Scott Woody, CEO and Co-Founder of Metronome – Interview Series

Scott Woody, CEO and Co-Founder of Metronome, has spent his career building products that bridge technical depth with practical impact. Before launching Metronome, he held key leadership roles at Dropbox, where he advanced from engineer to Director of Engineering, shaping core infrastructure and scaling operations across millions of users. Earlier, he co-founded Foundry Hiring, an intuitive applicant tracking system, and began his professional journey at D. E. Shaw & Co., where he pioneered data-driven recruitment strategies. His multidisciplinary background—from scientific modeling to large-scale software engineering—underpins his ability to lead Metronome at the intersection of technology, data, and business transformation.

Metronome provides a modern billing infrastructure designed to power usage-based and hybrid pricing models for innovative software companies. The platform enables seamless metering, billing, and revenue recognition with real-time data accuracy, empowering finance and engineering teams to align on a single source of truth. By abstracting away the complexity of building in-house billing systems, Metronome helps fast-growing companies like OpenAI, Databricks, and Anthropic scale their monetization strategies without sacrificing flexibility or transparency.

You co-founded Metronome in 2019—what problem did you originally set out to solve, and how has that mission evolved as AI adoption has accelerated?

When we started Metronome, the original problem was simple: modern software businesses need to move incredibly fast on pricing and packaging, but their billing systems can’t keep up. At Dropbox, we’d want to run a pricing experiment and it would take six months just to get it coded into the billing system. The billing system became the long pole for basically everything we did in that business.

We set out to build monetization infrastructure. Metronome was built to bring speed and agility to modern software businesses—to make pricing and packaging changes fast and easy instead of engineering-heavy projects.

AI has accelerated this mission in two critical ways. First, it’s making more of the world usage-based, which is our core bread and butter. But more importantly, AI has created hyper-competition. Different companies are constantly battling each other and trying to use pricing and packaging as a way of differentiating.

That means the software we originally built—something that makes it really easy and fast for pricing and packaging to be changed—is now table stakes. If you don’t take advantage of that flexibility, your competitors will. Pricing and packaging has become a battlefield of Darwinian competition, which means the need for Metronome goes up as competition gets more intense.

You just announced new capabilities like seat-based credits and unified invoicing. How do these fit into that vision?

Yes, today we announced a major expansion on pricing, invoicing, and customer experience – really the next chapter of monetization infrastructure for AI.

At the center is our new seat-based credits capability, which lets companies run hybrid pricing models that blend subscription predictability with usage-based growth. What we’re seeing is that many companies created during the 2010s—think Dropbox, Figma, Notion—primarily monetize on a seat-based fee. The more people in your company use the product, the more you pay. This is great—it’s easy, predictable, and scales as your business grows.

But these companies are now adding AI-native features into their products, and they’re realizing the value of their product doesn’t scale with seats anymore. It actually scales with the usage of these AI-native features. They need a commercial model that scales with the value their product provides. Seat-based credits is a very specific way of doing this—you get the benefits of seats with the upside of usage. This is becoming the de rigeur model for almost every SaaS business in the world.

The second feature we’re highlighting is unified invoicing across AWS, Azure, and GCP marketplaces, and introduced account hierarchy for enterprise billing. This means companies can now manage every revenue motion — self-serve, enterprise, and marketplace — through one system instead of juggling multiple disconnected tools.

What our customers are demanding is payment optionality. These AI-native companies tend to go to all geographies at once, and if you study payments—international payments in particular—you’ll find that different payment rails have higher acceptance rates and lower fees in different geographies. As our customer base grows and matures, they’re looking for payment optionality in different geographies. They might want to use a European-specific payment processor or US-specific payment processor. By allowing our customers to have choice and flexibility in how they receive payment and do invoicing, we give them more options for taking payment in different geographies. The feature we’re launching today is just the first step in that journey—the ability to issue an invoice directly from Metronome and take payment with the payment processor of your choice. Over time, we’re going to expand the choices available in that payment processor layer.

On the customer experience side, we’re releasing the Cost Preview API, in-dashboard invoicing, and lifecycle notifications. Modern billing should be transparent and part of the product experience. These capabilities give customers real-time visibility into usage and spend, eliminating surprise bills and building trust through transparency.

Together, these announcements reflect our belief that monetization infrastructure must give companies three things: predictability in revenue, visibility across teams, and control to evolve pricing safely as their products change.

Before Metronome, you spent several years as an engineer and later Director of Engineering at Dropbox. What lessons from scaling a global SaaS platform informed how you approached building Metronome?

There are two main lessons from Dropbox that shaped how we built Metronome.

First is the importance of flexibility at scale. Dropbox was famous for having simple “good, better, best” pricing with a free plan—very simple on the surface. But behind the scenes, inside the billing system, there were thousands of different SKUs for thousands of different customer configurations. Managing that complexity is actually quite hard.

We built Metronome to scale with that complexity for very large businesses. The question became: how do you build simple abstractions that give customers the full power and flexibility their businesses demand as they grow and mature?

The second lesson is about servicing multiple personas. One of the main frustrations at Dropbox was that the billing team was constantly overtaxed—they had a thousand things going on, always getting pulled in different directions trying to help finance, sales, and product all at once.

We built Metronome—both the business and the product—to service multiple different personas at once. One thing we’re best at is being an externalized partner for our customers. If you’re one of the large language model providers, Metronome serves not just as software, but as a pricing expert. We’ll individually help support customers in very high-touch ways.

That’s one of the things people find truly notable about working with us—how deep of a partnership we form. It’s much less a vendor-client relationship and more of a true partnership.

Metronome is powering the business models of OpenAI, Anthropic, Databricks, and NVIDIA—some of the most influential players in AI. What do they all have in common that made your approach to dynamic billing so valuable?

There are two or three specific things these customers have in common.

First, when you get to that size and scale, your pricing is just complex. You have lots of different products, lots of different flavors of customers. The necessary complexity—the large number of SKUs you offer, the different pricing and packaging configurations—means you need a system built from the ground up to handle that level of scale and difference between customers.

At the same time, you want the abstractions you interface with to be simple. If you’re an operations person working with Metronome, you don’t want to think about all that complexity all the time. Balancing those two things—giving you the power and control that Metronome provides without overwhelming the end user—that’s a key design principle we had when building the product.

The other thing that unites all our customers is that they’re extremely end-customer centric. We built Metronome to make all the data inside it continuously available to their end customers. If you’re an OpenAI customer, you can check your balance, set budgets, rate limit yourself—all of that is about customer experience on top of a consumption-based pricing model, and Metronome is the key platform that enables that.

Many founders focus on product or model innovation. You’ve argued that pricing and billing are now part of the AI infrastructure stack. Why do you see monetization as foundational to this new era of software?

There are a couple of different reasons monetization is so critical for AI infrastructure.

First, it goes back to the hyper-competition point. This era of software is just much, much more competitive. In past eras, you could focus solely on product differentiation—that really doesn’t work anymore.

Second, in every era of software, the biggest, most successful companies paired product innovation with business model innovation. Think Salesforce—they invented a cloud-based CRM. CRM software wasn’t new, but deploying it in the cloud was. But they coupled it with seat-based subscription pricing that scaled as your company grew, which was hugely disruptive versus the incumbent Siebel, which charged a large flat fee. You’d go from spending a million dollars a year on Siebel to $100 a month per seat in Salesforce—completely different value proposition for customers.

The same thing is happening in AI. But there’s another major factor: AI is incredibly expensive to run. The more your customers use your product, the more expensive it gets. That means you, as a vendor, need a pricing model or business model that scales with the usage of your product—otherwise you risk overrating on COGS.

What are the biggest technical or cultural challenges companies face when shifting from static subscriptions to usage- or outcome-based pricing?

There are two or three major changes that come with moving from seat subscription to usage.

First, you’re moving from a bookings-based business to an NRR-based business. In practice, this means that in a seat subscription era, your bottom line isn’t always tied to customer value—you could sign them up, and if they didn’t go live for 10 months, you still get paid. In a usage-based business, you literally can’t collect revenue until customers use your product, which means customer success and post-sales is super, super important.

Second, people underestimate that usage-based business models are fundamentally variable, which means customers have much higher expectations of visibility into how they’re using your product. The way I like to put it is: they need visibility, transparency, and control over their budget. If you don’t give them tools to do that, they’re not going to be happy customers.

Third, in a usage-based business, it really rewards building what I think of as growth flywheels—little loops in your product where the more you use, the more you spend, the more you want to use. By creating these viral loops, it’s kind of like in social networks, where viral loops work really well inside ad-based social networks because the more you build virality into your product, the more ad inventory you display, the more money you make.

The same thing is true in usage-based pricing. It’s not really true in subscription, which is why viral loops in B2B SaaS haven’t been a big thing, except in cases like Dropbox where those viral loops were critical for distribution. But mostly, virality has been confined to ad-based businesses. I actually think the rise of growth as a discipline—pioneered by Facebook—is going to coincide with the rise of AI.

Your recent whitepaper on the “Monetization Operating Model” outlines how companies can align revenue systems with real customer value. How does this model change the way AI startups think about scaling?

It goes back to what I was saying about viral loops. When these AI-native businesses find product-market fit, revenues can scale extremely fast. You see the virality that used to exist with social media networks, but now it’s directly monetized.

That causally explains why a company like Cursor can go from zero to a billion dollars in ARR in like two years. They’ve finally aligned price and value, which is a really, really powerful unlock for businesses.

With OpenAI and Anthropic as both customers and investors, how do you balance collaboration with independence in shaping the future of AI-driven business infrastructure?

We see those relationships as partnerships rooted in solving real problems at the frontier of AI. OpenAI and Anthropic are defining the next generation of software, and we’re building the infrastructure that turns innovation into scalable, sustainable business models.

At the same time, our mission is broader than AI labs. Metronome is built to serve every company that needs to modernize how they monetize, including AI-native startups and SaaS companies adding usage-based pricing to established products. We’re focused on being the category leader in monetization infrastructure, not just a billing tool for one segment.

How does AI itself influence Metronome’s own platform—are you using machine learning to optimize billing accuracy, detect anomalies, or predict usage trends?

We use machine learning to improve anomaly detection, usage forecasting, and pattern recognition—but we’re deliberate about where we apply it. Billing requires precision, so AI has to enhance accuracy, not introduce abstraction.

Long term, we see AI helping companies turn monetization data into strategic intelligence—understanding which features drive value, identifying optimal pricing thresholds, and surfacing revenue opportunities in real time. That’s where monetization infrastructure becomes a true growth engine.

Metronome has become a backbone for outcome-based monetization. Do you think we’re approaching a world where every software company becomes, in essence, an AI-driven data business?

My basic theory is that AI is going to disrupt every aspect of software and business. You can see the initial disruptions inside software businesses—software developers are completely disrupted by AI, writers are completely disrupted by AI.

I think it’s just a matter of time before more and more businesses become influenced by AI. We’re seeing the early stages with the more readily disrupted parts of the business, but things like legal and other areas will follow. I think it’s obvious that over time, more and more jobs are going to come under the sway of AI—and therefore under outcome-based, usage-based business models.

Thank you for the great interview, readers who wish to learn more should visit Metronome.