Solving the AI agent pricing puzzle: Why SaaS models don’t fit in an AI world

AI agents don’t behave like SaaS. So why price them like SaaS?

Every prompt, every call, every output an AI agent delivers creates significant variable costs in real time. That’s a fundamental break from the SaaS model, where costs are nearly static. Applying SaaS pricing logic, such as flat rates or per-seat fees, to AI products is a risky business.

AI pricing is more than a go-to-market detail. It’s part of product design. When every interaction has a cost, your pricing model is your business model.

The limits of SaaS pricing in an AI world

SaaS pricing thrives on a simple formula: high fixed cost, low marginal cost. Build the product once, sell it to thousands, and let margin grow with each new seat.

GenAI breaks that model.

Every prompt, API call, or generated output incurs compute costs that quickly add up. The more your product is used, the more it costs you to run. That $0.01 model call might seem trivial until a single customer runs ten million of them. What appears to be engagement quickly becomes a margin drain.

Flat-rate or seat-based pricing, once a shortcut to predictable revenue, becomes a liability at scale. Without pricing that’s aligned with usage, you risk subsidizing your most expensive users and stalling growth right when it takes off.

SaaS-era pricing wasn’t built for GenAI. The economics are different, and your pricing needs to be too.

The four key differences between the SaaS vs. AI pricing puzzle

SaaS pricing is predictable, but AI pricing needs to be dynamic. If you try to monetize AI with the same assumptions as SaaS, you’ll miss the mark on costs, customer value, and revenue. Here are four ways AI breaks the old rules.

Usage isn’t just a metric; it’s your business model

In SaaS, usage tracking is a nice-to-have because SaaS has high fixed costs and near-zero marginal costs as more people use the product. In AI though, usage tracking is mission-critical. 

Every token, image, or API call carries a cost that quickly adds up and eats into your revenue. AI agent pricing needs to take this into account, but charging your customers based on usage requires real-time, granular tracking. Without it, you can’t bill accurately or confidently experiment with pricing. 

For example, OpenAI’s API charges per one million tokens. This token system represents usage, with a different amount of tokens used for every input and output based on the large language model, input length, and other factors.

Pricing and packaging are strategic differentiators

Pricing in SaaS is static, as companies don’t need to innovate on how they package their products. However, this won’t cut it when it comes to AI. 

With the economics of AI and a hyper-competitive market, pricing becomes a strategic differentiator. AI companies need agile pricing, which includes the ability to mix and match a variety of pricing components such as subscriptions, usage caps, credits, and tiers. This enables them to respond to competitors, the market, and evolving customer needs.

Replit’s pricing nails this with several tiers that have different subscription fees, usage limits, and included credits. These tiers demonstrate how the right packaging can be used to target different customer profiles, unlock upsell paths, provide buyers with flexibility, and align cost with value.

Seat count ≠ value

Traditional SaaS pricing revolves around seats. This is because the value of the software increases as more people use it. In AI, agents augment or replace human labor. For example, one AI agent might do the work of ten humans. Usage, not headcount, drives value in the world of AI. 

As a result, the number of users or seats doesn’t play a large role in AI pricing. Hybrid pricing models may include per-user pricing, but it’s rare for pricing models to solely focus on charging by seats or users.

Monetization becomes dynamic

SaaS monetization is built around predictable ARR and the ability to land and expand. On the other hand, AI monetization is dynamic and can take on many forms. Revenue can be generated based on consumption, outcomes, or anything else that can be turned into a billable metric. 

With so many options, how companies choose to charge customers now significantly impacts their bottom line. And the only way to achieve long-term, sustainable monetization is through safe and fast pricing experimentation. 

“The leaders in AI monetization will be those who continually learn from data, experiment with models, and stay aligned with both customer value and the underlying tech economics.”
_{— Pricing AI Agents} 

From puzzle to platform: How to bring it all together

Pricing AI is a continuous journey, not a one-time decision. Costs shift. Competitors innovate. Customer needs evolve. Your monetization model must keep pace. That’s why billing is no longer a back-office function. It’s a core part of your product strategy.

Orb was built to turn this pricing puzzle into an advantage.

Orb’s billing platform empowers GenAI companies to rapidly evolve their pricing while maintaining accurate and seamless billing. Instead of pulling engineers away from product every time you need to change pricing, hacking around the limitations of legacy billing systems, or manually reconciling issues caused by pricing updates, Orb lets you roll out any type of pricing model easily and automatically updates customer invoices. 

With Orb, you get:

• Precise, real-time visibility into customer usage and revenue
• Flexible tools to implement any pricing structure, including prepaid credits, outcome-based, subscriptions, tiers, hybrid, and more
• Pricing simulation to test the impact of pricing changes using historical data before going live.
• Automated invoice generation that gives customers a clear understanding of their usage and costs
• Robust reporting and analytics to align product, finance, and GTM

Don’t tweak the old model; build the right one

One of the biggest mistakes founders make is assuming the SaaS pricing playbook just needs a few tweaks. It doesn’t. GenAI flips the economics, the usage patterns, and the value delivery.

You’re not optimizing an old system; you’re inventing a new one. That takes infrastructure built for how AI products actually work.

Orb is the platform powering dynamic pricing, precise billing, and scalable monetization for AI companies.

Let’s build it right. Contact Orb to solve your pricing puzzle and maximize the revenue of your AI agents.