Schematic

Schematic allows businesses to outsource pricing and packaging with a few lines of code, enabling them to quickly launch new packaging models, take the burden off of engineering, and flexibly adjust their pricing and packaging to individual customer preferences.

With Schematic, teams can ship faster, delight customers, and get out of billing projects forever.

Co-Founder & CEO

First Round Fast Track

Mentor

Techstars

Techstars Mentor

Automox

VP Business Operations

Chief Of Staff

Director of Growth

Springbot

VP Corporate & Business Development

Matcha

Matcha is the first all-in-one blogging tool and the largest marketplace of licensable professional articles for growing ecommerce brands. The company was acquired by Springbot in 2020.

* Identified market opportunity & commercialized vision
* Brought product to market with enterprise & SMB customers
* Assembled a group of incredibly talented and intrinsically motivated people. Recruited & hired ~80 people across the company's lifespan. 
* Built internal operating procedures & managed P&L
* Executed both inside sales & product-led go-to-market motions
* Raised angel & venture capital
* Acquired in late 2020

Founder & CEO

RootsRated Media

RootsRated.com was developed to help people to discover the best outdoor experiences in the world, according to local experts. The site grew into one of the leading outdoor discovery sites in the world (500k unique visitors/mo) and served native advertising & sponsored content campaigns to brands like Marmot, Arc'teryx, Toad&Co, ExOfficio, Timberland, Smartwool, New Balance, and more.

Pointer Management Company

Intern Hedge Fund Analyst

InternMatch

Chattanooga Outreach Director

Creathere

Stand Campaign Field Organizer

I'm a co-founder & CEO of Schematic, where we help businesses solve the last mile of pricing and packaging. With just a few lines of code, Schematic allows companies to quickly launch new packaging models, free up engineering resources, and tailor pricing to individual customer preferences. In short, we help teams ship faster, delight customers, and eliminate billing headaches for good.

As a kid, I wanted to play professional soccer, and I pursued that into my early 20’s, playing collegiately & then semi-professionally. My professional journey off the field began with a community visioning campaign to help continue Chattanooga TN’s revitalization, then a short, but formative stint with an edtech startup, which got me interested in startups, and then a year at a fund of hedge funds, before founding my first company in 2012, RootsRated.

Founding RootsRated kicked off a decade-long journey into building software companies. RootsRated began as a go-to site for outdoor recreation and adventure travel. We later pivoted to SaaS, launched Matcha, a content marketing platform, and eventually, were acquired by Springbot, an email marketing service. After Matcha, I joined Automox, a leading ITOps platform, where I wore multiple hats—Director of Growth, Chief of Staff, and VP of Ops. 

I love the challenges of entrepreneurship—identifying market opportunities, designing & commercializing products, and building teams to bring ideas to life. 

I was raised in Chattanooga, spent the last 6 years in Atlanta which is one of the great cities, and recently moved to Colorado with my wife, two kids, and two dogs. I went to the University of Richmond, where I captained the men’s soccer team and studied International Relations & French (though my French has since atrophied). 

Outside of work, I enjoy trail running and cycling, and occasionally share my thoughts on history and technology on Substack.

Monetizing SaaS:
- Youtube: https://www.youtube.com/@schematichq
- Spotify: https://open.spotify.com/show/5yjegk5duBz3LtvjwOo8ai
- Apple: https://podcasts.apple.com/us/podcast/monetizing-saas/id1757006729

🚨 Excited to announce Monetizing AI — a summit for founders, CEOs, and GTM & product leaders building, pricing, and monetizing AI applications.

📅 August 28th 
📍 Modern Loft, SF (FiDi)
 🎟 50 seats only — invite-only, small-room

We’ll be tackling the most pressing questions in AI monetization with an incredible lineup:

The state of AI pricing in 2025 — Kyle Poyar (Growth Unhinged)

Is consumption pricing the only viable pricing model moving forward? — Marcos Rivera (Pricing I/O)

Negotiating & presenting pricing through enterprise cycles — Simon Ooley (Veles)

Using limits to land & expand enterprise deals — Daniel Chalef (Zep AI (YC W24))

Rolling out a major pricing change the right way — Gaurav Vohra (Startup advisor and ex-Superhuman)

Billing architecture in the AI era — Shar Dara (Vercel)

Launching & managing credit burndown pricing — Giovanni Hobbins (Schematic)

How to build an entitlement engine to power pricing agility — Benjamin Papillon (Schematic)

How to master willingness to pay conversations  — Fynn Glover (Schematic) 

No sales decks. No fluff. Just deep conversations about how to build real revenue engines for AI.

👉 Signup from the link in the comments.

Fynn Glover

Co-founder & CEO of Schematic | helping SaaS & AI startups monetize smarter and faster

Pricing in SaaS is changing fast because of AI.

Salesforce is moving away from seats. 

Clay is leaning into credits. 

Stripe is investing in billing. 

Startups are raising big rounds to reimagine monetization, billing, quoting, and ERP from the ground up.

Every vendor has a different point of view on questions like:

Who owns pricing & packaging?

Where the product catalog should live?

Is billing a finance & accounting tool, a sales tool, a product tool — or all of the above?

As Ryan Glushkoff once said: “Price is a number. Pricing is an architecture.”

At Schematic, we're building the product based on our POV on optimal pricing architecture. 

We believe that Monetization has to start in your application — not your billing platform, not your quoting platform — with architecture that strategically decouples pricing logic from your application. 

Why?

Because it’s in your app that you make decisions that are continuously evolving:

 • How and when to gate features and enforce limits

 • How to design the experience that drives adoption and expansion

 • How to showcase pricing and plan upgrades

That’s why we believe the way you handle features and limits is where monetization begins.

Ask yourself: Can you gate features and enforce limits — soft or hard — without pulling developers off core product work?

If yes, you’re ahead of the game. If not, it’s one of the most important problems to fix.

I share more on this point of view — and where it comes from — in this video and article (linked in comments).

Last week, we hosted the Monetizing AI Summit in San Francisco. 

Some of the smartest practitioners and operators in growth & monetization. 

50 founders, CEOs, and GTM leaders together to talk pricing, packaging, and billing for AI products.

Takeaways that stood out to me:

Kyle Poyar → The shift is toward hybrid billing. More so than outcome-based. At least for now. As everyone knows the challenge with outcome is the attribution problem.  

Marcos Rivera → A super useful framework for navigating consumption & hybrid billing: the 4 T’s (Transparency, Types, Time, Tee Up).

Simon Ooley → When selling to enterprises with hybrid/consumption/credit models, the job of the seller is to tie consumption directly to customer use cases. 

Daniel Chalef → Limits are the connective tissue between PLG adoption and enterprise expansion.

Giovanni Hobbins → Common pitfalls with credit models: unclear value of a credit, unclear cost of an action, unclear limits policy. Takeaway: Design for clarity.

Shar Dara & Benjamin Papillon → Modern billing architecture in the AI era depends on: unified pricing data via source of truth for the product catalog and company profile, unified usage & cost data, decoupled entitlements, and SDK-driven billing with reusable UIs.

Gaurav Vohra → A well-run pricing change drives material growth. At Superhuman: one owner, clear principles, customer interviews, early communication (3 months before rollout), and the result was material uplift, not PR crisis. 

Thanks again to our speakers and everyone who joined us!

🚨 This Thursday in SF: Monetizing AI Summit

We’re bringing together founders, CEOs, and Product & GTM leaders to tackle the hardest questions in AI pricing and monetization.

The lineup:

Kyle Poyar — State of AI Pricing in 2025

Marcos Rivera — Is Consumption Pricing the Only Model That Works?

Simon Ooley — Navigating Enterprise Pricing Cycles

Daniel Chalef — Using Limits to Land & Expand Deals

Gaurav Vohra — Rolling Out Major Pricing Changes the Right Way

Shar Dara & Benjamin Papillon — Billing Architecture in the AI Era

Giovanni Hobbins — Credit Burndown Pricing

Me — Mastering Willingness-to-Pay Conversations


If you're building a great product and asking questions like... 

- What’s the right price model for our product?

- How should we monetize our second product?

- How do we use limits to better land & expand customers?

- How should I negotiate pricing with enterprise customers?

- How should I price agents?

- How do I validate willingness to pay?

- How do I roll out a major pricing change that drives growth, not churn?

- How do I roll out credit-based pricing?

- How should I architect billing for agility & flexibility?


📍 Modern Loft, SF (FiDi) from 1:30 PST to 7pm PST
 🎟 Invite-only, small-room
 👉 schematichq.com/summit

Conversations on how to price, package, and monetize AI offerings. Lots of Q&A. Dinner, drinks, and networking after the sessions.

Let’s imagine something.

You’re either:

1. A founder of a $1M ARR AI startup growing 100%+ YoY.

2. The CFO of a $50M ARR SaaS company launching new AI products to expand TAM.

Now, a couple of assumptions:

As the founder/CEO, you’ve been pricing flat-rate to win users fast — because you believe your market is winner-take-all. 

But you’ve realized the “models will be 10x cheaper next year” story is misleading. You’re burning margin, and the path forward is metered billing — pay-as-you-go, credit burndown, something usage-based.

As the CFO, you see AI products as high-margin revenue drivers — but you can’t clearly model costs, your board is asking tough questions, and your CRO says pricing is hard to explain to customers. 

You’re also being pulled toward consumption-based pricing.

Either way:

If you’re selling AI (whether as a feature or as a core offering), usage-based pricing is no longer optional. It’s becoming existential.

Competitors are already doing it. 

Flat-rate holdouts will eventually throttle usage and raise prices.

So you ask yourself... what are the second-order effects in this world?

1. Revenue recognition & forecasting get harder — especially with hybrid SaaS + AI models.

2. How limits are managed & enforced become levers for NDR and churn mitigation.

3. In-app billing experience, UI, & cost transparency rise in priority.

4. Pricing is treated like product, not just a spreadsheet exercise.

5. You have new models to test — especially credit burndown & hybrid usage-based models, and you must become good at iterating on pricing & packaging.

6. Entitlement management becomes a competitive differentiator. Otherwise you're always miring developers in pricing initiatives.  

This is where SaaS is headed — and it’s coming faster than most teams are ready for.

Practical levers to survive & win (thanks to Jasdeep Garcha for the conversation on this front):

1. Usage/credit-based pricing that directly tracks cost.

2. Proprietary “smart abstraction” layers to offload cheap tasks.

3. Model pickers to let customers choose lower-quality outputs for lower prices.

4. Charging for features (collaboration, design mode, analytics) in addition to transactions.

The shift is already well underway. 

Winners will combine cost discipline with product-led pricing innovation — and every software company will eventually need to play by these rules.

You’re the CFO of Outreach. 

You're a leading revenue-tech SaaS company. 

You're evolving from a core SaaS product into a multi-product SaaS + AI offering.

How would you:

Price and package both SaaS and AI?

Deliver usage transparency and clarity to customers?

Support the systems complexity of hybrid billing, hybrid GTM, and multiple product lines?

Factor in new COGS realities for AI?

Operationalize, report on, and forecast new pricing models like credit burn-down?

I had a conversation on Monetizing AI with Drew Laxton, who is living and breathing these questions every day.

If you’re a founder, product leader, finance leader, or sales leader building a multi-product company, trying to navigate credit-based pricing and support hybrid selling & billing, this episode is worth your time.

Here are four insights that stood out:

1. Credit-based AI pricing must be simple and margin-aware.

The market is coalescing around credit burndown as a pricing model. Pure SaaS pricing can sink your margins fast. Credits protect our margins and deliver flexibility and transparency to our customers. 

2. Multi-product pricing changes your whole operating model.

Switching to consumption pricing touches everything — product, billing, Salesforce, analytics. Every system has to know how many credits a customer has and how they’re using them.

3. You need thoughtful systems architecture to succeed. 

Pricing and packaging is governed by three stacks: product, finance, and sales. They must be well integrated to deliver the flexibility customers have come to demand.

4. Consumption and credit-based pricing changes your math — and your story.

If a $60K credit package burns twice as fast, is that $120K ARR? If it burns half as fast, is it $30K? Straight-lining contracts doesn’t work. You have to do math you never had to do before — and be transparent with your stakeholders about the volatility.

I learned a lot from this conversation. 

Full episode linked in comments

Thanks to TLDR for the shout-out! Was a great surprise to have customers and prospects share this with us today.

This Thursday in San Francisco, we have an incredible group of practitioners speaking about the most pressing questions in AI monetization. 

For example, how do you roll out a major pricing change? 

Having been a Superhuman customer since 2018, I had a front row seat to how Gaurav Vohra and Alex Rodrigues executed. It was a masterclass. 

Other sessions at Monetizing AI this Thursday:

Kyle Poyar (Growth Unhinged): The state of AI pricing in 2025

Marcos Rivera (Pricing I/O): Is consumption pricing the only viable model?

Simon Ooley (Veles): Negotiating & presenting pricing through enterprise cycles

Daniel Chalef (Zep AI (YC W24): Using limits to land & expand enterprise deals

Shar Dara (Vercel) & Benjamin Papillon (Schematic): Billing architecture in the AI era

Giovanni Hobbins (Schematic): Launching & managing credit burndown pricing

Me :-) How to master willingness-to-pay conversations

If you're building AI and asking questions about how to monetize, hope to see you Thursday.

Startup Advisor • Growth Leader • Superhuman • Advisor at Clay, Replit, WisprFlow, GPTZero, Superpower & others

🗓️ Last year we shipped a massive pricing update at Superhuman
🗓️ Last quarter we did the same at Kondo: Superhuman for LinkedIn DMs
🗓️ Last month we did the same at Atria AI

Each time: massive growth 📈

And each time: barely any customer grumbles 😌

Smoothly rolling out major price changes isn't easy

At Schematic's Monetizing AI Summit this Thursday I'll share my playbook on doing it right — customer first, revenue driving, and efficient 🎁

Register here: schematichq.com/summit

(in-person in SF, for founders / growth / product leaders, space limited)

Open article: Monetizing AI - Private Summit @ San Francisco - Schematic by schematichq.com

Monetizing AI - Private Summit @ San Francisco - Schematic

“Are you spending $200k in license fees or is it gonna be $5 million?”

👆Tido Carriero & Netto Farah, Co-Founders and CEO of Koala, talking about AI pricing.

It’s quite literally the (multi)-million dollar question when it comes to purchasing inference.

More and more AI companies are moving toward credit-based pricing (where you prepay for some measure of inference/tokens/whatever, then burn it down as you use it). But my convo with Koala showed this is such a difficult balance to get right.

Capturing and preserving margins, accurately tracking usage/burndown, clearly communicating both value & usage to your customers (all without them losing trust in you and your pricing), are really hard.

And to top it all off… all of that could change in a week’s time as models advance, inference costs fluctuate, etc. 

Striking that balance between flexibility, predictability and customer trust when it comes to AI pricing is something we’ve been working on for a while now at Schematic. 

If you're exploring credit burn down pricing, take a look at some of what we've learned. Link in comments.

Hard truth I learned the hard way:

You either pull your most expensive engineers off core innovation…

Or you avoid revenue-driving pricing & packaging changes.

At my last company, we’d hard-coded entitlements. It looked like this:

if (
    company.plan in ['pro', 'enterprise', 'growth']
    or company.id in [12345, 67890]
    or company.trial_flags.get('export')
    or company.custom_flags.get('export_enabled')
):
    allow_export = True

It meant every change to pricing required an engineering sprint.

Result: innovation slowed or pricing stayed stale. Both were terrible tradeoffs.

When we interviewed dozens of other SaaS teams, we found the same duct-taped patterns:

- Stuffing entitlements into Stripe metadata
- Hard-coded entitlements
- Zombie flags in LaunchDarkly
- Homegrown billing infra glued across product, billing, and CRM
- A few “hero engineers” who understood the system and were always fixing it when it inevitably broke
- CEOs telling us: “We just can’t touch pricing right now; it’s too difficult to change.”

That’s why we founded Schematic.

To move pricing from code to configuration.

And give GTM, product, and finance control of monetization without code changes.

If pricing agility is still on your “someday” list — it’s probably costing you more than you think.

Here are a few questions to ask: 

1. how quickly can we launch & monetize a new feature or product?

2. how many sprints are we losing to billing initiatives?

3. how many billing disputes are we running into through poor checkout experiences? 

4. how much revenue are we leaving on the table by not continuously adjusting our pricing and packaging? 

5. are we capable of supporting hybrid billing models (e.g. seats + credits)?

6. are we excellent at supporting hybrid sales motions (e.g. trials + enterprise config)?

7. have we adequately paired the usage and billing data of our customers to identify and drive net dollar expansion?

Monetization is such an incredibly deep and interesting field in SaaS & AI, and the reality is that it's only a lever when you have the right billing architecture. 

What's the optimal billing architecture, you ask? 

Shar Dara & Benjamin Papillon discussed this last week at the Monetizing AI Summit. 

It hinges on unified pricing data via a source of truth for the product catalog, unified usage & cost data, decoupled entitlements, and SDK-driven billing with reusable UIs.

If this resonates, would love to connect.

Make monetization a first-class citizen from Day 1.

Early in the Schematic journey, I spoke with Justin Gagnon and Aubrey Rhodes, who led growth engineering at Calendly. 

They’d lived the pain we describe in this article: hardcoded entitlements, scattered product catalogs, brittle billing systems.

To fix it, they refactored from the ground up—feature flags, an entitlements engine, in-app billing UI, admin panels, metering, usage tracking—so Calendly could scale across multiple products, hybrid sales motions, and complex billing models.

Justin later joined our podcast Monetizing SaaS and put it best:

“Monetization should be a first-class citizen as early as humanly possible, from Day 1. And entitlements are the best place to start.”

The stakes The fastest-growing SaaS & AI companies can ship monetization changes in days, not quarters — new products, new packaging, new pricing mode

Open article: Our POV: Monetization as a First-Class Citizen in the Product by Fynn Glover • 5 min read

Our POV: Monetization as a First-Class Citizen in the Product

I’ve been thinking about credit pricing and trust a lot recently. 

Almost every call I'm on right now, SaaS companies & AI startups are asking about credit-based pricing. 

And with good reason… as a pricing model, it makes a 𝘵𝘰𝘯 of sense for them; it can also make a lot of sense for their end users, too. 

But it’s not easy to implement for a host of reasons.

Chief among those? 𝗧𝗿𝘂𝘀𝘁

If your end users have no idea what a unit of measure means (or how much their actions are actually using), trust erodes. 

If your users feel like they don't understand what they're paying for (or worse, that they're being cheated somehow), you could lose them. 

So if you’re considering moving toward credit-based pricing — you need to excel at communicating answers to the following... clearly... intuitively... 

  • What does a credit actually mean?

  • What do they get for that credit?

  • How are you computing/monitoring credit usage?

  • How many credits do they have left?

  • How do they re-up if they need more?

Something I’m really excited about is helping builders both answer those questions and communicate those answers clearly and natively in their applications. 

We want to help you ensure that every touchpoint your customers have with your pricing builds on the trust you’ve worked so hard to earn through your product.

Credit burn down is a critical pricing model for many companies building and monetizing AI & SaaS.

But most teams hack support for credit burn down pricing with Stripe + spreadsheets and hit the wall.

That’s why we've been investing in making sure Schematic supports credit burn down elegantly, flexibly, & transparently.

- Define credit types, expirations, promos

- Mix with seats, PAYG, flat-rate

- Give customers full transparency in-app

Bottom line: credits are powerful as a standalone pricing model, or as a component of a hybrid model. But they're hard to operationalize. 

With Schematic, they're easy.

If you’re considering credit-based pricing for an AI or usage-heavy product, let’s talk.

Today we launched credit burndown! Schematic now makes it super easy to monetize your AI product with credit based billing.

💪  Flexibility: Offer credits included in a plan and as prepaid bundles
🤑  Price based on value: Features can burn credits at different rates (useful for expensive vs cheaper LLM queries)
🤝  Manage exceptions: grant promotional credits, force expire credits
📖  Close the books: handle revenue recognition with a detailed credit ledger
✨  10x user experience: give users transparency with a customizable customer portal and checkout to monitor and top up their balance
💲 All built on top of Stripe to make it easy to migrate and manage

Check the video for a demo and learn more here: https://schematichq.com/ai

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