Still pricing your AI feature like a regular SaaS?
That might be why users aren't upgrading, and your revenue isn’t scaling.
I’ve worked with dozens of early-stage teams navigating exactly this. It’s a new set of challenges to figure out what to charge, when to charge, and how to design pricing that actually reflects the value users experience, especially when AI is involved.
Here’s the shift: AI changes what customers value, how they perceive cost, and when they’re willing to pay. Traditional seat-based or flat pricing fails to capture the exponential value users get from AI features.
AI is not the first shift that forced a rethink in pricing models. Let’s look at two historical moments:
1. SaaS shift from license to subscription (early 2000s): Adobe, Microsoft, and others moved from $1000+ licenses to monthly cloud access, sparking both outrage and explosive adoption. This moved cost from CapEx to OpEx.
2. Mobile apps and the rise of freemium (2010s): Mobile developers learned that users expected value before paying, forcing the birth of feature gating, ads, and in-app purchases.
3. Cloud infrastructure pricing (AWS era): Developers went from fixed-price hosting to pay-per-GB, pay-per-request. This was the birth of metered pricing at scale.
Each of these shifts came from a change in technology and behavior, just like AI today.
Let’s talk about what the AI pricing playbook looks like.
When Adobe transitioned from Creative Suite to Creative Cloud, it completely redefined its pricing strategy. Originally, Adobe sold Photoshop, Illustrator, and other tools as one-time licenses. You paid hundreds or even thousands of dollars upfront for software you’d own forever. This was classic feature-tiered pricing: Want video tools? Pay extra. Want the whole suite? Pay even more.
But as cloud infrastructure evolved and user expectations shifted, Adobe moved to a monthly subscription model. That was the first shift, from feature ownership to continuous access.
Then, with the introduction of Adobe Firefly (its generative AI engine for images and video), Adobe added a new layer: usage-based pricing. Instead of simply giving Firefly to all subscribers, Adobe added a credit system, users get a certain number of AI generations per month, and can pay for more if they exceed it. It’s a hybrid model: feature access via subscription, AI output via credits.
Source: Compare plans that include generative AI | Adobe Firefly
This is the core shift AI introduces:
1. Traditional SaaS pricing is built around feature differentiation. You charge more to unlock more tools (even though it started changing with tools like Slack, Amplitude, which incorporated usage-based tiers way before massive AI-adoption).
2. AI pricing needs to reflect value variability. You charge more when the AI delivers more.
Why doesn’t the old model work?
3. AI is expensive to operate. Every action, even a short summary, might cost you API or compute fees. Flat pricing becomes risky.
Value is nonlinear. One AI query might return an unusable result. Another might generate a proposal that lands a deal. That’s not the same value, so in the user’s mind shouldn’t be the same price.
The buyer mindset has changed. Users don’t see AI as a nice-to-have feature. They see it as labor-saving automation. They don’t want to pay for access, they want to pay for results.
This is why pricing AI tools per seat or per feature, like most SaaS used to, doesn’t make sense on its own. Many AI-driven product think in terms of:
Because when AI can deliver massive leverage, but the results dynamic might be unpredictable, users expect pricing that reflects value, not seats, storage, or admin tools.
Balancing cost predictability and a low barrier for the users is definitely a challenge. I designed this AI-pricing Matrix to highlight how different models scale on the predictability of cost-to-serve for the teams and a barrier to conversion for the users.
I love how Elena Verna asks a sharp question when it comes to AI-pricing: “What are your buyers really buying?
Because you’re not selling API calls. You’re not selling features. You’re selling outcomes:
Companies like Intercom, Copy.ai, and Synthesia structure pricing accordingly:
Source: Intercom Pricing
Source: Copy.ai Pricing
Source: Synthesia Pricing
What is Buyer Motion?
Buyer motion refers to the way your customers make purchasing decisions, including who initiates the purchase, who approves the budget, and what motivates the decision. This concept, popularized by Elena Verna, helps teams match their pricing strategy to real-world buying behaviors.
Here are the key types of buyer motion:
Understanding your buyer motion helps you:
The biggest pricing mistakes I see with AI products aren’t about charging too much, they’re about charging in a way that doesn’t match how users experience value.
Here are five traps I see teams fall into (and how to avoid them):
1. Charging for inputs. If you’re charging by token, second, or API call, your pricing feels like metered water, not a product. Most users don’t understand what they’re paying for, and they certainly don’t brag about how many tokens they used. Instead, price by output or outcome: what did the AI actually do for them?
2. Giving AI away in the base plan. If users get the magic too early, they won’t upgrade. Worse, you’ve made the most valuable part of your product invisible in your pricing. Instead, use AI as your upgrade engine: give a taste, then charge for automation, scale, or deeper integrations.
3. Forgetting to update pricing after launching AI. You just shipped something that saves users hours, and your pricing stayed the same? That’s value leakage. If the product improves, the pricing should too.
4. Locking yourself into static pricing. AI usage is spiky. One week a team barely uses it. The next, they automate half their workflow. Flat pricing makes you unscalable. Build in usage-based tiers or flexible overages.
5. Charging for the wrong thing. If your pricing is based on clicks, but the real value is in results, you’ll lose trust. Make sure your pricing reflects what your users care about, not what’s easiest to meter.
In short: price for impact, not activity. And revisit your model often, especially when AI keeps shifting the ceiling on what’s possible.
From Monetizing Innovation and Growth Unhinged, here are 5 traps:
A better path: Test. Iterate. And align pricing with what your best customers brag about getting.
Once you've avoided the common traps, the next challenge is turning a great product into a smart pricing model. Most teams get stuck right here, not in building, but in translating product value into pricing clarity.
Here’s a simpler way to think about it:
1. Find the moment that matters.
What’s the moment when users feel the impact of your product? Maybe it’s when the AI summarizes 20 pages into 2, or when it drafts an email that gets a reply. That’s your core value moment.
2. List what you could charge for.
Think broadly:
Here are 9 examples of AI-pricing from top products.
3. Match it to how they buy.
Do they buy solo, like a marketer trying a tool? Do they need team budget approval? Or is it an executive-level decision driven by ROI? This determines if your pricing should be self-serve and simple (like Copy.ai credits), or ROI-based and contract-driven (like Chargeflow’s revenue share).
4. Make sure it works financially.
Can you afford to deliver the value you promise? If usage spikes, do your costs spiral? Sanity check your margins.
5. Design for growth, not just sign-up.
Structure your plans so users can grow with you. Good: try the tool. Better: use it more deeply. Best: automate workflows, get concierge support.
6. Ask: "Can they explain your pricing in one sentence?"
If the answer is no, simplify. Confused users don’t upgrade.
7. Test. Test. Test.
Try a usage-based model vs a per-seat one. Measure what drives upgrades and what blocks adoption.
8. Make room for growth.
Add-ons, overages, usage tiers, design your pricing to expand as users succeed.
9. Watch what users actually do.
See where they get stuck. Where they churn. Where they overuse a free tier. These behaviors should shape your pricing.
10. Review it regularly.
AI changes fast. Your pricing should evolve with it, not once a year, but every time your product changes in a meaningful way.
There’s no perfect pricing model, it’s always a trade-off where you juggle scaling, costs-to-serve, and conversion barrier.
AI isn’t a feature. It’s a force multiplier.
So price like it:
Instead:
Because your AI doesn’t just run code, it replaces workflows, changes behavior, and unlocks speed.
And when pricing reflects that, growth follows.
So dig into your usage model and find the combination that helps you grow.
And let me know if you have any questions:
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