The state of B2B monetization in 2025
Introducing new data from 240 software and AI companies
The appetite for new, disruptive pricing models is higher than ever before. Software companies are realizing they can't solely rely on flat-rate or seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in.
But interest in disruptive pricing models doesn't always translate into adoption. And interest doesn't mean folks will accept new ways of buying. That left me wondering: what’s really going on with software monetization?
With your help, I've spent the past three months investigating the latest in SaaS and AI monetization. And I've collected data from more than 240 software companies. This wouldn’t be possible without the help of Growth Unhinged readers — THANK YOU!
You can read the full 35 page State of B2B Monetization report here. In today’s newsletter, I’ll unpack the six biggest takeaways that stood out to me:
Seats and flat-rate pricing are increasingly under threat.
As AI continues to gain traction, hybrid has become the ‘it’ pricing model.
There are a seemingly infinite number of ways to structure hybrid pricing. Choose wisely (or support multiple models).
Outcome-based pricing is seen as the 'holy grail'. It’s still out of reach for 95% of the market.
The shift toward pricing transparency seemed inevitable. This isn’t playing out.
Pricing models keep evolving. Most of the market is unprepared to keep up.
Who participated in the survey
Before we dive into the data, I wanted to share more information about who participated in the research. The State of B2B Monetization survey was run from April to May 2025. A typical respondent was between $1-20M ARR, headquartered in the US and selling a product that’s a hybrid of SaaS and AI.
There was a fairly healthy mix of survey participants by company size, geography and product types.
Annual recurring revenue (ARR): 26% were <$1M ARR, 22% were $1-5M ARR, 15% were $5-20M ARR and the remaining 37% were above $20M ARR.
Geography: 57% were based in the US. 24% were in Europe or Israel. The remainder were in the UK (5%) or elsewhere (14%).
Product type: 50% described their product as mostly SaaS. 14% said they’re AI-native. The remaining 36% were a hybrid of SaaS and AI.
Takeaway 1: Seats and flat-rate pricing are increasingly under threat.
As recently as 12 months ago, software pricing was mostly seat-based pricing and flat-rate subscriptions. These models offered pricing predictability and the promise of durable recurring revenue (ARR). And they’re increasingly under threat, particularly for AI-native products, due to value misalignment and cost pressure.
Flat-fee and seat-based pricing are being replaced by hybrid pricing, i.e. combinations of subscriptions and usage (more on that later).
Flat-fee subscriptions are down from 29% to 22% over the past 12 months.
Seat-based pricing is down from 21% to 15%.
Hybrid pricing is up from 27% to 41%.
What’s happening is that software and AI are becoming inseparable. More than half of survey respondents (53%) said they include AI capabilities as part of their core software offering. A mere 20% said that they don’t offer any AI capabilities. Even fewer (16%) said that AI is largely sold as a standalone product or add-on.
The rise of AI, and AI agents in particular, results in value being decoupled from customers needing to buy more seats. In fact, if the AI works as intended, customers likely need fewer people and more AI.
Alphabet says that AI now generates more than 30% of their code.
Microsoft’s CTO expects 95% of all code to be AI-generated by 2030.
Cursor grew to $200M ARR with 60 people, more than $3M per employee.
Klarna says their ARR per employee soared from $575k to $1M as a result of an AI efficiency push.
Meanwhile, the costs of delivering AI capabilities are real, and they’re becoming a key input into pricing. Survey participants cited internal costs and margins as the most important factor when pricing their AI capabilities.
Takeaway 2: As AI continues to gain traction, hybrid has become the ‘it’ pricing model.
Software founders used to tell me their pricing was inspired by Salesforce or maybe Slack. Now they tell me they were inspired by Clay. (Other pricing inspirations include Intercom, HubSpot, OpenAI and, yes, still Salesforce.)
Clay's hybrid pricing model has multiple routes to expand customers while keeping pricing relatively simple: more features (subscription packages) and more usage (credits). All of Clay’s plans include unlimited seats.
Instead of offering a huge discount for an annual plan, Clay offers a small discount (10%) and lets the customer get all their credits upfront. Unused credits can be rolled over to the next month (up to 2x), which is both customer friendly and creates lock-in.
Similar hybrid models have been recently introduced by startups and large incumbents alike. I’ve been tracking monday.com (which now offers 500 AI credits per month across all paid plans), Agentforce by Salesforce (which added a flex credits model in May), Atlassian and numerous others.
Hybrid pricing is a natural evolution from seats or flat-rate subscriptions. I see four reasons why they’re becoming so popular:
Minimal disruption. Hybrid pricing doesn’t require reinventing the wheel – it can be layered into existing seat-based and subscription models.
Upsell path. It creates a natural upsell path, letting customers try new products ‘for free’ and then monetizing as usage grows.
Margins. By capping usage, companies control costs and minimize the risk of unprofitable customers.
Relatively predictable. By staying within a traditional pricing paradigm, buyers can estimate their costs and feel in control of their spend.
Takeaway 3: There are a seemingly infinite number of ways to structure hybrid pricing. Choose wisely (or support multiple models).
As the industry steadily shifts toward hybrid models, particularly for AI products, a new challenge arises: there’s a seemingly infinite number of ways to structure hybrid pricing.
I’ll unpack some of the approaches along with the pros and cons. I suspect most folks will offer multiple options as they seek to balance lands, expands and tough procurement conversations. (The downside, of course, is added complexity.)
Pay-as-you-go (PAYG). Ok, this isn’t really hybrid, but it’s helpful to start with this one. PAYG means no commitment, totally flexible. This works best when customers can bill-back the expense or bake it into an operating budget. Otherwise, beware of enterprise procurement!
PAYG with a cap. This model offers buyers peace of mind by capping their potential usage/spend. It’s increasingly seen with outcome-based models where outcomes are unknown in advance.
Usage-based tiers. Customers commit to a certain level of usage or tier; this is typically "use it or lose it". There are multiple sub-flavors including high water-mark billing (if usage ever exceeds the plan, customers immediately go into overages) or a drawdown model (usage can be consumed flexibly, like a gift card). Fear of overages and usage fluctuations encourage sales to over-sell and customers to over-buy.
Platform fee plus usage. Asking for a platform fee helps lock customers in while providing them access to advanced features, premium support, etc. This approach works well when the pricing metric is getting commoditized (ex: SMS messages, compute, storage) or when it doesn’t reflect the full value of the product. Vendors can advertise being affordable, but make up for it with the platform fee.
Platform fee (includes usage) plus usage. Also known as a three-part tariff, this model has a larger subscription fee that includes some level of usage "included for free". Providing a minimum amount of usage helps get the customer hooked and usually incentivizes more overall consumption.
Adaptive flat rate. In this model, the customer commits to a usage-based tier, but can use the product as much as they want with no overages or upgrades during that contract. Their tier resets up or down at renewal based on actuals. An adaptive flat rate is predictable for customers while encouraging them to increase consumption over time (the downside is that you’re stuck with the costs!).
Platform fee plus success bonus. In this model, pricing is communicated as a more traditional subscription fee. If the customer gets a better ROI than expected, they pay a bonus or commission on top.
Takeaway 4: Outcome-based pricing is seen as the 'holy grail'. It’s still out of reach for 95% of the market.
5% of survey participants said their primary pricing model is outcome-based right now. However, 25% said they expect it to be outcome-based by 2028.
Outcome-based pricing early adopters — like Intercom, featured previously — are paving the way for the market. Some of these outcome-based models could be better understood as work-based pricing (see: EvenUp, Casemark). Others are truly success-based pricing where they take a cut of the upside (see: Chargeflow, Flycode and AirHelp). AirHelp, for example, charges a 35% success fee when it wins compensation for delayed or cancelled flights.
When AI agents are positioned as doing the work, it feels natural that they would be priced based on the amount of work completed (or, potentially, the upside associated with that work). This is quite powerful from a marketing perspective. It sends a message that you’re willing to fully stand behind your product. And it creates a strong incentive for vendors to invest in continuously delivering more outcomes.
There’s a big elephant in the room, though, which I’m calling the CAMP framework. To pull off outcome-based pricing, businesses need outcome consistency, attribution, measurability and predictability (CAMP):
Consistency. Do all customers value the same outcomes? Or do outcomes need to be customized, leading to a proliferation of bespoke contracts?
Attribution. Can you convince customers to give your product credit for delivering the outcome? Or do they believe they drove the outcome, with a small assist from you?
Measurability. Can you measure and report on the outcomes in real-time? Or do you require customer reporting, A/B testing and/or a proof of concept?
Predictability. Can you predict the outcomes your product will achieve with some level of accuracy? Or do outcomes vary significantly from customer to customer?
Takeaway 5: The shift toward pricing transparency seemed inevitable. This isn’t playing out.
Hiding your pricing felt like a relic of the 90s & 00s. After all, a savvy buyer is doing research online (or asking their peers) & can probably find the info. Tools like Vendr are even showing how much others are paying with a free Chrome extension.
Making your pricing public lets you tap into this buyer demand (and search traffic) while controlling the narrative. And it screens out disqualified buyers so they don't waste your team's time.
Well, this isn't exactly playing out. Respondents with an average annual contract value (ACV) of less than $5,000 and those with a PLG offering do typically put their pricing online. Everyone else, not so much.
My two cents: many software companies, especially early-stage and AI companies, don't have pricing totally figured out. As soon as they publish pricing, it gets way harder to adjust without confusing folks.
Additionally, as pricing models get more complicated (see: hybrid pricing with AI credits), buyers don't necessarily trust what they see on a website. There are inevitably questions about usage limits, overages, which features cost extra and the like. When complexity goes up, buyers want to talk to a real person.
Takeaway 6: Pricing models keep evolving. Most of the market is unprepared to keep up.
AI is simply changing too fast to blindly stick with the pricing status quo. (In fact, three-in-four software companies made a change to their pricing last year.)
As pricing has become an increasingly strategic (and complex) decision, pricing needs to be resourced appropriately. There’s real work to be done to understand costs, competitors and customer value. Most of the market is unprepared to keep up both in terms of personnel gaps and a reliance on legacy tooling.
In the early stages of company-building, pricing almost always becomes a Founder/CEO decision. Then it starts to become a game of hot potato, bouncing across Sales, Product, Marketing, Finance and Operations.
Be careful to avoid pricing ‘no-man’s land’ — this happens between $5-20M ARR when ownership often falls through the cracks.
What comes next
I’m still bullish about usage-based and hybrid pricing models, but increasingly I see them as a stepping stone toward work-based and outcome-based pricing.
This is part of a broader movement from owning software (on-premise) to renting software (SaaS and subscriptions) to using it on-demand. At each stage, the industry lowered upfront costs, making software more accessible. And risk shifted from the buyer to the vendor, making vendors accountable for delivering outcomes for customers.
If we could get to the promised land, we’d change the dynamics of how software companies operate. Every department would be oriented around helping customers achieve their ambitions.
I’ll be watching to see who’s able to unlock this next evolution of software pricing. Circle back next year? In the meantime, don’t forget to download the full report.
It is going to become more and more important to align pricing with AI based buying processes. If the AI cannot understand your pricing model it is less likely to recommend purchase.
I recall we spoke about it on LinkedIn when you published the survey. Kudos to you for all the work you do to generate these insights.
Great to see, the future is hybrid - at least, certainly in the short to medium term :)..