AI Usage-Based Pricing: Pros, Cons, and Pitfalls

Why the pricing model transforming SaaS could make—or break—your AI product's margins

The $20 customer costing you $40 isn't a hypothetical scenario anymore. It's Tuesday at an AI startup.

Traditional SaaS built empires on a beautiful economic truth: once the software was built, serving each additional customer cost almost nothing. 80-90% gross margins were the norm. Every new subscription was practically pure profit.

AI changed everything.

Now every customer interaction has a price tag. Every prompt consumes tokens. Every AI feature carries inference costs that stack up faster than your finance team can track them. The model that powered a decade of SaaS growth doesn't work when your cost of goods sold looks more like a manufacturing business than a software company.

The response? Usage-based pricing. And 61% of new B2B SaaS products are now exploring it, according to OpenView Partners' 2024 research.

But here's what nobody tells you until it's too late: usage-based pricing isn't a magic solution. It's a double-edged sword that can either save your margins or slice them to pieces—depending entirely on how you implement it.

What Is Usage-Based Pricing?

Usage-based pricing (also called consumption-based pricing) charges customers based on actual product usage rather than flat subscription fees. Instead of paying a fixed monthly rate regardless of consumption, costs scale with activity.

For AI products, this typically means charging per API call, per token processed, per document generated, per compute hour consumed, or per outcome delivered. Over 60% of SaaS and AI companies have now adopted some form of usage-based pricing.

The logic is straightforward: if AI features cost you money every time a customer uses them, you should pass those costs through proportionally. Revenue scales with consumption. Costs and income stay aligned. By 2024, 25% of SaaS companies already used usage-based pricing, and another 22% were implementing hybrid models combining subscriptions with consumption elements.

In practice, it's far more complicated.

The Pros: Why Usage-Based Pricing Is Gaining Ground

1. Margin Protection in a Variable-Cost World

The math behind usage-based pricing for AI is defensive, not offensive.

A 2025 industry report found that 84% of enterprises saw gross margin erosion exceeding 6% due to unmetered AI infrastructure costs. When you bundle unlimited AI features into a flat subscription, you're essentially betting against your own customers using your product. The more value they extract, the more money you lose.

Usage-based pricing breaks this pattern. If a customer runs 10,000 AI operations, they pay for 10,000 operations. Your revenue tracks your costs.

2. Lower Barriers to Entry

Usage-based pricing dramatically reduces the initial commitment required from new customers. A startup testing your AI tool might spend $50 in their first month, scaling to $5,000 as they grow and see value.

This model aligns naturally with product-led growth strategies. Customers can experiment, validate ROI, and expand usage without negotiating new contracts or committing to seats they may not need.

Research from Chargebee shows that companies with usage-based models achieve 29.9% year-over-year growth compared to 21.7% for traditional subscription models.

3. Higher Net Revenue Retention

When pricing scales with usage, customers who get more value automatically pay more—without requiring sales intervention.

Companies with usage-based models report net revenue retention rates around 125%, compared to 110% for pure subscription models. Expansion happens organically as customers increase their consumption.

4. True Value Alignment

Per-seat pricing made sense when software was a tool humans used. AI changes that equation. When one person with an AI agent can do the work of ten, charging per seat means your revenue contracts by 90% while the value you deliver increases.

Usage-based pricing ties revenue to work performed rather than humans logged in. The more your AI accomplishes, the more you earn.

The Cons: Where Usage-Based Pricing Falls Apart

1. Revenue Unpredictability

CFOs hate surprises. Usage-based pricing delivers them monthly.

Customer consumption patterns fluctuate with seasons, campaigns, market conditions, and factors entirely outside your control. Research shows that SaaS companies without AI-powered forecasting tools face prediction errors of 20-40%, and more than 60% identify revenue predictability as a major operational challenge.

When your board asks for next quarter's revenue forecast, "somewhere between $200K and $400K depending on how much our customers use the product" isn't the answer they're looking for.

2. Customer Anxiety and Bill Shock

Usage-based pricing shifts financial risk to customers. They don't know what they'll pay until the bill arrives. This uncertainty can suppress adoption—customers may avoid using features they need because they're afraid of the invoice.

One analysis found that email marketing platforms using usage-based models regularly see customers experience 30-50% billing fluctuations in a single quarter during promotional campaigns. Without real-time dashboards and spending alerts, confusion and frustration follow.

3. Enterprise Procurement Friction

Enterprise customers need predictable budgets. Their procurement processes are built around annual commitments with defined spending limits. A variable bill that could be $20,000 or $200,000 doesn't fit into how large organizations buy software.

A McKinsey survey found that 65% of enterprise purchasing decision-makers consider the ability to exchange usage commitments between products "very or extremely important." They want flexibility, but flexibility within predictable bounds.

4. Billing Infrastructure Complexity

Counting API calls sounds trivial until you're processing millions of events daily. You need accurate tracking, monthly counter resets, proper handling of retries and failed requests, idempotency across distributed systems, and logic to handle edge cases without double-billing or missed charges.

Most billing systems were built for seat-based subscriptions. Bolting usage metering onto legacy infrastructure creates technical debt that compounds over time.

The Pitfalls: What Can Destroy Your Margins

1. Pricing for Average When Costs Are Anything But

A customer support platform charges $0.99 per resolved ticket. Simple questions cost $0.04 in AI resources. Complex issues cost $2.80. The average margin looks healthy at around 60%—but the customers who engage most heavily generate losses on every interaction.

AI agent costs aren't predictable like traditional API calls. Simple queries need one LLM call. Complex ones trigger research, access memory, execute multiple reasoning steps, and generate detailed responses. The cost variance can be 70x between the cheapest and most expensive operations.

Pricing based on averages when your cost distribution has a fat tail is a recipe for margin erosion concentrated in your most engaged customers.

2. Wrong Metric Selection

The metric you charge for determines everything. Get it wrong, and you either leave money on the table or push customers away.

Mixpanel famously had to change its pricing metric from raw events tracked to users tracked because the original metric didn't align with how customers perceived value. The right metric should be correlated with your costs, aligned with customer value, intuitive for customers to understand, and shouldn't discourage natural product usage.

Charging per token when customers think in documents or outcomes creates confusion. Charging per outcome when costs scale per token creates margin risk.

3. Insufficient Cost Visibility

You cannot price what you cannot measure.

If you don't know which customers are profitable, which features consume the most resources, which usage patterns predict margin erosion, and which workflows are cost sinks versus profit centers, you're flying blind while thinking you're data-driven because you're "usage-based."

One report found that naive usage-based pricing without proper tracking correlates with churn rates as high as 70% and significant margin erosion.

4. Ignoring the Hidden COGS of AI

Traditional SaaS COGS primarily involve hosting, storage, support, and professional services. AI fundamentally disrupts this equation.

Per-token API charges for inputs and outputs (often priced differently), consumption variability per user and workflow, model selection costs, orchestration overhead, and infrastructure for retrieval, memory, and reasoning all add up. Research from a16z suggests AI inference costs can be 5-10x higher than traditional computing costs for equivalent functionality.

A B2B SaaS company added an AI chatbot as a premium feature at $15/user/month flat rate. They discovered too late that power users generated costs exceeding $50/month. The feature meant to boost margins became a profitability drain.

5. Flat-Rate Thinking in a Variable-Cost World

Many companies default to cost-plus pricing based on computational resources—add a margin to your AI provider's token costs and call it a day.

This approach fails to capture the business value delivered. If your AI saves a customer 100 hours of work, pricing based on the tokens consumed ignores the outcome entirely. You leave money on the table while competing on infrastructure costs against providers with deeper pockets.

How to Navigate Usage-Based Pricing Successfully

Embrace Hybrid Models

Pure usage-based pricing optimizes for infrastructure efficiency when the world demands outcome delivery. Pure subscription pricing ignores variable costs.

The winning approach for most AI products combines both: a platform fee providing predictable base revenue plus usage-based components that scale with consumption. According to 2025 pricing research, companies using hybrid models (platform fee plus usage) achieved 21% median growth rates, outperforming pure models on either extreme.

Build Cost Attribution Infrastructure

Before implementing usage-based pricing, you need infrastructure to track LLM calls across providers, attribute costs to specific workflows and customers, monitor tokens in real time, calculate per-interaction margins, and catch cost spikes before they destroy profitability.

Without this foundation, you're guessing.

Match Metrics to Value

Your pricing metric should reflect how customers experience value, not how you experience costs.

If customers think in "documents processed," price per document—even if your costs are per token. Build the translation internally and absorb the variance through margin buffers or tiered pricing that accounts for document complexity.

Provide Spending Visibility

Give customers dashboards, usage alerts, and spending controls. Transparency builds trust and prevents the bill shock that drives churn. Real-time visibility into consumption helps customers budget and plan, reducing anxiety about variable pricing.

Protect Margins with Intelligent Tracking

The difference between AI companies running at 25% gross margins and those achieving 60%+ isn't just pricing strategy—it's operational visibility. Bessemer's research shows this gap comes down to infrastructure maturity and pricing sophistication.

The Bottom Line

Usage-based pricing isn't inherently good or bad. It's a tool—and like any tool, its effectiveness depends entirely on how you use it.

For AI products, the question isn't whether to adopt usage-based elements. Variable costs demand variable revenue structures. The question is how to implement them without sacrificing revenue predictability, customer trust, or the margins that make your business viable.

The $20 customer costing you $40? That's a visibility problem, not a pricing model problem.

Solve the visibility, and the pricing follows.

Building AI features into your product? tknOps provides the cost attribution and margin visibility that makes intelligent pricing decisions possible. See exactly what each customer costs you—before the invoice arrives.