SaaS pricing models: moving beyond per-seat to usage and outcomes

Kyle Poyar's 2025 State of B2B Monetization survey, which collected data from more than 240 software companies in April and May 2025, offers the clearest recent picture. Over the previous twelve months, seat-based pricing fell from 21% to 15% as a primary model, flat-fee subscriptions fell from 29% to 22%, and hybrid pricing rose from 27% to 41%. Three in four software companies changed their pricing in the past year.

Two features of the data matter for a CEO. First, the winner is not pure usage pricing. Hybrid, a platform or subscription fee combined with a metered element, absorbed the share lost by both older models. Second, the pace is fast. A model that was dominant three years ago is now a minority position, which means the buyers you are courting have already seen alternatives from competitors.

A caution on interpretation: the typical respondent was a small or mid-sized software business headquartered in the United States. Larger enterprise vendors and European or Gulf-based companies may move at a different pace, constrained by procurement practices, public-sector contracting rules and the preferences of long-standing buyers. The direction is clear; the speed in your market has to be tested.

The implication is that doing nothing is not a neutral choice. If your competitors move to hybrid and you keep selling seats, you compete on a model that buyers increasingly find hard to justify as AI changes how work gets done. What to do: map your current model against the three alternatives, and write down which customers would pay more, the same or less under each. That table will show quickly whether the change is a threat or an opportunity for you.

There is a further point about the sample. The survey divides respondents into those whose product is mostly SaaS, those that are AI-native and those that are a hybrid of the two. In Poyar's data 53% of respondents include AI capabilities as part of their core software offering, while only 16% sell AI mainly as a standalone product or add-on. If AI is part of the core product, the unit of value is changing inside the same licence, and a pricing model built around named users has no obvious place to capture it.

Finally, consider where your own customers sit on that curve. Enterprise buyers with complex procurement may take years to change how they buy, while smaller companies adopt new models quickly. If your base is concentrated in large enterprises, the pace of change in your revenue may lag the market average, giving you time to learn from others. If your base is in small and medium-sized businesses, expect the shift to arrive faster and plan for it now.

A useful early step is to examine your own renewal data for signs of seat contraction: customers who reduce licences at renewal, or who ask for flexibility on user counts. These signals tend to appear before the revenue effect becomes material, and they show where the pressure on your model is building. Tracking them quarterly gives the leadership team an evidence base for deciding how quickly to move.

Per-seat pricing assumes that value scales with the number of people logging in. AI agents break that link. If software completes tasks that a team once did, fewer seats are needed for the same output, and the customer will want to pay for the work done. BCG's IT buyers survey found that 40% of buyers cite seat reduction as their primary lever for cutting software spend, which is a direct threat to any vendor whose revenue is a function of headcount.

Gartner takes the same view at market scale. It estimates that agentic AI could disrupt up to $234 billion of enterprise application software spending by 2030, and expects market price adjustments to account for about 20% of enterprises' SaaS spend by that year. These are forecasts, not outcomes, and Gartner's figure covers a period of several years, but the direction is consistent with the buyer behaviour BCG reports.

Vendors are already experimenting. Salesforce moved Agentforce from $2 per conversation to Flex Credits priced at $500 per 100,000 credits, with a single action consuming 20 credits, which works out at $0.10. Whatever one thinks of the specific numbers, the structure is revealing: a large incumbent chose to meter the work done by an agent rather than charge for a licence to use it.

What to do: identify which parts of your product replace labour and which merely support it. The first group is where seat-based pricing is most exposed and where an alternative unit, such as a task, a document, a resolved ticket or a credit, is most natural. The second group can often stay on seats. Not every product needs to change, and a mixed price book may be the right answer.

The commercial consequence is a squeeze from two directions. Buyers are using their own AI tools to reduce the number of people who need access, and vendors are adding AI features that cost real money to run. A seat price that was designed for a fixed cost of service now faces a variable cost of delivery. The two pressures together explain why even companies with strong retention are looking at their price books, and why we treat this as a question of margin as much as of growth.

The CEO's question is not whether to respond but how fast. Companies with high net revenue retention have time to run a careful pilot; those already seeing seat contraction at renewal need an interim measure, such as an AI add-on priced on usage, while the wider structure is redesigned. The distinction between a defensive and an offensive move should shape the pace.

Press coverage often implies the industry is heading toward charging only for results. The data are more measured. An ICONIQ survey of 300 executives at software companies building AI products, reported by SaaStr, found 38% use hybrid pricing, 36% subscription or seat-based, 19% usage-based and 6% outcome-based. In the Poyar survey only 5% said outcome-based pricing is their primary model today, though 25% expect it to be by 2028.

The barriers are practical. BCG, citing an a16z survey, notes that 47% of buyers struggle to define clear, measurable outcomes. If the customer cannot agree what counts as a result, the vendor cannot invoice it. Outcome-linked pricing also makes revenue depend on factors outside the vendor's control, such as the customer's data quality, process discipline and seasonality.

Usage-only models carry a different risk, visible in growth figures. Benchmarkit's 2024 survey, published by the billing vendor LogiSense, reports median growth of 21% for hybrid pricing companies and 20% for subscription-only, against just 13% for those using usage-based pricing without a subscription. Treat this as one vendor-published data set, with a single year of data, rather than proof. It does fit what finance teams say: pure usage revenue is volatile, and volatility is penalised in planning and valuation.

What to do: reserve pure outcome pricing for narrow, measurable use cases where both sides can verify the result from system data, and where the vendor can influence the outcome. Elsewhere, keep a subscription floor that covers your fixed costs and use usage or outcomes to capture upside above it.

It is tempting to read these figures as an argument for caution, but they are better read as an argument for structure. The firms capturing growth are those combining a recurring fee with a variable element. Their customers get a budget they can plan around, and the vendor gets an upside that tracks the customer's own success. Pure models, at either extreme, ask one side to carry all the risk, which is why they remain uncommon.

The honest conclusion is that nobody yet has definitive evidence on the best structure for AI products. The surveys are recent, the samples are modest, and several come from vendors with a commercial interest in particular approaches. A CEO should treat them as a guide to direction and a source of hypotheses, then collect their own evidence from pilots, win-loss analysis and customer interviews. Doing so builds an advantage that cannot be copied from a report.

The commercial case for adding a usage element rests on expansion. Benchmarkit reports that 33% of usage-based companies achieve net revenue retention of 120% or more, the highest of any model, and that 85% of companies with NRR above 110% use some form of usage pricing. It also found 67% of companies now use some usage-based pricing, compared with 52% in 2022. Usage lets revenue grow with the customer without a renegotiation.

L.E.K. Consulting, summarising other research in August 2025, reports that hybrid-pricing companies achieve average revenue growth of 19% and EBITDA margins of 16%, and that consumption-priced companies trade at a valuation premium despite lower margins. These are secondary citations of third-party work, so they should be read as indicative. They do suggest investors reward the combination of recurring base revenue with usage-linked growth.

The risk is that variable revenue becomes hard to forecast, and a CEO is accountable for the forecast. A platform fee that covers committed costs, a minimum commitment tier, and clear usage bands reduce variance. Finance teams also need new capabilities: usage metering that is accurate enough to bill from, revenue recognition rules for variable consideration, and forecasting models that treat usage as a driver rather than a plug.

What to do: before piloting, model how revenue would have behaved over the last eight quarters under the proposed structure. If monthly revenue would have swung by more than your cost base can absorb, increase the fixed element or add a committed-spend tier. The pilot should test the structure that you can afford to run, not the one that looks best in a pitch.

Maxio's 2025 pricing report, powered by Benchmarkit, reports the same ranking: companies using hybrid models report the highest median growth rate, at 21%. It also finds that 73% of SaaS companies with usage-based models are actively forecasting variable revenue. The second number is a reminder that the capability to forecast usage is becoming a normal part of finance, and companies that build it early will be able to negotiate commitments and plan hiring with more confidence than those that react to monthly billing runs.

L.E.K. cites an a16z survey of 100 CIOs in which 39% said they favour pricing tied to tokens, API calls or time, while seat-based pricing was preferred by just 21%. It also cites a survey in which 85% of SaaS companies already use usage-based pricing or are implementing it. The direction of travel favours consumption, and buyers have started to expect it from AI products in particular.

Preference is not the same as willingness to sign. The same CIOs worry about budget certainty, and procurement teams, particularly in regulated sectors and public bodies, often prefer fixed annual commitments. In the Gulf, large buyers frequently contract through multi-year agreements, so a model that depends on monthly consumption may meet resistance unless it is paired with a committed volume. We would test this directly with buyers rather than assume.

BCG's finding that 68% of vendors, per ICONIQ, charge separately for AI features or restrict them to premium tiers shows how widely vendors are experimenting with packaging. The more important point is what is happening in conversations. Customers want to understand the likely cost before they commit, and a vendor who offers a calculator, a spend cap and alerts at agreed thresholds removes the principal objection to usage pricing.

What to do: design for predictability as deliberately as for value. Offer committed-use discounts, caps, rollover credits and a clear cost calculator. Report the bill to the customer's administrator before the invoice arrives. Customer success teams should treat the first three invoices as part of onboarding, because a surprise invoice early in the relationship is a common cause of churn.

The nature of the product shapes how much predictability a buyer needs. A developer tool used by engineers can tolerate variable bills because usage correlates with value and the buyer can control it. A customer service platform handling regulated interactions has seasonal peaks the buyer cannot control, and so needs caps and committed pricing. Segmenting the offer by how controllable usage is, rather than choosing one structure for all customers, is a practical way to respect both needs.

Procurement practice differs between regions, and it affects design. In the United States, buyers are accustomed to negotiating usage tiers and committed-spend discounts. In the United Kingdom and continental Europe, finance and legal reviews often emphasise data residency, annual budgeting and the ability to terminate. In the Gulf, large organisations and government-linked entities frequently prefer fixed multi-year agreements. A model designed with those norms in mind, such as a committed base with defined overage rates, tends to travel better than one that assumes monthly variable billing everywhere.

The installed base is where the risk is concentrated. Existing customers have contracts, budgets and internal champions who negotiated the current price. Changing the price book for them invites renegotiation, concentrates attention on cost, and can trigger a competitive review at the worst moment. New customers carry no such history, which makes them the cheapest place to learn.

A pilot should be deliberately narrow. Choose one product line and one or two segments, define the success measures in advance, and run it for two or three quarters. Measure win rate, average first-year contract value, time to first expansion, invoice disputes and gross margin per unit of usage. Compare the cohort with a control group still sold on the old model, and share results with the leadership team monthly.

Be ready for what you will learn about unit economics. Metered pricing exposes the cost of serving heavy users, especially where AI inference costs scale with consumption. Salesforce's shift to credits suggests that large vendors are wrestling with the same question. If your gross margin on the heaviest decile of usage is poor, the pilot is telling you something valuable about where to place price floors, tiers and fair-use limits.

What to do: write a one-page pilot charter covering scope, duration, success thresholds, and the conditions under which you will stop. Approve it at board level so that a weak first quarter does not cause the programme to be abandoned, and so that a strong one does not cause it to be rolled out before the finance and billing systems are ready.

Be careful, too, about internal communication. A pilot that the sales team believes is a prelude to cutting their commission will be undermined. Explain what is being tested and why, share early results and invite field feedback on packaging. Often the best objections to a draft price book come from account executives who have spent years listening to customers describe how they actually budget for software.

Pricing changes fail more often in the sales plan than in the price list. If reps earn commission on annual contract value from seats and the new model produces a lower initial contract with higher potential expansion, a rational rep will avoid selling it. Quota, accelerators and the definition of bookings must be changed first, so that the people closest to the customer benefit when the customer chooses the new structure.

Several design questions need answers. How will usage revenue count toward quota: as forecast, as committed, or as realised? Who is paid on expansion and for how long? How are renewals and overages shared between the account executive and customer success? Each answer sends a signal about what the company values. We prefer simple rules that pay on committed revenue at signature and on realised usage as it arrives, with a cap on how much forecast can be credited.

Sales leadership will also need new skills and tools. Reps must explain consumption, handle questions about variability and help buyers size their commitment. Deal desks need guidance on discounting a platform fee versus a unit rate. Pre-sales teams need calculators and reference examples. Training is cheap relative to the cost of a stalled pilot, and it signals that the change is a company commitment rather than a pricing experiment.

What to do: change the compensation plan one quarter before the pilot starts, and communicate the logic to the team. Then treat the installed base as a separate project with its own design: grandfathering rules, migration incentives, renewal-date sequencing and a customer communication plan. Only start it when the pilot shows win rates and expansion at least as good as the old model.

The pay plan is also the cheapest lever to reverse. If an early design pays too generously on forecast usage that never materialises, a one-quarter adjustment corrects it. A price book that has already been published to customers is far harder to retract. That asymmetry is the main reason we recommend the order: compensation first, pilot second, price book third, installed base last.

Our Commercial Growth & Sales Execution work helps SaaS leadership teams design the pilot, rebuild sales compensation and prepare the forecasting and billing changes needed to move from seats to hybrid or outcome pricing. We work alongside your finance and product teams so that the price book follows evidence from new customers, not assumptions.