A quiet revolution is reshaping the business model of software-as-a-service. The SaaS industry is shifting from monthly “per seat” licenses to embrace usage-based, pay-as-you-go pricing.
The driving force? AI, and specifically a new class of reasoning models that are computationally intensive and expensive to operate.
This isn’t just a pricing experiment; it may be an economic necessity for some companies as they adjust to the cost of running AI-powered software services.
The rise of costly inference
If you’ve been reading my stories, you’ll know I warned that the generative AI revolution would bring major pricing changes to some internet businesses. Back in January 2024, I wrote that it costs a lot to build AI models, and noted that Big Tech companies were looking for new sources of revenue growth, such as subscriptions.
Now, there’s a new breed of “reasoning” AI model that’s really expensive to run. They don’t just spit out simple responses. They loop through steps, check their work, and do it all again — a process called inference-time compute. Every step generates new “tokens,” the new language of generative AI, which have to be processed.
For instance, OpenAI’s o3-high model was found to use 1,000 times more tokens to answer a single AI benchmark question than its predecessor, o1. The cost to produce that one answer? Around $3,500, according to Barclays analysts.
These costs aren’t theoretical. As enterprises integrate AI into core workflows, building agents, copilots, and other complex decision tools, each query becomes more compute-hungry. And when millions of users are involved, those costs scale fast.
The result: Software companies may struggle to keep charging flat monthly fees if AI usage and compute costs spike and become wildly uneven across their customer bases.
Why seat-based models may no longer work
For decades, SaaS companies such as Microsoft and Salesforce have typically charged per user, per month. This has been a clean, predictable model that worked well when marginal usage costs were near zero. But generative AI changes that. With inference compute costs high and rising, flat pricing becomes a potential financial liability.
“Elevated compute costs for AI agents may drive a higher cost of revenue compared to traditional SaaS offerings, forcing companies to rethink their cost-management strategies,” consulting firm AlixPartners, wrote in a recent study about AI threats to software companies.
The new model: pay-as-you-go
Instead of charging per user, companies are beginning to charge based on activity, whether that’s tokens consumed, queries run, automations executed, or models accessed. This aligns revenue more closely with usage and ensures companies can cover their variable and rising infrastructure costs.
Sam Altman floated an idea like this for OpenAI last month.
an idea for paid plans: your $20 plus subscription converts to credits you can use across features like deep research, o1, gpt-4.5, sora, etc.
no fixed limits per feature and you choose what you want; if you run out of credits you can buy more.
what do you think? good/bad?
— Sam Altman (@sama) March 4, 2025
Developer platform Vercel already operates on this principle: The more traffic a customer’s site receives, the more they pay.
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“It’s better aligned with customer success,” Vercel CFO Marten Abrahamsen told me in an interview. “If our customer does well, we do well.”
Early adopters
Younger companies like Bolt.new, Vercel, and Replit are at the forefront. Bolt.new, a low-code platform powered by AI agents, saw a major inflection in revenue growth after shifting from per-seat pricing to usage-based tiers. Its plans now scale with tokens consumed, from casual hobbyists to full-time power users.
Bolt.new/Barclays research
Meanwhile, Braze and Monday.com have introduced hybrid pricing models, mixing base seat licenses with pay-per-use AI credits.
For Monday.com, many seat-based customers get 500 AI credits to use each month. When they exhaust these, they must pay extra for more.
ServiceNow’s approach
ServiceNow
ServiceNow, one of the SaaS players, has added usage-based pricing, but only as a small add-on to an otherwise predictable, seat-based offering.
CEO Bill McDermott told me the company spent years building a cheap, fast, and secure AI platform with help from Nvidia. He also noted that many of the big AI models out there, such as Meta’s Llama and Google’s Gemini, have become a lot cheaper to tap into lately.
Still, ServiceNow weaved in usage-based pricing to protect itself in rare situations when customers are extremely active and use a huge amount of tokens that the company has to process.
“When it goes beyond what we can credibly afford, we have to have some kind of meter,” McDermott said.
He stressed that customers can still rip through thousands of business processes before they hit this usage-based pricing tier.
“Our customers still want seat-based predictability,” McDermott added. “We think it’s the perfect goldilocks model, offering predictability, innovation, and thousands of free use cases.”
Investors have noticed
Investors are taking note. Barclays analysts argued recently that usage-based software companies, such as JFrog and Braze, should command premium valuations, especially as seat-based vendors face potential slower revenue growth from AI features that don’t scale with user count.
“We are hearing more concerns from investors that the ongoing prevalence of AI agents could lead to lower incremental revenue contributions from seat growth for SaaS vendors,” the analysts wrote in a note to investors recently.
This shift could lead to more volatility in quarterly revenue, but stronger long-term alignment with product value delivered, the analysts explained.
The downsides
The downside is that these are variable costs for customers. Instead of knowing exactly how much a service costs every month, your costs might rise unexpectedly if you get a lot of traffic, or your employees go nuts for new AI tools, for instance.
There’s a similar problem facing the companies providing these new AI-powered software services. Their sales may rise and fall more in line with customer success and activity in general. That lumpy revenue is less attractive to investors, compared to the reliable monthly seat-based sales often generated by traditional SaaS providers.
David Slater, a chief marketing officer who’s worked at tech companies including Salesforce and Mozilla, recently built a personal website using Bolt.new. He says costs could easily get out of control if you use the tool heavily, or go down a design rabbit hole and keep tweaking something over and over.
The allure of SaaS services is that they are predictable, for customers as well as providers. Anything that messes with this situation could be a concern, especially for end users.
“A pricing model that’s not predictive for the company and the consumer cannot stand,” Slater told me in an interview.
The road ahead
The shift from seats to usage isn’t just about AI, but AI is the catalyst. As software gets smarter, more dynamic, and more compute-hungry, tying pricing to actual use may become a more sustainable path forward.
Expect to see more companies introduce token credits, pay-per-query pricing, or hybrid models in 2025, not just because it’s more efficient, but because it may be the only way to stay afloat as AI adoption accelerates.
Now, this could all change again if generative AI compute costs fall over time. That’s happened in previous computing eras, and some experts see this happening again. Or, at least, they hope so.
“Sooner or later, AI costs are going to plummet, and then this usage-based model dies, replaced with an anchor like seats, or time, or a monthly subscription that’s understandable,” Slater said.