SaaS revenue is getting less predictable. Here’s how finance teams can adapt.

For the past decade-plus, SaaS finance teams have operated on a comfortable assumption: revenue is predictable and recurring. You lock in annual contracts, update your ARR forecast, and plan accordingly.

But that era may be coming to an end sooner than expected.

Usage-based pricing is gaining steam, and it's changing the questions SaaS finance teams need to answer. Instead of just “how many deals did we close?,” it's “how much are customers actually using what we sold?" And that second question is exponentially harder to model.

On a recent episode of the 10x Finance Podcast, Johnnie Walker, Director at Rooled, joined Aleph’s Albert Gozzi to unpack what this shift breaks about traditional financial planning, and how finance teams should respond.

The structural forces driving SaaS pricing shifts

Usage-based pricing didn't come out of nowhere. AI models have consumption-based cost structures. Cloud infrastructure is inherently usage-driven. The economics underlying modern software delivery are pushing companies toward pricing that reflects actual value delivered, not just what a customer committed to upfront.

Think about what that actually means day-to-day. A finance leader used to be able to say: "We have 500 customers at $10K/year. Baseline revenue is $5M." Plug that into your model, layer in some churn assumptions, add a growth rate, and you've got a defensible forecast.

With usage-based pricing, the questions multiply:

• How many of those 500 customers are actively using the platform, and how much are they using it?
• Will usage trend up or down next month?
• Is their usage correlated with success, or are they still just experimenting?

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Forecasting gets a lot harder

The complexity really hits when you try to model what happens mid-contract. In traditional SaaS, revenue is locked in—you know what you're getting for the next twelve months.

With usage-based models, revenue becomes a function of customer behavior that you can't fully control.

Instead of just historical subscription patterns, you’re now forecasting based on adoption curves, usage acceleration, seasonal variation, and the countless small variables that determine whether a customer actually gets ROI and sticks around.

And the risk profile changes, too. In a subscription model, you manage churn at renewal. In a usage-based model, you manage usage churn: a customer can stay on your platform but cut their usage in half, and your revenue drops accordingly. They never technically churned, but you lost half the deal.

Resist the urge to overcomplicate the model

The instinct here tends to be to throw sophistication at the problem. Hire a data scientist to build a machine learning model. Run Monte Carlo simulations to capture every variable.

Johnnie's advice is the opposite:

This tracks with what most experienced finance leaders know but sometimes forget in the moment: overly complex models become black boxes. You lose the ability to explain what's driving your forecast, which means you can’t explain it to your board or adjust when conditions change.

Here’s a simpler framework. It won’t capture everything, but it'll be reliable, explainable, and something you can actually manage.

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Revenue = Base Customers × Average Usage per Customer × Price per Unit of Usage × Retention Rate

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Finance teams are also buyers, and that creates tension

Here's the other side of this: even if finance teams get better at forecasting usage-based revenue, they also have to buy software priced this way. And for a function that runs on predictability, that can be uncomfortable.

There's a real paradox here—finance teams are being asked to adopt tools with variable pricing at the exact moment they're under pressure to tighten budgets. The pitch from vendors can't just be "it's cheaper upfront." It has to be "this scales with your success, and here's how you'll measure that."

That only works when there's genuine alignment between vendor and customer outcomes, and when the buyer can track ROI in something close to real time.

The adoption problem gets more expensive

There's one more dynamic worth sitting with. Most enterprise software implementations underdeliver. Teams buy the tool, don't invest enough in rolling it out, and end up paying for something they're barely using.

In a subscription model, that's a sunk cost—you eat it and move on. In a usage-based model, you keep paying for your failure to adopt.

This is the hidden cost of the shift: the burden of proof moves from the vendor to the buyer. You have to actually use what you buy. That means real change management, training, usage monitoring, and accountability.

What this means practically

To recap the key takeaways from the podcast:

Finance teams that want to stay ahead of this shift should be adapting now

Usage metrics need to become leading indicators of revenue. Don't wait for the monthly invoice to understand customer health. Look at the source data: API calls, feature adoption rates, processing volume, whatever tells you whether customers are actually getting value.

Forecasting models need to reflect usage distribution, not just customer count

Segment by adoption velocity, not just contract size. Understand which cohorts are expanding and which are contracting.

Cross-functional data integration isn't optional anymore.

You can't forecast usage-based revenue from the finance system alone. You need product usage data, customer success signals, and operational metrics feeding into your planning process.

Your forecast cadence needs to increase

When revenue was locked in for 12 months, quarterly forecasting was fine. Usage-based revenue demands monthly—maybe weekly—updates as adoption patterns develop.

The finance leaders who start building these muscles now will have a real edge when the shift accelerates. The ones waiting for the old playbook to keep working are going to get caught off guard.

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