For nearly two decades, SaaS (Software-as-a-Service) pricing has worked like a well-oiled machine…
In the cloud era, seat-based tiers and flat-rate plans made up for predictable MRR (monthly recurring revenue), with the value scaling with the number of users accessing shared systems. However, the traditional SaaS playbook was designed for a different era, where it made sense to tie value to access when it was users who were driving outcomes.
In the AI (artificial intelligence) era, value has shifted from users to the work that the software performs on your behalf, automating tasks and deciding, analysing, and resolving on its own. And this shift has upended how enterprises monetise, with “output” replacing the old value metric of “users.”
Is Agentic AI rewriting the rules for SaaS pricing models. This article delves into it, and a lot more.
Why User-Based Pricing Breaks Down With AI
While user-based models scale value with human usage, that logic breaks down in AI-native products. When AI agents can autonomously analyse datasets, resolve support tickets, translate pages, and basically generate hundreds of responses, the value is no longer tied to user seats —it’s tied to usage, outcomes, or actions.
Thus arises a misalignment, where customers pay for access, and not outcomes. So, while customers end up getting enormous value without the price reflecting in some cases, the product feels grossly overpriced for what’s delivered in others.
Hence, if your product uses AI and is driving results independent of user activity, then the per-user price might dramatically misalign with the delivered value. If your product is underperforming or seems overpriced despite delivering tremendous value, then it’s worth asking whether your current pricing model should change.
This isn’t the first time SaaS pricing models are seeing a shift. In the early 2000s, you could pay a one-time fee and buy software on a CD-ROM. As internet speeds increased, the industry shifted from single-time license purchases to subscription models that charged monthly/annual fees for access rather than ownership. This then evolved into seat-based pricing, with customers paying for the number of users, which then finally evolved into tiered models that categorised users by support, storage, and capabilities. Every evolution has changed how customers have used software and how vendors have delivered value.
How AI Is Re-examining Saas Metrics
AI is helping SaaS firms use internal data to refine their pricing strategies. For instance, AI can improve pricing decisions, with new tools analysing customer behaviour in real time and testing flexibility across sectors, helping adjust price points and packages quickly.
What’s more, AI is also forcing business enterprises to consider how they package and sell offerings, with many vendors now separating AI features from core functionalities and moving toward modular packaging, allowing customers to decide what they need. Going further, other firms are even adding premium assist tiers or use-based add-ons that can be scaled through adoption.
AI is also reshaping how SaaS companies are tracking and predicting performance. Since usage-based pricing affects recurring revenue, teams are now being forced to reconsider what sustainable growth looks like. With metrics such as retention and ARR (annual recurring revenue) telling only one side of the story, financial teams are now adding factors such as usage trends, cohort revenue, and net dollar expansion to understand customer behaviour more accurately.
Last but not least, AI tools have been improving forecasting by processing usage data in real time and flagging churn risk earlier. So, all static plans are now being replaced with dynamic data-driven assessments of performance. This transparency is now more important than predictability to investors, with companies that have linked usage to revenue earning better market confidence.
Is Hybrid Pricing The New Standard?
A particular model being spoken about in this scenario is hybrid pricing, with 41% of SaaS and AI-native firms rely on it today as their primary monetisation approach. Basically, hybrid pricing is a toolkit that blends fixed subscriptions and usage-based components depending on the product. What makes it tick is that it unlocks expansion levers for go-to-market strategies while offering a predictable base for finance firms. While the base fee keeps billing stable, the usage metering aligns revenue with variable AI infrastructure costs.
Above all, hybrid pricing gives customers control, and even leaders across the stack are using it. For instance, the dev-first Retool has introduced usage add-ons into traditional plans, and Clay has removed seating limits altogether, monetising visa usage and advanced features. Cloud-based WorkOS firm Monday.com has AI credits in its plans, and even OpenAI is supposedly shifting from fixed subscriptions to a hybrid model.
Hybrid models might not have their own set of issues – it’s being labelled as a “catchall” label, and the complexities it brings into the system are undeniable – but when done right, it balances clarity and flexibility beautifully.
What Will Happen in 2026 – And Beyond?
The shift from traditional to dynamic SaaS pricing models might still be underway, but the direction is clear. According to the industry, 2026 is the year of stabilisation, with pricing models likely to consolidate around hybrid models balancing flexibility with predictability. With AI features now standardising across offerings, many software enterprises are expected to face new margin pressures, leading to more discipline in pricing and packaging of features. And enterprises that polish their AI-driven and usage-based pricing models will certainly race ahead in building more stable revenue models tied to performance directly.
The message is clear: with pricing now forming a fundamental part of product strategy, those treating it so will lead the next era of SaaS growth.
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