For decades, SaaS companies had a simple pricing model:

Pay $99 per month.

It was predictable. It was easy to understand. And for a long time, it worked extremely well.

But AI is changing the economics of software.

The cost of delivering an AI-powered product can vary dramatically depending on how much a customer actually uses it. One customer might make a few hundred AI requests each month. Another might generate millions.

Charging both customers the same fixed subscription price doesn't always make economic sense.

That's why usage-based pricing is becoming one of the most important business-model shifts in SaaS—and especially in AI.

Instead of charging customers primarily for access to software, companies can charge based on the value or consumption generated by the software.

And I believe this will become the dominant pricing model for a significant portion of the AI software industry.

What Is Usage-Based Pricing?

Usage-based pricing is a pricing model where customers pay based on how much of a product or service they actually consume.

Think of it like electricity.

You don't pay your electric company simply for having electricity available.

You pay based on how much electricity you use.

The same principle can apply to software.

Instead of:

$199/month for unlimited usage

you might have:

$0.02 per transaction

or:

$10 per 1,000 AI requests

or:

$0.05 per document processed

or:

$1 per completed AI workflow

The customer pays more as they get more value.

That creates an important alignment between customer success and vendor revenue.

When the customer grows, the software company grows with them.

Why Usage-Based Pricing Matters More for AI

Traditional SaaS has relatively predictable marginal costs.

If you have 1,000 customers using a CRM, adding another customer doesn't necessarily create a dramatic increase in your infrastructure costs.

AI is different.

AI applications often rely on third-party models and infrastructure.

An AI product may depend on:

• OpenAI

• Anthropic

• Google Gemini

• Perplexity

• ElevenLabs

• Image-generation models

• Vector databases

• Cloud infrastructure

• Other specialized AI APIs

Those providers frequently charge based on consumption.

That means an AI company can have a customer whose usage costs $10 one month and $1,000 the next.

If the AI company charges that customer a flat $99 subscription, the economics can break very quickly.

This creates a fundamental problem:

Your revenue is fixed while your costs are variable.

Usage-based pricing can help solve that problem.

The Problem With "Unlimited"

"Unlimited" sounds great to customers.

But it can be dangerous for AI companies.

Imagine an AI SaaS company charges:

$199/month unlimited

A customer uses the product lightly.

The company spends $20 providing the service.

Great margin.

Then another customer discovers a powerful automation and starts running it continuously.

Their AI consumption costs the company $600.

The customer still pays $199.

The more successful the customer becomes, the more money the SaaS company loses.

That's backwards.

In a traditional SaaS environment, "unlimited" may be manageable because incremental usage costs are relatively low.

In an AI environment, unlimited usage can create unlimited exposure to variable costs.

That's one reason I believe AI will accelerate the adoption of usage-based pricing.

Usage-Based Pricing Aligns Revenue With Value

The strongest argument for usage-based pricing isn't simply cost control.

It's value alignment.

Suppose you're selling an AI system that automatically qualifies sales leads.

A customer processes 1,000 leads per month.

Another processes 100,000.

If the second customer is generating dramatically more value from the system, it makes sense that they would pay more.

The pricing model grows alongside their business.

That's fundamentally different from traditional seat-based pricing.

Seat-based pricing

You pay for:

How many people use the software.

Subscription pricing

You pay for:

Access to the software.

Usage-based pricing

You pay for:

How much you use the software.

Value-based pricing

You pay for:

The business value the software creates.

AI is pushing SaaS companies toward the last two models.

Usage-Based Pricing Doesn't Mean You Have to Abandon Subscriptions

This is an important distinction.

Usage-based pricing and subscriptions aren't mutually exclusive.

In fact, one of the most effective models for AI SaaS may be a hybrid pricing model.

For example:

Starter

$49/month
Includes 10,000 usage credits

Growth

$199/month
Includes 100,000 usage credits

Enterprise

Custom subscription
Includes negotiated usage

Then customers can purchase additional usage when they exceed their included allocation.

This creates:

Predictable recurring revenue + variable usage revenue.

For the SaaS company, that can create a much healthier relationship between revenue and cost.

For the customer, it provides predictable baseline spending while allowing them to scale.

The Rise of AI Credits

One way companies are implementing usage-based pricing is through credits.

Credits provide an abstraction layer between the customer and the underlying infrastructure.

Instead of forcing a customer to understand:

• tokens

• API calls

• model pricing

• input tokens

• output tokens

• image generation costs

• audio minutes

• vector searches

the company can simply say:

Your plan includes 10,000 AI credits.

The customer consumes credits as they use the product.

This makes the pricing model easier to understand.

It also gives the software company flexibility.

If the underlying AI provider changes its pricing, the SaaS company doesn't necessarily have to redesign its entire customer pricing model.

The credit system becomes the commercial abstraction layer.

But There Is a Problem With Credits

Credits can also create confusion.

If customers don't understand what a credit represents, they can feel like they're buying an arbitrary currency.

That's why transparency matters.

A good AI pricing system should make it easy for customers to understand:

What am I paying for?

How much am I using?

What does my usage cost?

What happens when I exceed my plan?

How much value am I receiving?

The goal isn't to hide complexity.

The goal is to abstract complexity without hiding economics.

Usage-Based Pricing Creates a New Challenge: Metering

Once you move to usage-based pricing, you need to know exactly what customers are consuming.

That sounds simple.

It isn't.

Imagine an AI application that uses:

• GPT for reasoning

• Claude for certain workflows

• Gemini for another process

• ElevenLabs for voice

• Perplexity for research

Now imagine 10,000 customers using different combinations of those services.

You need to understand:

Who used what?

When did they use it?

How much did it cost?

What did you charge them?

What was your margin?

That's where usage metering becomes critical.

The modern AI application increasingly needs something that looks like a general ledger for AI usage.

AI Creates Two Different Types of Usage

One of the concepts we think about at Walleta is the distinction between usage that creates revenue and usage that creates cost.

For example:

Monetization

What you charge your customer.

Consumption

What your customer consumes.

Upstream usage

What your application consumes from AI providers.

Those aren't always the same thing.

A customer might spend $500 in credits inside your application.

But your actual AI infrastructure costs might be $175.

Your gross margin is the difference.

That means AI companies need visibility into both sides of the equation.

Revenue in. AI costs out.

The AI SaaS Margin Problem

Here's the question every AI company should eventually be able to answer:

"How much does this customer actually cost us to serve?"

Not approximately.

Not at the company level.

Customer by customer.

Imagine having a dashboard showing:

Suddenly, your pricing strategy becomes much more obvious.

Customer C isn't necessarily a bad customer.

Maybe they're getting enormous value.

Maybe they simply need a different pricing tier.

Maybe they should pay based on usage.

Maybe a different model could reduce your cost.

The important thing is that you need to know.

Usage-Based Pricing Changes the SaaS Growth Model

Traditional SaaS often focuses heavily on:

Number of customers × subscription price

Usage-based SaaS adds another dimension:

Customers × usage × price per unit

That creates a powerful growth mechanism.

A customer doesn't necessarily need to buy another product or another seat for your revenue to increase.

They simply need to use your product more.

That's particularly powerful for AI because successful AI implementations often become more deeply embedded in business operations over time.

More workflows.

More agents.

More automation.

More transactions.

More data.

More usage.

And therefore:

More revenue.

Usage-Based Pricing Can Create a Better Customer Experience

At first, customers sometimes resist usage-based pricing because they fear unpredictable bills.

That's a legitimate concern.

But a well-designed usage model can actually be more customer-friendly.

Consider two customers:

Customer A

Uses your product $20 worth per month.

Customer B

Uses your product $2,000 worth per month.

Under a rigid subscription model, both might pay $499.

Customer A is subsidizing the platform.

Customer B may be getting a massive bargain.

With usage-based pricing, each customer pays more closely in proportion to their consumption.

That can make the pricing model feel more fair.

The key is predictability and transparency.

Give customers:

• Usage dashboards

• Spending limits

• Alerts

• Forecasts

• Usage history

• Clear pricing

• Budget controls

Usage-based pricing doesn't have to mean surprise bills.

The Future Is Probably Hybrid

I don't believe every SaaS company will abandon subscriptions.

Nor should they.

Different products have different economics.

But I believe we're going to see far more companies combine:

Subscription + Usage + Value

For example:

$299/month platform fee

• included usage

• additional consumption

• premium services

This gives companies predictable baseline revenue while allowing revenue to scale with customer usage.

And it gives customers a clear path to grow without constantly renegotiating contracts.

Usage-Based Pricing Is More Than a Pricing Strategy

This is the bigger idea.

Usage-based pricing changes how a software company thinks about its customers.

You're no longer simply asking:

"How many customers do we have?"

You start asking:

"How are our customers using the product?"

Then:

"Which customers are growing?"

Then:

"Which workflows are creating the most value?"

Then:

"Which workflows are costing us the most?"

Then:

"Where should we invest?"

That's much more powerful data.

Your pricing system becomes part of your product intelligence.

What Companies Need to Build for Usage-Based Pricing

If you're considering moving toward usage-based pricing, you need more than a pricing page.

You need infrastructure.

At minimum, you'll want:

1. Usage Metering

Track exactly what customers consume.

2. Customer Wallets

Maintain balances, credits, or prepaid value.

3. Real-Time Usage

Give customers visibility into their consumption.

4. Cost Tracking

Understand what your upstream providers are charging you.

5. Margin Visibility

Know how profitable each customer, product, and workflow is.

6. Flexible Pricing Rules

Support different pricing models without rebuilding your application.

7. Spending Controls

Allow customers to establish budgets and usage limits.

8. Billing Integration

Connect usage to your existing payment and invoicing infrastructure.

This is becoming a new layer of the modern SaaS stack.

The Opportunity for AI Companies

I believe we're still very early in this transition.

The first generation of SaaS companies built software around subscriptions.

The next generation of AI companies will increasingly build software around consumption and value.

That's a profound change.

AI applications don't simply provide access to software.

They perform work.

And when software performs work, there is a natural economic relationship between:

Work → Usage → Value → Revenue.

The companies that understand that relationship will have a significant advantage.

The New SaaS Equation

For decades, the SaaS equation was relatively simple:

Users × Subscription Price = Revenue

The AI SaaS equation is becoming more sophisticated:

Usage × Value × Price = Revenue

And underneath it sits another equation:

Usage × Provider Cost = COGS

The companies that can connect those two equations will have something incredibly valuable:

Real-time visibility into revenue, costs, and margins.

That's the foundation for sustainable AI economics.

The Bottom Line

Usage-based pricing isn't simply another pricing strategy.

It represents a broader shift in how software companies create and capture value.

As AI becomes more deeply embedded into products and business processes, consumption will become increasingly variable.

That makes fixed pricing harder to manage.

It also creates an enormous opportunity.

The companies that can successfully connect usage, value, cost, pricing, and customer outcomes will be able to build much more intelligent—and potentially much more profitable—businesses.

The future of SaaS may not be:

"How many seats do you have?"

It may be:

"How much value are you creating?"

And in the AI era, that's a much more interesting question.

How Walleta Fits In

At Walleta, we're building the infrastructure to help modern software companies manage this new economic model.

Walleta connects customer wallets, credits, usage, upstream AI costs, and billing so companies can understand what's happening between the moment a customer uses an AI-powered product and the moment that usage becomes revenue.

Because in an AI-powered world, knowing what your customers are using—and what that usage costs you—is no longer optional.

It's becoming part of the foundation of the business.

The future of SaaS isn't just subscription-based.

It's usage-aware, value-driven, and increasingly intelligent.