Why seat-based pricing will slowly die (for most categories)
How AI is changing software monetization and pricing
Seat-based pricing built many of the biggest SaaS successes of the past 10 years. Notion, Slack, Zoom, Jira… the list goes on.
It was a great innovation because it let you bet on your customers and through that, the tech market at large: As your customers’ business expanded, their team grew, and so did their software spend.
You’d participate in the upside of the growth of a company without investing.
Customers were also fine with this because most of these tools are collaborative. But that dynamic is crumbling for two main reasons:
Teams are smaller (and grow less)
AI products have real cost
That’s why usage-based or blended models are taking over.
Why seat-based pricing worked—and why it’s breaking down
The past 10 years (up to 2022-23ish) were a golden age for tech. Money was cheap, so it was easy to raise. The millions raised flowed into growing teams, and it seemed like every company was always hiring (on every team).
As teams and companies everywhere grew their head count, they needed new tools to collaborate, which is why collaboration tools were some of the biggest successes of the pre-AI SaaS era.
Take Figma (which Adobe was willing to buy for $20B), whose entire initial pitch was about problems that only arise when teams grow.
Invision and Sketch did much of what Figma initially did, but Figma’s killer feature was real-time collaboration, which obviated versioning (and with it, much miscommunication and parallel work).
Collaboration is easy when you’re tiny, but gets harder as teams grow. Many other 2010-2023 SaaS successes follow the same pattern:
Slack did text-based communication, but email gets too messy for big teams.
Notion did more or less what Google Workspace does, but juggling docs get too messy for big teams.
Asana did more or less what Excel spreadsheets and emails do, but those get messy for big teams.
(all of these products are also superior to the incumbents in other ways, but the first versions especially were often “just” collaboration-ready alternatives)
Pricing should follow how you create value. And if you create value by orchestrating collaboration, charging per team member makes sense. Each team member makes collaboration messier, so collaborative tools get more valuable with each user.
But this also means that charging by the seat means your growth is dependent on customers’ teams growing.
Today, many teams are shrinking. Companies are more hesitant about hiring. New companies hire less because AI increases worker productivity. So the fundamental assumption of seat-based pricing makes growth harder.
AI creates real COGS
One of the premises of pre-AI seat-based tools is that you got more or less unlimited usage (with rate limits so high you’d rarely bump into them). That’s because each individual bit of usage is practically free for the providers.
Slack’s pro plan offers unlimited messages.
It’s probably not even worth calculating the cost of storing each individual message. Their aggregate AWS bill may be giant, but each individual message is basically free.
That’s why software companies have margins other industries could only dream of (often 75%+).
This is no longer true the minute you build with AI.
LLM providers typically charge per million tokens. That means every bit of usage has a direct cost. It also means a single power user can bankrupt you.
Two other factors are subscription fatigue (companies are tired of recurring costs) and, looking into the future, AI agents, who will only access products through API.
All of this indicates that seat-based pricing won’t be as prevalent as before. But how will pricing and monetization change?
How pricing is changing in 2025 (and onward)
keeps talking about the shift towards usage-based pricing. One part of that is that many companies are removing seat caps (example from Webflow): 
You may not get unlimited seats like you do on PostHog (below), but seats are getting more flexible—an admission that they’re no longer the core value driver.
Many companies are switching to usage-based pricing. When your hear usage-based pricing, you might think of pure-play usage-based like Twilio:
But usage-based pricing exists on a gradient. There are many hybrid models:
Credits: Prepay for some amount of usage and use it up over time.
Subscription with overages: Pay a subscription that includes some usage, pay per unit if you use more.
Subscription and usage: Pay a subscription and pay for usage on top of it.
Kyle Poyar has studied this intensely and found that usage-based pricing (in all of its forms) is becoming more and more popular:
The report for 2024 isn’t out yet, but I can’t see that number going down.
That doesn’t mean usage-based is right for every company. Returning to our previous examples, nobody wants to pay by the Slack message or by the Figma frame.
But as usage-based pricing is taking hold, another pricing model is only emerging.
Outcome-based pricing
Usage-based pricing has the downside that usage doesn’t always correlate with value (e.g. Notion doesn’t strictly get more valuable the more pages you have). Seat-based subscriptions have the downside that expenses don’t always correlate with usage.
Outcome-based pricing remedies both of these. It’s what it sounds like: You pay whenever the AI tool achieves an outcome. Intercom is a pioneer of this. It’s AI support agent Fin charges per resolution, i.e. per support chat that didn’t result in a support ticket being submitted.
This is great because it aligns the incentives: Intercom is incentivized to keep chats as short and effective as possible (they pay OpenAI for usage, so their margins increase the less they use). Customers don’t pay for hallucinations.
This is what Sarah Tavel describes as selling work, not software: Instead of selling software that augments a human worker (like Notion, Slack etc. do), AI can complete the work itself—and is compensated like a human (commission-based) worker.
This sounds rosy on paper. But the hard part is defining the outcome you’ll charge for. If a user quits a chat in frustration (meaning they don’t submit a ticket), will Intercom register that as a resolution and charge the customer for it?
These things are hard to define and the error rate is never 0%. That’s why outcome-based pricing is coming to easily measurable things first. It’s relatively easy to calculate your cost per support ticket.
Chargeflow (a chargeback recovery tool), you pay them a commission on the money they get you back, much like agencies would.
You can imagine a similar thing happening for sales (pay per qualified lead meeting booked) or other easy-to-measure outcomes.
For other metrics, outcome-based pricing won’t work.
has a great example of gyms:Planet Fitness: A subscription model. You’re charged the same amount each month for access to the gym. They don’t care if you actually use the treadmill or not. In fact, they hope you don’t show up because it allows them to sign up even more deadbeat members based on expected capacity. The success of the business is based on people paying for something they don’t fully use—CFOs know this all too well from seat-based pricing in software.
Barry’s Bootcamp: A usage-based model. You’re charged (an extraordinary amount) per class. The pro here is that you don’t pay if you don’t use the services. The con? You could theoretically show up, pay an arm and a leg, and just roll around on the mats for 45 minutes without breaking a sweat (guilty as charged). It’s aligned with usage, but it’s still not connected to actual success.
If AI Were a Gym: This would be an outcome-based model. You’d be charged based on actual improvements in fitness. For example, this could be measured using a proxy like muscle-to-fat ratio or an increase in how much you can lift. It’s not about whether you attended the gym or how many classes you took, but whether you achieved your fitness goals.
The problem with the AI gym is that a gym only gives you access to the facilities and can’t control your entire lifestyle. An outcome-based gym couldn’t stop you from having a double cheeseburger after your workout.
That’s why outcome-based pricing fails when the outcomes are vague. Think of HR or brand marketing teams whose work is hard to measure. Or in engineering: You could measure them in lines of code or PRs, but maximizing either of these metrics gives you bad results.
So outcome-based pricing won’t be everywhere, but it will be a new method of pricing. It also means that many companies will wake up to the fact that pricing is extremely often a bottleneck because of their billing systems.
The hidden challenge of pricing AI
For the previous iteration of SaaS, building pricing was an afterthought. You could put up a simple Stripe page or use Paddle to set up subscription payments. As you grew, you could add restrictions around seats and certain feature usage.
But most SaaS companies thought about how much to charge, not how they could actually build their pricing. Because building usage-based billing is actually quite hard.
The more complex your pricing, the more complex your billing. And we’ve seen numerous times (including in previous jobs) how billing can become the bottleneck to shipping new pricing.
That’s why we’re building Lago—because it makes the pricing of the future easier.