How AI is changing the way software companies set their prices

AI is transforming software pricing into hybrid, usage-based models

The software business is experiencing one of its biggest shifts in years. Artificial intelligence isn’t just changing how products are built, it’s changing how they’re sold. Traditional pricing models built on fixed monthly fees or per-user charges no longer match the way AI creates value. The data shows this shift clearly: Solvimon reports that usage-based charges have increased sevenfold since 2025, and the most advanced software and AI companies now employ roughly five distinct pricing models on average, up from three last year. A Bain & Company study found that 65% of established SaaS vendors have already integrated hybrid pricing by adding AI usage or outcome metrics into existing frameworks.

This is a sign of how quickly the industry is adapting. The old approach worked when software consumption followed predictable patterns tied to user count or access rights. But AI tools generate real-time value through computation, transactions, or data processing. A fixed-price model can’t capture that. The emerging trend is to mix subscription reliability with metered billing that reflects actual usage or results. This evolution helps companies align revenue with value, and it’s reshaping entire financial systems behind the scenes.

Hybrid pricing enhances revenue accuracy, but it also increases system complexity. Managing multi-layered structures requires flexible, real-time infrastructure that can automatically calculate, reconcile, and report on countless micro-transactions. Those who still rely on static billing setups will face integration bottlenecks and lost revenue opportunities. Leaders should treat billing modernization as a core strategic initiative, not a technical afterthought. Companies skilled in designing adaptive pricing frameworks are already building stronger customer trust while safeguarding long-term growth.

Pricing complexity is straining financial systems and risking revenue leakage

As pricing models evolve, financial systems are starting to show their limits. It’s no longer just about billing for seats or flat fees. Companies now manage combinations of credits, tokens, outcome-based measures, and consumption-based pricing, all within one system. For finance teams, that complexity multiplies the risk of errors and makes it harder to capture what the business has truly earned. Reconciling usage data, contract terms, and invoices across multiple entities and geographies is now one of the toughest operational challenges in software.

The effect on revenue is measurable. Research from MGI shows that recurring billing errors can lead SaaS companies to lose between 1% and 5% of annual recurring revenue each year, leakage that persists across billing cycles until detected. That’s a meaningful amount for any business and can scale into millions in missed earnings for larger enterprises. This problem is happening in real time as companies expand their product lines and pricing structures.

For CFOs and other financial leaders, billing has become a strategic infrastructure issue. Treating it as simple back-office software is no longer viable. The future of accurate, real-time billing depends on stronger integration between product, finance, and data systems. Those who invest in this infrastructure will move faster, capture more upside, and build deeper accountability across their organizations. Those who don’t will likely face shrinking margins and delayed revenue recognition.

Executives should also understand that the goal is more than error prevention, it’s intelligence. Modern billing systems can provide live data on how each customer segment interacts with pricing, how revenue scales with usage, and which configurations drive the highest margins. This level of transparency helps companies make confident, data-backed pricing decisions at speed. In today’s AI-driven landscape, precision in pricing and billing is becoming as strategic as the technology itself.

Solvimon is emerging as a pivotal solution provider for modern, complex billing needs

The rise of AI-driven pricing has opened up a new frontier for financial infrastructure, and Solvimon is one of the few companies tackling it head-on. Founded in 2022 by former Adyen executives Kim Verkooij and Etienne Gerts, Solvimon was built to address the operational challenges of hybrid billing in the AI era. The company’s platform integrates pricing logic, quote management, invoicing, payment collection, and revenue recognition into a single system. Its core strength lies in translating complex product usage data into precise, real-time billing outcomes, something traditional systems fail to do at scale.

Kim Verkooij, Solvimon’s Co-founder and Chief Executive Officer, puts it plainly: “Pricing models are more complex because of AI, and you can no longer price on a per seat basis as the risk of revenue leakage grows from unbilled usage to misconfigured contracts.” Her perspective reflects a reality that growing enterprises are now confronting: billing inaccuracies caused by system fragility and data misalignment can undermine both profitability and trust. At Adyen, the founders processed billions in annual transactions. Now, they are applying that experience to enterprise billing, ensuring that companies can recover every unit of earned revenue.

For executives, Solvimon represents more than just tools for automation, it’s a model for strategic resilience. In an environment where product innovation moves faster than traditional finance infrastructure, Solvimon enables companies to reconcile that pace. Its clients, ranging from mid-sized SaaS firms to fast-scaling AI services, use it to eliminate manual errors, speed up closing processes, and create visibility across multi-country and multi-entity operations.

Decision-makers should take note of how quickly revenue operations are shifting from manual oversight to continuous intelligence. Billing systems that adapt in real time can now define whether a business scales efficiently or gets bogged down by operational friction. Solvimon’s approach signals where the industry is heading: a future where pricing precision is an integral part of competitive advantage, not just a financial necessity.

AI-based products introduce variable costs that challenge traditional fixed pricing structures

AI products operate under a different cost model than legacy software. They require ongoing compute power, GPU processing, and data handling that fluctuate based on application use. That reality makes the concept of fixed monthly pricing obsolete. Instead, these variable costs push companies to align billing with direct consumption, ensuring that revenue reflects actual usage and that operating costs stay transparent.

For many AI businesses, these infrastructure expenses aren’t minor, they scale with demand. This means pricing must dynamically respond to both resource usage and value delivered. Static per-user charges simply can’t account for variations in compute processing or inference volume. The shift toward usage-based or credit-based billing reflects the need to tie every financial input and output more precisely to actual cost drivers. It’s a pragmatic step, not a trend.

Executives need to approach this shift with clear operational readiness. Moving from static to variable billing introduces volatility that can affect both forecasting and customer relationships. Finance and operations teams must have accurate data pipelines capable of metering, tracking, and reporting all consumption at fine-grained levels. This infrastructure is essential not only to prevent revenue leakage but also to support transparent, trustworthy billing relationships with customers.

The leaders who manage this transition effectively stand to benefit beyond simple revenue alignment. Variable pricing models create tighter bonds between user needs and pricing fairness. They also make it easier for companies to scale globally, since consumption data is consistent and adaptable across currencies, markets, and contract models. As AI continues to push computing costs higher, those that can precisely measure and reflect those costs in real time will stay ahead of their competitors, financially and operationally.

Real-world customer implementations underscore the shift toward consumption-based billing

Real business use cases now confirm that hybrid and consumption-based pricing models aren’t theoretical, they’re already redefining how AI companies manage revenue. A clear example is Reson8, a provider of speech recognition software serving industries such as healthcare and finance. The company bills its customers by the minute, using credit-based pricing across varied workloads. This structure connects billing directly to how customers use the product, producing transparent and predictable revenue capture.

Raoul Ritter, Co-founder and Chief Executive Officer of Reson8, explained this operational necessity: “When you’re running your own GPU cluster and inference stack, and selling speech by the minute, usage-based billing is core to how you operate. As we rolled out credit-based pricing across different workload types, we needed a system that could evolve alongside the product.” His remarks highlight a reality facing most AI providers today, billing precision must scale with the complexity of product usage. Missing or inaccurate usage data translates directly into uncollected revenue, something modern billing systems are designed to eliminate.

For executives, these practical implementations offer more than insight into how billing innovation works, they set a standard for future readiness. Customers expect clarity on what they’re paying for and how pricing reflects actual use. Businesses that move decisively toward consumption-based billing will establish stronger trust and operational alignment with clients while gaining more flexibility in how they manage product growth.

It’s also a financial evolution. Linking billing directly to measurable inputs like usage or computation ensures that profits rise in proportion to real demand. This approach reduces the mismatch between operating costs and income, creating stability as AI models scale. The companies that master automated, transparent billing will be the ones that thrive as the complexity of digital transactions continues to increase.

Investors are increasingly prioritizing advanced billing infrastructure as a strategic asset

Investment interest in billing modernization is growing fast. As companies expand internationally and adopt AI-driven business models, the importance of accurate, transparent revenue systems has intensified. Investors are identifying billing infrastructure as a crucial growth enabler, something that directly impacts scalability, customer retention, and revenue integrity. It’s no longer considered just a back-office system but a central pillar of enterprise strategy.

Esen, representing investor Northzone, emphasized this belief, stating, “Solvimon is now expanding globally, entering its next phase of growth with proven infrastructure, a validated customer base, and expanding pipeline for AI and SaaS companies… We believe it is solving one of the most important infrastructure and monetisation challenges enterprises will face in this decade.” This perspective captures what the investment community is seeing: as product portfolios diversify and pricing grows more complex, companies with advanced billing engines are better prepared to scale efficiently and manage international complexity.

For executives, this signals a clear direction. Investors will increasingly favor businesses that modernize billing, since precise revenue tracking lowers operational risk and improves multiples during valuation. Companies demonstrating strong billing governance are signaling operational maturity, the kind that gives confidence to investors looking for scalable, resilient technology portfolios.

The broader takeaway for leaders is straightforward: in the age of AI-driven value creation, the quality of a company’s billing infrastructure reflects its overall execution capability. Organizations that treat billing as a strategic asset, one that enables flexibility, financial insight, and long-term monetization, will be positioned to lead, not follow, as the next wave of digital transformation unfolds.

Key executive takeaways

• AI reshapes pricing models: AI is driving software companies toward hybrid, usage-based pricing that better reflects real consumption and value creation. Leaders should modernize pricing strategy and supporting systems to keep pace with this evolution.
• Billing complexity demands precision: Hybrid billing structures increase risk of errors and lost revenue. Executives should invest in real-time financial infrastructure to ensure accurate revenue capture and prevent leakage.
• Solvimon sets the new standard: Solvimon, founded by former Adyen executives, is building infrastructure tailored for AI-era pricing. Decision-makers should view advanced billing platforms as strategic tools to enable scalability and financial accuracy.
• Variable AI costs require dynamic models: Fluctuating compute and data costs make fixed pricing inefficient. Leaders should adopt data-driven billing tied to usage to keep margins healthy and resource allocation transparent.
• Real-world adoption proves the model: Companies like Reson8 are using consumption-based billing to link usage directly to revenue. Executives should follow this lead, implementing flexible pricing that aligns value delivery with payment.
• Investors see billing as strategic infrastructure: Investors are prioritizing companies with robust, intelligent billing systems that protect revenue during rapid growth. Business leaders should treat billing modernization as a core driver of long-term enterprise value.