IT hurtles toward the ‘Great Enterprise Pricing Reset’

The SaaS and AI software markets have entered an era of pricing upheaval, with some new pricing models that can benefit IT leaders and some that may burn through their budgets.

The global software marketplace may be headed toward a widespread pricing reset, as AI products that compete with traditional SaaS offerings force vendors to rethink per-seat pricing, many observers say.

Pricing for traditional SaaS products is trending toward outcome-based billing, which is generally good news for CIOs and CFOs. At the same time, however, pricing for some AI tools, and some SaaS packages, is moving toward consumption-based billing, which can lead to huge invoice surprises if IT leaders don’t keep a close eye on employee usage.

CIOs should expect major pricing changes — and the need to deal with pricing uncertainty — as software vendors respond to competition and market demand, says Sidharth Ramsinghaney, director of strategy and operations at CRM data platform provider Twilio.

“The most immediate problem is budget volatility that most organizations have never had to manage before,” he says. “The shift transfers forecasting risk from vendor to buyer.”

At least 40% of enterprise SaaS spending will shift toward usage-, agent-, or outcome-based pricing by 2030, Gartner has predicted, with seat-based vendor revenue share declining from 21% to 15%. As recently as late 2025, however, some SaaS vendors were hiking prices.

AI agents, in particular, have the potential to disrupt pricing models, Ramsinghaney says. “That projection holds if AI agents continue replacing human task execution,” he adds. “AI agents have decoupled labor from value, making per-seat pricing obsolete when one agent does work that previously required 10 employees.”

However, if agent adoption stalls, per-seat pricing may have more longevity than current predictions suggest, he says, and with uncertainty about the most profitable pricing model, hybrid pricing may become the default.

Questions about outcomes

Meanwhile, even though some customers have pushed for, and some vendors have experimented with, outcome-based pricing, it’s not an easy model to figure out, Ramsinghaney says.

“Pure outcome-based pricing stays aspirational for most categories because outcome attribution is genuinely hard to measure cleanly,” he adds.

Jasper Geurts, CTO at software consulting firm Software Improvement Group, agrees that outcome-based pricing is difficult to implement. Many AI vendors, for example, are combining seat-based pricing with usage, instead of focusing on outcomes, he says.

Vendors will continue to experiment with outcome- and usage-based models, but there’s an ongoing debate over what counts as an outcome, he notes.

 “An agent can pass every acceptance test and still produce code nobody can safely evolve,” he explains. “If you pay only for functional outcomes, you are paying vendors to create technical debt.”

While some SaaS vendors look at outcome-based or hybrid pricing models, it’s a different story with AI vendors, Geurts says. The cost for frontier AI models is going up, not down, with Anthropic’s new Fable 5 model costing double the per-token price of Opus 4.8, he notes.

“CIOs tell me the token economy is opaque,” he says. “You cannot forecast a bill when agents decide how many tokens to burn. CIOs should treat tokens like cloud spend a decade ago: Meter it, govern it, tie it to outcomes.”

Still, token pricing isn’t fixed, with both OpenAI and Anthropic considering price cuts to better compete against each other as both move toward IPOs.

This pricing upheaval creates new headaches for IT leaders, Geurts says. Spending becomes harder to predict, and the outputs harder to control.

“Per-seat pricing capped the bill at headcount,” he adds. “Usage pricing scales with consumption, and as AI moves from assistants to autonomous coding, code volume outgrows what any architecture team can review. The code itself is a liability you keep paying for.”

At the same time that token usage pricing has increased, subscription pricing for AI tools has risen as well, according to a study from Lorka AI, provider of an AI-based writing and research tool.

AI tools that were free or low cost in 2023 now average between $20 and $30 per employee per month, with some premium AI tiers now reaching $200 per month, Lorka AI says. Some enterprises using several AI tools are now spending more than $1,200 per year per employee, often with overlapping functionality between products, according to the company.

Vigilance needed

All of this pricing uncertainty means that IT leaders need to be extra vigilant about software costs, experts say.

CIOs will need to pay close attention to both how services are procured and how much they are used, says Maksim Hodar, CIO at software development firm Innowise. CIOs should pay attention to usage analytics so that they can identify cost control measures and determine which pricing model best suits their business cases, he recommends.

CIOs should also conduct pilot programs before scaling the use of new software with new pricing models, he suggests.

“CIO success depends upon your ability to choose a service’s pricing model based on your business process, risk profile, and expected value associated with the service,” he says. “It’s easy to become overwhelmed by trends in the industry and simply go with what everybody else is doing.”

Hodar expects several different pricing models to hang on, with the predictability of per-seat pricing and the flexibility and scalability of usage-based pricing keeping both models around. Outcome-based pricing will also emerge as an alternative as long as vendors and customers can measure the outputs, he predicts.

IT leaders who haven’t yet embraced FinOps practices should now do so, adds Twilio’s Ramsinghaney. CIOs should track token and other usage to verify that features deliver more value than they cost, he recommends.

Those enterprises moving to outcome-based pricing models should enter explicit outcome measurement agreements with software and platform providers, he adds. “Attribution disputes are where these contracts fall apart,” he says. “Outcome-based pricing only scales where both parties can agree on attribution, and in complex enterprise environments where multiple factors drive results, that clean attribution often doesn’t exist.”

Like others, Ramsinghaney sees room for multiple pricing models going forward. Usage-based pricing works for infrastructure and API-first products where buyers build their own applications, while outcome-based pricing wins where results are cleanly measurable, such as resolved support tickets or completed workflows.

“Hybrid wins as the dominant model rather than either pure form, because it balances vendor revenue predictability with buyer cost alignment,” he adds.

Some per-seat pricing will survive as well, he says, but its use will shrink to situations where humans remain the primary users and value is closely tied to headcount.

“Per-seat persists for collaboration tools and applications where software augments human work, rather than replacing it,” Ramsinghaney says. “The misalignment only becomes acute when AI agents start acting as users that don’t occupy seats.”