription? Usage? Credits? What counts as “value” when your product is part feature, part infrastructure, and part black-box thingy?
Meanwhile, you’re bleeding time in internal debates. Customers ask questions that your sales team can’t answer. Your costs are rising with every GPU-hour, but your revenue isn’t keeping pace. The numbers don’t make sense – and the margins are getting worse, not better.
The dirty secret of AI product development? You can’t afford pricing to be a go-to-market afterthought. It’s the hidden failure point that can tank even the most technically advanced AI initiative.
There are two ways this hurts you:
- Opportunity Cost: You leave money on the table. A lot of it. That slows growth, weakens your reinvestment cycle, and limits your ability to outcompete rivals – which affects your valuation and your ability to raise.
- Risk Exposure: Unlike SaaS, where heavy usage is great, AI features often have real, variable costs. If your heaviest users are driving up inference costs without corresponding revenue, you’re creating a margin-negative time bomb. One customer’s success could quietly destroy your unit economics.
Learn from the best AI startups
The top-performing AI startups have quietly moved beyond flat subscriptions. They’re adopting hybrid models, combining subscriptions, usage-based billing (like credits or tokens), and even outcome-based pricing where appropriate.
Top AI companies use hybrid pricing models – typically “Per User + Usage” – often via credits to align cost with usage. Some charge by inputs like characters or file size, others by outputs like video minutes. A few adopt outcome-based pricing, tying cost to value delivered, such as resolutions or completed conversations.
Why? Because subscriptions alone are poorly suited to AI’s economics:
- They decouple usage from revenue – a potentially fatal mismatch when the cost of supporting that usage (underlying API calls) is expensive.
- They don’t scale with value – a light user pays the same as a power user, or vice versa.
- They create pressure on margins – particularly when customers learn to game flat-rate plans.
In short, if your pricing still looks like 2015 SaaS, you’re not just outdated, you’re vulnerable.
Hybrid pricing brings revenue in line with value delivered, and with the underlying cost of delivery.
Another way to get pricing wrong – the revenue stack
Let’s say your pricing model is solid, but your systems can’t implement it. Now you’ve got:
- Manual billing workarounds
- Sales reps with no visibility into pricing triggers
- Engineers writing custom scripts just to generate invoices
- Finance flying blind on revenue recognition and forecasting
These are fragile, unscalable systems. And they create exactly the kind of customer experience – surprise bills, inconsistent pricing – that erodes trust.
Making pricing work – without breaking your stack
So how do you adopt smarter pricing without dragging your engineering team into billing hell?
The answer: invisible infrastructure.
You don’t need to rip and replace your CRM, ERP, or subscription tools. In fact, most of the stack you need is already in place. What you’re missing is:
- Metering: tracking real-time usage across products and features
- Rating: translating that usage into billable value
- An integration layer: piping this data between systems automatically
Think of it like an intelligent layer that connects sales, product, and finance without disrupting workflow.
Sales teams stay in the CRM. Finance stays in the ERP. Engineering doesn’t have to build a billing system from scratch. Pricing becomes a strategic lever, not a bottleneck.
Pricing innovation must catch up with product innovation
If there’s one takeaway, it’s this: pricing is part of the product. Your pricing model should evolve alongside your feature roadmap. And your systems need to support it from day one, not duct-taped together at Series B.
Pricing innovation must catch up with product innovation
And systems innovation must keep up with pricing innovation
There’s no fixed playbook yet, which means you can design something that fits how your product works and what your customers value. With the right infrastructure in place, trying new models doesn’t have to be risky or painful. And when you get it working, pricing stops being a stress point, and starts helping you grow faster, stay in control of costs, and keep your team focused on building.
