Table of Contents
AI adoption has skyrocketed, but the pricing behind it remains inconsistent, complex, and rapidly evolving. From bundled enterprise software to standalone AI-native tools, businesses are navigating a crowded market without clear benchmarks. While some tools promise low-cost access, others introduce hidden fees tied to usage, integrations, or licensing tiers. As AI becomes embedded into nearly every SaaS platform, understanding the true cost of artificial intelligence is no longer optional—it’s essential to controlling software spend in 2025.
How Much Does Artificial Intelligence Cost?
The short answer: it depends—on everything from the type of tool and deployment method to how usage is measured and monetized. Unlike traditional software with straightforward pricing, artificial intelligence solutions are priced in a variety of ways, from per-user and per-seat models to pay-as-you-go usage-based tiers that charge per prompt, token, or interaction.
AI tools embedded in existing SaaS platforms can be deceptively expensive. For example, Microsoft Copilot now adds up to $30 per user per month to Microsoft 365 subscriptions, while Google has increased Workspace prices but bundled AI tools at no additional cost. Meanwhile, enterprise-focused AI tools often charge premiums for features like enhanced analytics, automation, and security. That makes it difficult to compare options side by side—and even harder to calculate the total cost of ownership.
AI-native apps add another layer of complexity. Spending on AI-native applications rose by 75.2% year over year, signaling a rapid shift toward integrating AI tools into the enterprise stack. At the same time, 70% of SaaS spend now originates from lines of business rather than IT, which increases the likelihood that AI tools are purchased outside centralized oversight. This decentralization makes it harder to monitor usage, assess value, and maintain control over total AI spend.
Factors Impacting AI Costs
AI pricing isn’t driven by a single variable. It’s shaped by a mix of licensing decisions, feature packaging, data usage policies, and the infrastructure required to support large-scale models. In 2025, one of the biggest cost variables is whether an AI tool is embedded in a broader SaaS platform or sold as a standalone product.
As IBM CEO Arvind Krishna recently noted, AI adoption is expected to “explode” as infrastructure costs fall. Yet at the same time, pricing is climbing for many enterprise-grade tools due to increased demand, compute intensity, and rising expectations around security. Our 2025 SaaS Management Index reveals that 63% of organizations are already investing in AI applications—and nearly half expect those costs to increase in the coming year.
Enterprises also face inconsistent packaging: some vendors include AI as a value-add, while others use it to justify dramatic price hikes. That inconsistency forces IT and procurement teams to closely monitor not just upfront costs, but ongoing spend and shadow IT risks.
AI Pricing Trends to Watch in 2025
AI pricing models are evolving faster than buyers can keep up. OpenAI’s CEO, Sam Altman, predicted that “AI prices will drop 10x annually,” but we’re seeing the opposite in the short term, especially among enterprise vendors. Microsoft added Copilot to Microsoft 365 and raised subscription prices, while Google adjusted Workspace pricing and embedded AI at no added cost. These contrasting strategies reflect a broader trend: AI is becoming table stakes, but not every vendor agrees on how to price it.
A growing number of vendors are layering usage-based pricing into formerly flat-rate or per-seat models. At SaaSMe 2025, Ben Pippenger noted that 53% of subscription-based businesses now offer some form of usage-based pricing, up from just 31% the year prior (Stripe data). That trend is accelerating as vendors seek to monetize AI through API calls, tickets resolved, prompts submitted, or autonomous task completions.
In addition, Zylo’s data shows that 66.5% of IT leaders have already experienced budget-impacting overages tied to AI or consumption-based pricing. What’s impacting these costs? Here are the top AI pricing trends to keep an eye on this year.
SaaS Premiums on AI-Enabled Features
Many SaaS providers are using AI as a justification for higher pricing tiers. Data shows that nearly half of organizations are paying more for tools with embedded AI features—even when those capabilities aren’t heavily used. These bundled upgrades often push companies into more expensive plans without a clear return on investment.
It’s not just a pricing issue—it’s a transparency issue. Businesses struggle to evaluate whether AI functionality justifies the added expense, especially when the features are only available in top-tier plans or are gated by usage caps.
Rising Investment in AI-Native Applications
Our report reveals another key trend: increased investment in tools that are purpose-built around AI. Individual teams often bring in these AI-native apps, with little oversight from procurement or IT. For the first time since tracking this data, AI apps appear on the most-expensed apps list. When AI-native tools are sourced outside formal channels, the risk of duplicate spend, security gaps, and unmanaged costs increases.
While these tools may offer strong value within specific use cases, they also introduce unpredictable licensing patterns and integration challenges. Organizations need better visibility into who is buying what and why to accurately assess the value and cost of these apps.
Cloud-Based AI Pricing Models
Cloud infrastructure plays a major role in AI pricing. Many AI services rely on cloud compute resources for training and inference, with costs that scale alongside usage. Models hosted on platforms like Azure, AWS, or Google Cloud often use pay-per-use billing tied to API calls, data volume, or inference time. This makes budgeting difficult without detailed usage monitoring.
According to High Alpha’s 2024 SaaS Benchmark Report (also cited at SaaSMe), nearly half of AI vendors now rely on hybrid pricing models—bundling usage charges alongside flat subscription tiers. These shifts aren’t just theoretical. Real-world licensing surprises—from envelope caps at DocuSign to tier reshuffling at Smartsheet—are hitting companies hard.
For enterprise buyers, that means AI tools aren’t just line items—they’re usage drivers that can trigger broader cloud overages. Without proper visibility, a spike in AI activity could inflate tool costs and total cloud spend.
Open-Source AI and Cost Implications
While open-source AI tools are often viewed as a low-cost alternative, that can be misleading. While the code may be free, the infrastructure, talent, and security required to run open-source models can be significant. Organizations need in-house expertise to deploy, scale, and secure open models—costs that aren’t reflected in a traditional licensing model.
Additionally, open-source models may lack the governance features found in commercial tools, increasing the risk of compliance violations or inconsistent usage across teams.
Subscription vs. One-Time Payment Models
Some AI providers offer traditional subscription models, while others price their tools as one-time purchases or perpetual licenses. Subscriptions may seem more manageable month-to-month, but they can add up over time, especially if user seats go unused or renewals occur without proper evaluation.
One-time payment models can offer upfront savings but often lack access to updates, support, or model retraining. Companies must balance cost predictability with long-term utility and support when evaluating AI investments.
Increasing Complexity in AI Licensing
Licensing structures are becoming harder to navigate. AI-related features constantly introduce new tiers, licensing structures, and usage gates. This is being compounded by increasingly unpredictable pricing shifts. Zylo co-founder Ben Pippenger pointed to examples like Smartsheet and DocuSign, where mid-contract licensing model changes created significant confusion at renewal time.
“Every app has its own flavor,” he said, highlighting how even major vendors like Salesforce and Zendesk now layer usage-based fees onto traditional per-seat contracts. Some charge by autonomous actions (e.g., closed tickets), others by task or compute unit. This rapid proliferation of billing models makes forecasting difficult and ROI measurement even harder.
This complexity doesn’t just affect procurement—it also creates barriers to adoption, visibility gaps, and exposure to shadow AI risk. As AI features become embedded across more tools, buyers need more than just price lists. They need clear standards for what’s included, how it’s billed, and what overages may apply.
AI Pricing Models
AI software pricing is anything but uniform. Vendors are experimenting with a wide variety of models to align revenue with perceived value, resource usage, and enterprise willingness to pay. The lack of standardization makes it harder for buyers to compare options or forecast long-term spend, especially as many tools combine multiple models into a single contract.
Value-Based Pricing
Value-based pricing ties the cost of AI software to the business outcomes it enables. Rather than charging for access or usage, vendors set prices based on perceived ROI, such as hours saved, revenue generated, or productivity gains. While this model can align pricing with impact, it often requires custom contracts and post-sale justification. This adds unnecessary complexity to procurement and renewals.
Usage-Based Pricing
Usage-based pricing is rapidly becoming the default for many AI-native platforms. Charges are typically based on metrics like tokens consumed, prompts submitted, or API calls made. While this approach offers flexibility and scalability, it can also create volatility in monthly billing. Without proactive monitoring, usage can balloon quickly, especially when multiple teams interact with the same tool.
Subscription-Based Pricing
Many enterprise AI solutions are still packaged as standard SaaS subscriptions, with monthly or annual billing tied to seat counts. This model offers predictability but may obscure actual usage. Organizations often overpay for unused or underused seats, particularly when AI features are bundled into top-tier plans.
Freemium Models
Freemium models offer basic AI functionality at no cost, with advanced features gated behind a paywall. This approach lowers adoption barriers and encourages experimentation, but can introduce shadow IT risk if free versions are adopted outside of formal review processes. IT teams should track adoption to avoid surprise costs when teams upgrade to paid tiers.
Flat-Rate Pricing
Flat-rate pricing gives businesses unlimited access to AI features for a fixed fee, typically within a defined scope of use. It can simplify budgeting, but often lacks flexibility for companies with fluctuating usage patterns. For growing organizations, flat-rate models may also become less economical over time compared to metered options.
License Fee Models
License fee models involve a one-time payment or recurring license cost to access AI software. These agreements typically include maintenance and support but may not cover ongoing training or updates—key needs for rapidly evolving AI tools. Without flexibility, this model can become outdated quickly in fast-moving AI environments.
Performance-Based Pricing
Under performance-based models, customers pay based on predefined success metrics. For AI tools driving sales, conversions, or customer satisfaction, vendors may charge a percentage of gains or bonuses based on outcomes. This aligns incentives but can be hard to measure consistently across industries or use cases.
Hybrid Pricing Models
Many enterprise AI vendors now use blended pricing—combining elements of subscription, usage-based, and value-based models. For example, a base subscription may include a fixed number of prompts or seats, with overages billed separately. These hybrid contracts offer flexibility but can complicate forecasting and procurement workflows.
Labor Replacement Pricing
Some vendors frame AI pricing around headcount savings. If an AI tool replaces a manual task previously handled by staff, its pricing may reflect a percentage of the salary it offsets. While intuitive, this model risks oversimplifying cost comparisons and may not hold up to detailed ROI analysis.
Outcome-Based Pricing
Outcome-based pricing ties payments to specific business goals, such as resolving a set number of support tickets or hitting defined productivity targets. This model requires precise tracking and clear SLAs, making it more common in high-touch enterprise contracts than off-the-shelf AI platforms.
Blended Pricing
Blended pricing combines two or more pricing strategies into a single rate. For example, a tool might charge a fixed monthly fee plus a smaller variable cost based on token usage. This model gives vendors revenue stability while allowing for some flexibility in scaling with usage.
Tiered Pricing
Tiered pricing offers a menu of plans with increasing features and limits at each level. Common in SaaS, this structure allows buyers to choose a plan that fits their needs—but often nudges them toward higher tiers to unlock AI functionality. This structure can inflate costs if AI is only available at premium levels.
Agentic Seat Pricing
With the rise of AI agents, some vendors now charge by “agentic seat”—a licensed instance of an AI agent that performs autonomous tasks. This model reflects a shift from user-based pricing to task-based pricing, and introduces new variables in terms of value measurement and cost justification.
Cost-Plus Pricing
Cost-plus pricing sets the price of an AI tool based on its development or compute cost, plus a markup. While straightforward, this model is less common in AI due to the variable and often opaque costs behind model training and deployment.
Competitive Pricing
Competitive pricing aligns an AI tool’s cost with similar products on the market. Vendors often benchmark against rivals to avoid pricing themselves out of consideration. While this benefits buyers, it doesn’t always reflect differences in model performance, integrations, or enterprise-readiness.
Penetration Pricing
Some vendors use penetration pricing to quickly capture market share—offering low prices initially, then raising them as adoption grows. This model can lure companies in with discounts, but buyers should be cautious of future increases, especially if switching costs are high or contracts auto-renew.
Factors Impacting the Cost of AI
While pricing models define how vendors charge for AI, the actual cost of deploying and managing AI hinges on several key factors. These factors span from the intrinsic AI features to the external requirements that drive the overall investment.
AI Features and Functionalities
The scope and sophistication of the AI capabilities play a pivotal role in determining costs. More advanced functionalities—such as natural language processing, image recognition, or autonomous decision-making—require extensive training, refined algorithms, and continuous updates. As a result, these features typically command higher prices than simpler automation tools.
Project Type and Scope
Different types of projects incur varying costs. A tailored AI solution designed to address specific business challenges will generally be more expensive than an off-the-shelf product. Custom integrations, bespoke model training, and specialized tool development add layers of complexity that increase both development and maintenance expenses.
Data Accessibility and Quality
High-quality, accessible data is the lifeblood of any AI initiative. The costs associated with data acquisition, cleaning, and preparation can be significant. Poor-quality data or restricted access often requires additional investments in data engineering resources, ultimately impacting the total cost of an AI project.
Labor and Expertise
Developing, deploying, and managing AI systems demand specialized skills. Hiring or contracting data scientists, machine learning engineers, and domain experts can be expensive. Furthermore, the ongoing need for training teams, keeping up with rapidly evolving technologies, and integrating AI into existing workflows contributes to higher operational expenses.
Infrastructure and Compute Resources
AI applications are resource-intensive, requiring significant computational power for tasks like model training and real-time inference. Investments in hardware, cloud services, and scalable infrastructure will directly affect the cost. Decisions between using on-premise solutions versus cloud-based platforms add another layer of cost variability based on anticipated usage and scalability needs.
Regulatory and Compliance Costs
As AI becomes more integrated into core business processes, regulatory compliance grows in importance. Depending on the industry, companies might need to invest in additional cybersecurity measures, audit trails, and data privacy tools to meet legal standards. These regulatory requirements affect upfront development and necessitate ongoing investment in system upgrades and compliance monitoring.
Project Duration and Management
The timeline of an AI project—from development through long-term management—also influences cost. Prolonged projects often experience budget creep due to evolving requirements or unforeseen complications. Furthermore, managing and maintaining AI systems over the long term requires consistent oversight, periodic retraining of models, and regular maintenance updates.
How the Cost of AI Is Impacting Business Revenue
As AI adoption increases, so does the pressure to prove its financial value. For many organizations, AI investments have led to improved productivity, faster decision-making, and new customer-facing capabilities. But these gains often come with rising software and infrastructure expenses that cut into margins.
Some companies face a widening gap between the cost of AI tools and their measurable impact. Without proper governance, teams may adopt overlapping tools, underuse premium AI features, or invest in models that never reach production. That’s why AI cost visibility is now central to protecting revenue—and why businesses are turning to SaaS visibility and inventory management solutions to regain control.
AI Costs in Relation to Business ROI
The ROI of AI varies widely depending on how it’s implemented. Organizations that take a strategic, centralized approach are more likely to see measurable returns—whether through automation savings, improved forecasting, or reduced manual effort. However, decentralized adoption can lead to fragmented spending, unused licenses, and inflated costs that undermine ROI.
Many companies are still in the early stages of tracking AI return on investment. Without centralized oversight, it’s difficult to evaluate which tools are worth the spend. SaaS governance and risk mitigation play a vital role here, offering the structure needed to manage AI investments at scale.
Predictions for the Future of AI Pricing
The cost of AI is expected to change dramatically over the next few years. Experts like Sam Altman have predicted that prices will fall tenfold each year, driven by innovation and model efficiency. IBM’s CEO recently said AI adoption will “explode” as costs continue to drop. But that future hasn’t arrived yet—at least not for enterprise buyers.
In the near term, pricing will likely remain inconsistent. Some vendors will continue bundling AI into high-tier SaaS packages, while others will push usage-based models that make monthly spend harder to predict. The growing role of AI-native tools also suggests that businesses must rethink their cost benchmarks entirely.
The Role of Innovation in Cost Reduction
Newer AI models are becoming cheaper to train and deploy thanks to innovations in model architecture, distillation, and compute efficiency. As infrastructure improves and open-source models gain traction, the cost barrier for high-quality AI tools drops. This is especially true for companies that run models in-house or on optimized cloud platforms.
Effects of Market Competition
Market saturation is already pushing some vendors to revise their pricing strategies. Google, for example, raised Workspace prices while offering AI features at no additional cost. Microsoft, by contrast, introduced Copilot as a premium add-on. As more vendors compete for enterprise attention, expect new pricing experiments—and more flexible models designed to capture share.
Influence of Global Economic Factors
Macroeconomic trends will continue to shape AI pricing in 2025. Cloud compute costs, talent availability, and global supply chains all influence how vendors structure pricing. Economic pressure could push buyers to reduce AI spend or renegotiate contracts, particularly if ROI remains unclear. Vendors may respond with new models that shift the cost burden away from upfront fees toward performance-based or modular pricing.
AI Pricing and Compliance Pressure
Increased regulatory scrutiny around AI ethics, transparency, and data usage may also affect pricing. Vendors that invest in compliance tools, explainability, and audit trails may charge a premium for those features, especially in industries like finance or healthcare. Buyers must factor in these hidden costs when evaluating long-term AI investments.
Zylo Helps You Understand the True Cost of AI
Understanding how AI is priced—and how those prices impact total software spend—is now essential to managing enterprise IT. Yet most organizations still struggle to track AI-specific costs across tools, platforms, and teams.
Zylo gives businesses the visibility and control they need to manage AI spend strategically. From tracking adoption of AI-native apps to analyzing usage patterns inside bundled SaaS platforms, Zylo equips IT, procurement, and finance teams to make smarter, faster decisions.
But we don’t just deliver data—we partner with our customers. With deep expertise in SaaS Management and a consultative approach tailored to each organization, Zylo helps you navigate the fast-changing world of AI with confidence. Our team works closely with key stakeholders to uncover hidden costs, align spend with value, and safeguard your SaaS budget.
Learn how Zylo can help you gain control over AI costs and protect your SaaS budget.