Marcos Rivera

Most B2B SaaS leaders build value.
But forget to capture it.

Let me guess:

- You’re guessing your price
- You’re losing revenue
- Scaling feels stuck

What if pricing could drive growth?

Here’s what I help you do:

→ Capture more value
→ Make pricing your edge
→ Build pricing confidence

Why should you trust me?

- 25+ years in tech pricing
- Ex–Head of Pricing at Vista Equity
- Priced 400+ SaaS products
- Author of *Street Pricing*
- Trusted by MBAs and SaaS CEOs

Beyond coaching, 

I’m an:

→ Investor 
→ Speaker
→ Entrepreneur 

passionate about helping businesses unlock their full revenue potential.

Ready to take control of your most powerful growth lever—pricing?

Checkout my featured section.

Founder and CEO

We help growth-stage B2B SaaS companies design the right pricing model to scale up

Ready to shift your pricing from guesswork to framework?

Your top 20% of customers are probably subsidizing your bottom 30%.
And you don't even know it.

See the #2 cause ↓

When we audit customer bases, we find this constantly: the customers who get the most value and never churn are often paying less than the customers who churn after 12 months.

Why?

Because most companies optimize pricing for customer acquisition volume, not for customer value fit.

You built pricing to attract as many customers as possible. 
So you attracted a lot of price-sensitive customers who were never going to be good long-term fits.

Meanwhile, your best customers, the ones who would happily pay 2-3x more because they're getting massive value, are grandfathered on old pricing.

The framework shows how to identify this pattern in your own customer base and fix it.

When we help clients migrate their high-value, under-monetized customers to pricing that reflects the value they're getting, we see 30%+ expansion revenue.

From customers who were already happy. 
They just needed a reason to pay more.

Are you optimizing your pricing for volume or value fit?

CEO of Pricing I/O • Award-Winning Author • Sought after Slayer of Bad Pricing

Years of calls, DMs, and pricing debates.
Finally in the same room.

Dan Balcauski, • Mark Stiving, Ph.D., and I have known each other for years through the pricing world. 

We've shared ideas, challenged each other's thinking, and watched the space evolve together.
But somehow, we'd never all been in the same place at the same time.

Until the Usage Economy Summit last week.

Pricing work can feel isolated. 
You're often the only person at your company who thinks about monetization strategy all day. 
The pricing community changes that.

These are the people who get it. Who've been in the trenches. Who know that pricing isn't just a spreadsheet exercise.

Good to finally make this photo happen.

This is Peter. He looks nice, but he challenges me all the time. 
And honestly? It makes our work better.

Here's the kind of thing we debate constantly:

We're reviewing a client case. High churn. Customers leaving after their first year. The executive team suspects it's a product problem.

The data tells a different story:
→ Legacy customers on old pricing? Staying longer. Making the most money. Seeing the most value.
→ New customers on "optimized" pricing? Churning at exactly 12 months. Citing ROI mismatch.

My take: "This is a pricing problem. The model doesn't align with value realization."

Peter's the first to push back on takes like this:

"Sometimes it really IS a product problem, Marcos. Sometimes the product just doesn't deliver enough value."

And he's right. Sometimes it is.

But here's how you know the difference:

If your legacy customers love the product and your new customers churn, it's not the product. It's the pricing.

The product didn't change. 
The pricing did.

The value is still there. 
Your pricing model just stopped aligning with how customers realize it.

This is what I love about our team at Pricing I/O.
We try to challenge ourselves:
→ Why that value metric?
→ What happens to expansion revenue?
→ Did you pressure-test this with sales?
→ I disagree with this segmentation, here's why.

It's not personal. It's rigorous.

Our best client work comes from these debates.

Because when smart people disagree about pricing strategy, that's where the real insights live.

The best pricing strategies aren't created in isolation.

They're forged when you put smart people in a room and force them to defend their thinking.

When you ask hard questions. 
Challenge assumptions. 
Stay willing to be wrong.

That's how you avoid the "drop decks and disappear" trap most consultants fall into.

If we can't defend a strategy to our own team, how can a client defend it to their board?

Across 400+ companies, this pattern repeats:
Pricing that worked at launch stops working as you evolve.

The winners don't treat pricing as a one-time project.
They build the muscle to test, learn, and adapt.

Do you have a process for pressure-testing big decisions?
Or are you making them in isolation?

Seat-based pricing is dying faster than most CEOs realize.

12 months ago: 50% of B2B SaaS used flat or seat-based models 

Today: Down to 27% 
Projected 2027: <10% 

The shift? 41% of companies are now using hybrid pricing models.

At SaaS Metrics Palooza, I walked through why this is happening, and the framework we use to build hybrid models that actually work.

Here's the reality:
AI changed who does the work.

It's not just humans anymore. It's systems completing tasks, generating summaries, resolving tickets, approving requests, all autonomously.

So how do you charge for that? By the person? The product? The outcome?

Answer: It depends on who's doing more of the work.

The BAM Framework - Base, Allowance, Meter 

These 3 layers create 5 different hybrid pricing models:

1/ Access Tiers (Base only) - Different AI complexity per tier, fair usage policy 

2/ Flat + Unit (Base + Meter) - Platform fee + outcome-based hybrid pricing 

3/ Flat + Limit + Unit (All 3 layers) - Most popular hybrid, includes volume + overages 

4/ Credit-Based (Base + Allowance) - PLG favorite hybrid, but don't fall into cost-plus trap 

5/ Pay-As-You-Go (Meter only) - Pure consumption, AI infrastructure 

The pattern from 400+ transformations: 
Companies using Flat + Limit + Unit (Model 3) are seeing the most traction. Why?

It gives customers predictability (base fee + included volume) while capturing expansion value (usage after allowance).

But, and this is critical, you need safety mechanisms.

→ Predict usage (dashboards, estimators) 
→ Prevent surprises (match reset timing to contracts) 
→ Protect customers (caps, true-up options, don't penalize usage) 

The companies winning with hybrid pricing aren't just picking a trendy model. They're designing the model that fits how their AI actually creates value.

Based on Tremont's research:
Hybrid models are capturing 3-4x more expansion revenue than traditional seat-based pricing.

The question isn't "should we go hybrid?"
It's "which hybrid model fuels OUR growth?"

Which model fits your product? Person doing the work or product doing the work?

Seat_Based_Pricing

At Vista Equity Partners, I learned something that changed how I think about pricing forever.

It wasn't about finding the "perfect price point."

It was about understanding how pricing connects to valuation multiples.

Here's what that looked like in practice:

We'd acquire a portfolio company and immediately audit their pricing. Not to squeeze more revenue out of customers, but to understand if their pricing model supported the growth thesis.

The companies with tight discount controls, clear value-based packaging, and pricing aligned to customer outcomes? They consistently outperformed on valuation multiples.

The ones with ad-hoc discounting, feature-based tiers, and "we charge what competitors charge" mentality? They left millions on the table.

One pattern I saw repeatedly: the pricing model that works for a Series B company scaling the business is completely different from a $100M ARR company optimizing for profitability pre-IPO.

There's no one-size-fits-all.

That's why I started Pricing I/O. To bring that PE-level rigor to growth-stage SaaS companies, without the $1 million McKinsey price tag and 9 months of PowerPoint slides.

We deliver results in weeks, not quarters.
We work in the trenches, not from ivory towers.
We teach teams to own pricing, not create dependency.

Because pricing isn't a one-time project. 
It's a strategic function that evolves with your business.

What's one pricing lesson you wish you'd learned earlier?

li_at

JSESSIONID

63% of AI monetization barriers are pricing-related.
And here's the #1 question I get from CEOs: "Where does AI actually belong in our pricing?"

Most companies are guessing. Chargebee's research shows 29% are just building AI into the core without charging extra.

That's a mistake.

At SaaS Metrics Palooza, I broke down the framework we use with PE-backed companies to decide where AI belongs.

It's not about the technology. It's about the experience.

Three different AI experiences = three different packaging strategies:

1/ Underscore the Core - When AI reinforces what you already do
Slack started with AI as a $10/user add-on. A few months later? Built into Business+ with a 25% price increase.

Why? They realized AI was becoming core to the Slack experience—making search better, recaps faster, answers easier. Not a "nice to have" feature.

2/ Upgrade the More - When AI extends adjacent use cases
ClickUp Brain charges $28/user (their base plan is $7). That's 4x their standard pricing.

Sounds expensive until you realize it's replacing 3-4 other AI productivity tools at $25-30 each. For customers, $28 is a consolidation play and a bargain.

3/ Unlock the New - When AI creates entirely new workflows
Intercom's Fin resolves support tickets autonomously at $0.99 per resolution. 
New TAM, new use case, standalone product.

Those who package based on experience (not just "it's AI so charge more") capture 2-3x more value and see 40% less pricing pushback.

The mistake? Treating all AI features the same.

Your AI that makes existing workflows 10% faster ≠ Your AI that unlocks entirely new capabilities.

Where does your AI actually belong?

McKinsey published a 4000-word guide on AI pricing. 
They're right about the problem. Wrong about the solution.

After analyzing 150 AI vendors, they found that despite $4.4trillion in potential economic value from AI, less than 1% of software spend is actually going to AI products today.

Why? Three consistent blockers: only 30% of companies publish quantifiable ROI, CFOs can't forecast what they'll spend on AI quarter to quarter and pilots fail to scale without proper change management investment.

I agree with all of this. We're seeing the exact same patterns across our 400+ client benchmark.

But here's where McKinsey loses me.

Their solution: "Test with customers and iterate quickly." That's not a strategy. That's expensive guessing.

Here's what I told a $75M ARR CEO who was about to "test" three different AI pricing models:
"You can't test your way to the right pricing without first understanding which experience you're selling."

→ Are you underscoring your core product? 
(AI makes existing workflows faster/better) 

→ Upgrading adjacent use cases? 
(AI extends what you do into new territory) 

→ Or unlocking entirely new capabilities? 
(AI creates workflows that didn't exist before)

That decision shapes everything, your packaging structure, pricing metric, go-to-market motion, whether you embed or sell standalone.

McKinsey's "iterate quickly" approach skips this question entirely.
So companies end up testing random combinations. 

"What if we bundle it?" 
"Let's try consumption-based." 
"Should we do per-seat or per-action?"

Each test costs 3-6 months and thousands in opportunity cost. This is the difference between consultants who drop decks and consultants who work in the trenches.

A 200-page McKinsey deck will tell you that 41% of companies are moving to hybrid models and you should "test and iterate." Great. Now what?

Working in the trenches with your team means asking: What experience is your AI actually creating for customers? Then designing the pricing model that monetizes that experience.

Not copying what Salesforce or Snowflake is doing. Not testing five models and hoping one works.

Companies that got AI pricing right didn't test their way there. They designed the experience first, then built pricing around it.

→ Slack realized AI was becoming core to the messaging experience, built it into Business+ with a 25% price increase. 
→ ClickUp saw AI extending productivity workflows, charged $28/user as an add-on to consolidate other tools. 
→ Intercom built AI that automated support entirely, 99¢ per resolution, standalone product.

Three different experiences. Three different pricing strategies. All intentional from Day 1.

McKinsey is right that this is the biggest shift in software since SaaS. But "test and iterate" isn't how you capture the opportunity.

Context without expertise is aimless. Expertise without context is pointless. You need both.

The discount death spiral: one deal at 30% off, another at 35%, your average hits 25%. 

Across 400+ SaaS companies, we see this pattern destroy margins and valuation multiples. 

Here's how PE-backed companies fix it with a 3-tier approval process. 

Full breakdown here 👆

McKinsey found that successful AI pilots require $3 in change management for every $1 in model development.

Most companies spend $0 on change management.
Then wonder why pilots don't scale to production.

After analyzing 150 AI vendors, McKinsey identified "sustained adoption" as the #3 blocker to AI monetization (behind value communication and price predictability).

Here's what's happening:

Companies invest heavily in building AI capabilities. Model development, infrastructure, data pipelines, millions of dollars.

Then they launch to customers and assume adoption will just happen.
It doesn't.

Without forward-deployed engineering, employee training, performance monitoring, and process integration, teams don't change their workflows. The AI sits unused. No ROI gets proven. Contracts don't renew.

The $3-for-$1 rule is McKinsey's finding from companies that successfully scaled AI pilots. For every dollar spent building the model, they spent three dollars on change management.

But here's what most pricing discussions miss:
Adoption friction isn't just an operations problem. It's a pricing problem.

This is the "L" in the REAL framework I shared at SaaS Metrics Palooza—Lift. How much effort does the customer need to invest to get value?

The more friction, the less pricing power you have.

→ High friction (3-6 months to value) means customers are skeptical. They want pilots. They negotiate hard. They're not willing to pay premium prices for something they're not sure will work.

→ Low friction (2-3 weeks to value) means customers see results fast. They're confident. They're willing to pay 2-3x more because the risk is lower.

Last month, I worked with a company that cut their AI onboarding from 12 weeks to 3 weeks through better change management, pre-built integrations, forward-deployed engineers, structured training.

Their close rates went up 40%. But they forgot to reprice.

They were still charging based on the old 12-week implementation timeline. Leaving money on the table.

Many companies forget to revisit pricing after they've reduced adoption friction. If you cut implementation time by 75%, you should be capturing more value, not the same amount.

Based on our 400+ client benchmark, companies that systematically reduce adoption friction and reprice accordingly capture 2-3x more revenue per customer than those who just focus on the product.

The pattern: 
Invest in change management → Measure time-to-value improvement → Reprice to reflect lower friction.

Adoption friction directly impacts pricing power.
Lower the friction, raise the price.

Are you investing $3 in change management for every $1 in AI development? 
Or are you building capabilities no one will adopt?

AIPilots

Your economics typically won't be as great out of the gate when it comes to AI, but the path to make it better over time is gonna be the key.

I shared this at SaaS Metrics Palooza because too many SaaS leaders are panicking about day-one AI pricing economics instead of building the right improvement path.

Here's the specific framework I shared:

Secondary limits that actually work:
→ Cap data sources (5 basic, 15 pro, unlimited enterprise)
→ Retention windows (30/90/365 days by tier)
→ Processing limits (hours per month, API calls)
→ Response time SLAs (24hr/4hr/1hr support)

The credit bonus math:
Instead of $1,200 ARR at $80/month (20% discount)
Do $1,200 ARR at $100/month + 20% usage credits

Same customer investment, but you protect pricing integrity and create margin expansion as they grow into those bonus credits.

Use this 3-step process as your 30-day action plan ↓

What secondary limit will you implement first?

Pricing

LLM costs have dropped 80% annually for two straight years.

Most AI companies responded by lowering prices or making features "unlimited."

McKinsey's research on 150 vendors shows that's the wrong move. 
Companies that maintained or raised prices despite falling costs saw 2-3x higher revenue growth than those who passed savings to customers.

Yet most are doing the opposite.

Jasper AI went from $10 per 5,000 words to unlimited generation included free. Sounds customer-friendly. But here's what that misses:
Your AI isn't getting cheaper. It's getting more valuable.

Two years ago, AI did single-step tasks, generate an email, summarize a doc, answer a question.

Today, AI agents execute multi-step workflows autonomously. Research across databases, draft outreach, update CRM, schedule follow-ups.

Your costs dropped 80%. 
Your complexity increased 10x.

Customers don't pay for compute. They pay for outcomes.

Adobe got this right. 
Started with embedded AI and simple features. 
Moved to standalone SKUs at higher prices as they added video generation, translation, and complex workflows.

Their costs went down. Their prices went up.

Q1 2025: $125M in AI revenue, expecting to double by year-end.

Only 16% of SaaS companies commercialized AI as standalone products. But those 16% report 2-3x higher customer traction and revenue.

The pattern across companies getting this right: costs fall, prices stay flat or rise with added capabilities. 

Use margin gains to add complexity, better integrations, multi-step workflows, higher quality.

Price based on value delivered, not costs incurred.

Based on our 400+ client benchmark, companies that maintain value-based pricing during falling infrastructure costs capture 4-5x more economic value.

Falling LLM costs are a margin opportunity, not a customer discount.

Question for your team: Are we pricing our AI based on the value customers get, or the costs we incur?

Most B2B SaaS companies are about to copy their way into a churn crisis.
This is the conversation I'm having with CEOs and boards 3x per week.

I just watched a $50M ARR company copy a competitor's usage-based pricing model.
Six months later: CAC up 60%, churn jumped from 8% to 14%, sales cycles doubled.

What works at scale can destroy an early-stage company.

Before you change your pricing model, answer these three questions:

1. How do your customers actually consume value?
Not how you think. How they actually behave.

CRM where value scales with team size? Per-seat still works.
Data platform where value comes from queries? Usage-based fits.

2. What's your sales motion?
High-touch enterprise sales can handle complexity.
PLG with self-serve? If prospects can't understand their cost in 30 seconds, you've lost them.

3. What's your growth stage?
Series A proving PMF needs different pricing than $100M ARR pre-IPO.

The shift from license-based to outcome-based pricing is real.
But context without expertise is aimless, and expertise without context is pointless.

Just dropped a video about it ↓

https://lnkd.in/gNGqitFU

I break down the complete framework, why AI is accelerating this shift, how to model impact before launch, and how to test without blowing things up.

You don't hire consultants because you have a strategy.
You hire them because you don't.

A CPO told me recently:
"The team wants to start quickly. They have urgent things they're nervous about because nobody knows what they're doing, bluntly."

Not "we're pursuing a strategic initiative."
Just: nobody knows what they're doing and we're scared.

This is how every consulting project actually starts.
But nobody admits it.

Here's what really happens:

Your company launches an AI product. 
Pricing team guesses at a model. 
Three months in, churn spikes. 
Board asks questions. 
Nobody has answers.

Or: Competitor changes pricing. Sales scrambles. Everyone has opinions. Nobody has data. CFO demands a strategy by next board meeting.

Or: New product launching in 60 days. Pricing still says "TBD" in the deck. CEO keeps asking "what are we charging?" and the room goes quiet.

That's when you call.

Not because you woke up wanting to "optimize monetization strategy."

Because you're 30 days from a board meeting with no idea what to tell investors about your AI pricing.

Consultants sell transformation.
You're buying panic relief.

When you say "we need to move fast", you're not impatient. You're scared and running out of time.

When you say "the team is nervous", you're admitting you don't have confidence in your approach.

When you ask "how quickly can you start?", you're really asking: how soon can you stop us from making an expensive mistake?

The consultants who get it don't lead with "here's our transformation methodology."

They lead with "here's how we'll de-risk this for you in the next 30 days."

If you're evaluating consultants and the process feels like it's taking forever, this is why.

You need someone to de-risk the urgent thing. 
They're selling you a six-month transformation.

The best projects start by de-risking the urgent thing first. Building the transformation second.

When's the last time you were brutally honest with a consultant about why you're really hiring them?

"We're paying for 20 seats but only 5 people use it."

You're charging per seat. 
But your customers don't get value from the number of seats, they get value from tasks automated, data processed, or outcomes achieved.

This is value metric misalignment. And it's killing your renewals.

Real example: 
AI-powered task automation company charging $50/user/month. 
Customer signs up with 20 seats at $1,000/month. 
But only 5 people actively use it.

They're paying $1,000/month but perceiving $250 worth of value.

At renewal: 
"Why are we paying for 20 seats when only 5 people use this?" 
They churn.

The fix isn't lowering the price. It's changing the value metric.

→ If value comes from tasks automated, charge for tasks, like Zapier does. 
→ If value comes from data processed, charge for data volume. 
→ If value comes from outcomes, charge for outcomes.

When your pricing metric aligns with how customers actually consume value, they can justify the cost.

Across 400+ B2B SaaS companies we've worked with at Pricing I/O, this is the #1 cause of pricing-driven churn.

What's your pricing metric? 
Does it match how your customers actually get value?

$60K for 6 months of value? That's too expensive.
This is how customers justify churning, even when your product works.

I see this pattern constantly: products that require data integration, team adoption, or behavior change take 6+ months to deliver full value.

But companies charge full price from day one.

So at renewal, customers do the math: 
"I paid $60K but only got 6 months of real value. That's $10K/month. Too expensive."

And they churn.

Not because the product failed. 
Because the pricing model didn't account for the time it takes to realize value.

The carousel above shows three ways to fix this: ramp pricing, success milestones, or hybrid models.

The goal? 
Make it easy for customers to justify the cost at every stage of their journey

If it takes 6 months to see full value:
Don't charge full price from day one

The value may be real.
But it takes time to materialize.

If your pricing model doesn't account for ramp time, customers will churn before they see the full value.

Your best customers are paying you 40% less than your worst customers.
And that's why you have a churn problem.

I see this pattern constantly at Pricing I/O.

A CEO comes to us: 
"We've invested millions in product development. Hired more CSMs. Improved onboarding. But our churn rate is still 15%."

So we pull their data.

Legacy customers, 5+ years with the company, love the product, paying 40% less than new customers.

New customers? Churning after 12 months. 
Primary reason: "Can't justify the ROI."

Most CEOs think their legacy customers are just more loyal.
But that's not it.

Your legacy customers are paying less, but they understand exactly what they’re getting for the money.

Your new customers? 
They're paying more, but they can't connect what they pay to what they get.

When we run pricing surveys with churned customers, here's what they tell us:
"We weren't using enough of the product to make it worth it."
"We couldn't justify the cost."
"The ROI wasn't there."

These aren't product complaints. These are pricing complaints.

Your product didn't fail them. Your pricing model did.

If your legacy customers love the product and your new customers churn, it's not the product. It's the pricing.

The product didn't change. 
The pricing did.

Across 400+ B2B SaaS companies, I see this constantly: 
High churn isn't always a product problem. It's often a pricing problem.

Are your best customers paying you the least?

Building a SaaS product is the easy part.
The hard part? 

Knowing what to charge for.
Not how much. What.

I've worked with 400+ companies on pricing transformations, and the most common mistake isn't bad pricing, it's never defining the value metric in the first place.

They default to per-user because competitors do it. 
Or per-seat because the billing system made it easy. 
Or per-transaction because... honestly, no one remembers why.

None of those are strategies. They're inherited defaults.

Here's the question that actually matters: 
What is the thing that, when your customer gets more of it, they're clearly getting more value?

It's not always obvious.

For a fintech client, it wasn't users. It was connected bank accounts, that's what correlated with value delivered.
For others, it might be projects managed, workflows automated, or outcomes achieved.

The right value metric does three things: it scales with customer success, customers understand it intuitively, and it's predictable enough to forecast.

"Per user" used to check all those boxes. But AI is breaking that model.

When automation reduces headcount, when one person can do the work of five, charging per user starts to punish the efficiency you're selling.

I'm seeing companies rethink this now, before their customers start asking why they're being penalized for getting better at their jobs.

What's your value metric? 
And when's the last time you questioned whether it's still the right one?

A client who's known me for years just evaluated five other consultants before hiring me.
That should terrify you.

This week, a billion-dollar SaaS company closed with us on a pricing transformation. 
Their Chief Product Officer and I go way back, we worked together at Vista Equity Partners. 
He knows my work. Trusts me. We literally survived the trenches together.

But when his company needed to hire a pricing consultant, he still looked at six different options.
During our debrief, I asked what made the difference.
He said: "When I did the reference calls, I asked each vendor's clients a specific question: Would you use this consultant for any type of pricing problem, or just this specific one?"
For the other five consultants, clients said: "Actually, they're great at this thing, but not so much at that. For that other problem, I'd probably use a different vendor."
For us: "I'd use them for anything. And have, multiple times."
Here's the uncomfortable truth nobody's talking about:
Your relationship doesn't protect you anymore.
Your client can love you. Trust you. Have worked with you before. And they'll still compare you with five others.
Why?
Because they've been burned by consultants who solved one problem and disappeared. Who dropped 200-page decks and never showed up again. Who created dependency instead of capability.
So now they do real diligence. Doesn't matter who you are.
What decides the deal isn't your pitch. It's what your clients say about you when you're not in the room.
"Would you hire this consultant again? For a completely different problem? Or just this one?"
If the answer is "just this one," you're a disposable solution. An expensive band-aid.
If the answer is "for anything," you're a partner.
What's the difference?
Disposable consultants solve problems. Partners build capability.
Disposable consultants deliver a model. Partners teach the team to execute it independently.
Disposable consultants create dependency. Partners make themselves less necessary over time.
The executive said something else that stuck with me:
"We wanted to make sure we didn't bias our process by our relationship. So we did the diligence even though we already knew you from the Vista days."
Your relationship gets you on the shortlist.
What your former clients say about you is what closes the deal.
In 2025, everyone's making those reference calls. Everyone's comparing. And the question they're asking is:
"Do you make me better after you leave?"
If the answer isn't "yes," you're losing deals you thought were already yours.
When's the last time you asked your clients: "Would you hire me again for a completely different problem?"
If you're not sure of the answer, you already know.

"We're not using it enough to justify the cost."
When SaaS customers say this at renewal, it's not a usage problem. It's a pricing problem.

After analyzing hundreds of B2B SaaS churn calls, I've identified 3 customer churn red flags:

Red Flag #1: "We're not using it enough"
→ Your pricing metric doesn't align with usage patterns

Red Flag #2: "We can't justify the ROI"
→ Your pricing model doesn't account for value realization timeline

Red Flag #3: "It's too expensive for what we're getting"
→ Wrong customer segmentation driving churn

If you're hearing these in renewal calls, fix your SaaS pricing model, not your product.

Watch more insights here:
https://lnkd.in/g-7XkY9X

LLM costs dropped 80% for 2 years straight. Should you lower AI prices?

McKinsey analyzed 150 vendors: Companies that maintained/raised prices saw 2-3x higher revenue growth.

Why? Your AI isn't getting cheaper, it's getting MORE valuable.

18 months ago: Single tasks
Today: Multi-step autonomous workflows

Adobe's playbook: Costs ⬇️ → Prices ⬆️ → $125M Q1 revenue

What to tell your CFO: Customers pay for outcomes, not compute.

Source: McKinsey 2025 report

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

Fynn Glover

Stripe bought Metronome. Congrats to everyone who made it happen. 

This is a good move for both parties in my opinion. 

But this acquisition solves only one part of the deeper monetization problem.

What it doesn't solve is the fulfillment & entitlements problem.

Billing, whether its seat-based, or usage-based or hybrid, determines how you invoice. 

Fulfillment determines how you monetize your product(s) and how customers receive value across the entire lifecycle.

Fulfillment answers questions like:

What does this customer get?
What are their limits? What happens when they cross them?
How do trials work?
How do add-ons activate?
How do exceptions behave?
How do products interact inside a suite?

All of this fulfillment logic is governed by how a company implements entitlements. 

Most companies hard-code entitlements and tie them to plan IDs in their billing system. Some use JWT tokens from their auth system. Some use feature flags and then build glue code between flags and billing that takes the form of an admin panel.

The result is predictable. 

We accept bad application design, where our applications are expected to know the billing state of our customers (e.g. hard-coded). 

Product catalogs are duplicated across billing, ERP, CRM, CPQ, and databases.

Exceptions pile up. Experiments stall. Engineering becomes the gatekeeper.  Pricing and packaging become untouchable. Billing disputes increase. Expansion remains unoptimized. 

No one can identify whitespace. No one knows how product usage behavior should influence pricing and packaging, or how pricing and packaging should influence product usage. 

Growth teams stop pushing for changes. Finance models ideas they can't execute. Product can't align the product roadmap w/ the pricing roadmap. 

Companies leave real money on the table. And worse, they accept a slowdown in their ability to lever pricing to grow faster. 

I saw this again yesterday with a $30M ARR Stripe Billing customer. 

Their VP Engineering and VP Finance described the same pattern. They want to evolve pricing. They want to experiment. They want to match price to value. But they can't. Engineering has to say no because the product cannot fulfill the ideas the business needs.

And the Metronome acquisition won't solve this customer's problems. 

This is the deep, deep gap in modern monetization. It's an architecture problem that stems back to fulfillment. 

My view is that the only billing platform that gets to multi-billion ARR is the one that makes entitlements & fulfillment a first-class pillar. 

Hybrid pricing, hybrid selling, and great developer experience all depend on it. 

This is one of the reasons I've been writing a book how to truly leverage pricing & packaging in b2b SaaS/AI.

Chapter 6 explains how to design the architecture that fixes this exact problem and delivers true flexibility. If you want early access, DM me.

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

I’ve started a few companies now… and the fastest way I’ve ever unlocked revenue wasn’t by adding features or hiring more salespeople. It was by 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗽𝗿𝗶𝗰𝗶𝗻𝗴.

Not necessarily raising prices. Not running promos. I mean iterating (𝘲𝘶𝘪𝘤𝘬𝘭𝘺) on packaging, price points, and add-ons… until the model actually mapped to how customers bought and used the product.

When I could do that with speed and agility?
Revenue jumped. Every. Single. Time.

When I couldn’t... when a simple packaging tweak took 3 dev sprints or a quarterly roadmap reshuffle? The business stalled.

Not because the product wasn’t good. But because the pricing architecture was rigid.

That’s why I believe 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗮𝗴𝗶𝗹𝗶𝘁𝘆 = 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗮𝗴𝗶𝗹𝗶𝘁𝘆. If you can’t move, you can’t grow.

𝗜𝗳 𝘆𝗼𝘂𝗿 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗶𝘀 𝘀𝘁𝗮𝘁𝗶𝗰, 𝘆𝗼𝘂𝗿 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗶𝘀 𝘁𝗼𝗼.

And yet... most teams still treat pricing like a one-time event. Spreadsheet → all-hands → rollout → pray.

Agility doesn’t come from more strategy meetings…
It comes from an operating model where pricing isn’t buried in code, where GTM teams don’t need to file a ticket to test a new plan, where “we should try this” can become “it’s live” in days, not quarters.

That’s the gap most SaaS companies are living in; it's not a strategy gap, it's a 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 gap.

I’ve lived both sides. You make a lot more money with the agile one (the founding thesis of Schematic tbf)

Full clip below — would love to hear where pricing gets stuck in your org.

Expo supports wildly different client types... and it changes everything about how they approach monetization.

On the same day, they might engage someone vibe-coding an app on nights on weekends, a 20-year veteran dev building out a personal project, a midmarket team rebuilding their mobile infrastructure, or an enterprise org running CI/CD pipelines at scale.

That kind of spread forces you to rethink nearly every pricing instinct you may have.

You can't assume a single value metric; you can't predict a tidy PLG → SLG on ramp across the board; you can't alienate users paying by credit card just as you can't expect an enterprise with complex budgeting concerns to blindly embrace usage-based billing.

Really enjoyed our most recent podcast episode with Joe Ryan, CRO of Expo and Jon Samp, Head of Product (full episode in the comments 😉)

The future of b2b monetization is hybrid. 

In 2026 unlock pricing & packaging leverage. Leave less money on the table. 

Build a great pricing practice internally. Don't neglect the systems. Don't write billing code. Don't hard code pricing. 

Make product monetization a 1st class citizen.

Make monetization 1st class

My last company left millions on the table... all because we couldn't change our pricing and packaging to turn a high-value feature into a separate add-on.

I pitched the C-Suite (I was running growth at this particular company), and leadership agreed.

But the pricing architecture at the company was so brittle and so manual, our eng team spent 6 months working on it... and couldn't ship the change.

The business lost patience (we were devoting so much engineering resources away from core product), they moved on, and left millions in ARR behind.

It's basically the origin story for Schematic .

It blew my mind 1) how long it took and 2) how complex it became to make this tiny change to pricing and packaging. It was something like a 20% NDR lift we just... couldn't capture.

So we fixed it. Schematic decouples pricing from code entirely. One source of pricing and packaging truth via an API call (instead of 5,000 "IF/THEN" statements hard coded into your product).

Ship pricing changes in hours, not weeks. Spin up usage-based pricing, hybrid billing, credit burndown in days, not months.

I'd love to show you how it works (almost as much as I would have loved having Schematic at my last company 🤣 )

If you're building billing/monetization from the ground up, you're signing up for an infinite roadmap. Here's what I mean:

Unless you’re incredibly intentional (and wildly skilled) about how you architect your pricing logic, you’re signing yourself up for what I call "the infinite roadmap" (and your engineers are going to hate you almost as much as your GTM & RevOps teams will).

Every time you change pricing, every time you want to grant an exception, if you want to roll out usage-based pricing, whatever... you're pulling engineers off core product to implement hard-coded pricing changes.

Most founders (especially in SaaS or AI) assume billing and pricing are more or less the same. You plug in your prices, generate invoices... boom, monetization.

Except that’s not what’s 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 happening.

You can put a $49 price tag in a price field, sure. You can send an invoice.

But pricing — 𝘳𝘦𝘢𝘭 pricing — is an architecture that starts in your product itself:

• In how you grant or restrict access
• In how you gate, bundle, and unbundle features
• In the rules you create around limits (e.g. hard limits & soft limits)
• In the UI that shows a customer what they've used 
• In how you grant exceptions

I ran growth at a company as we scaled from $10MM ARR to $40MM ARR… and we left 𝘮𝘪𝘭𝘭𝘪𝘰𝘯𝘴 on the table.

My team wanted to unbundle a high-value add-on out of our “good, better, best” pricing. It would have not only driven new revenue, but would have materially impacted our ability to cross sell, up sell, the works.

We tried to make it happen for six months. Engineering couldn’t make it work, too many devs spent too much time away from the core product, the business lost patience, and the initiative was scrapped. 

𝗛𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 𝗶𝗻 𝗵𝗮𝗿𝗱 𝗰𝗼𝘀𝘁𝘀 𝘄𝗮𝘀𝘁𝗲𝗱, 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗶𝗻 𝘂𝗻𝗰𝗮𝗽𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗹𝗲𝗳𝘁 𝗼𝗻 𝘁𝗵𝗲 𝘁𝗮𝗯𝗹𝗲.

That’s the infinite roadmap at work.

𝗬𝗼𝘂 𝘤𝘢𝘯 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁. 𝗕𝘂𝘁 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘢𝘭𝘭 𝗼𝗳 𝗶𝘁. 𝗔𝗻𝗱 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝘢𝘭𝘭 𝗼𝗳 𝗶𝘁. 𝗙𝗼𝗿𝗲𝘃𝗲𝗿.

I didn’t have this term in my vocab yet, but the infinite roadmap is why we started Schematic. Not because billing is broken… Stripe Billing is great & getting better by the day. But rather because the tooling we have to control product monetization is homegrown and brittle.

Pricing done right isn’t a number. It’s a living system. Modular. Composable. Decoupled from code. Able to power PLG and enterprise and hybrid models… without begging for sprint time.

If founders internalized one idea from this entire journey, I hope it’s this:

Pricing agility is revenue agility. And if your pricing is static or hard-coded, your revenue is too.

So yes — build it yourself if you want. That’s a valid choice. Just make sure you understand what you’re signing up for.

No alternative text description for this image

Pricing is a discipline every bit as important as finance, operations, marketing or R&D… yet most founders learn the skillset on the fly (and usually through costly mistakes).

𝗧𝗵𝗮𝘁 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗮𝗿𝘆 𝗱𝗲𝗳𝗶𝗰𝗶𝘁 𝗹𝗲𝗮𝘃𝗲𝘀 𝘄𝗮𝘆, 𝘸𝘢𝘺 𝘁𝗼𝗼 𝗺𝗮𝗻𝘆 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝘀𝘁𝗮𝘁𝗶𝗰 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗱𝗲𝗿𝗶𝘃𝗲𝗱 𝗳𝗿𝗼𝗺 𝗶𝗻𝗲𝗿𝘁𝗶𝗮 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗮𝗻𝗱 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲.

I wish I could say I always understood how important pricing & packaging was… but I was one of those founders who learned the hard way.

I ran growth at a scaling startup on the journey from $10MM ARR to $40MM ARR… and we left many millions on the table.

We wanted to decouple our highest-value feature set for a specific subset of our user base into an add-on. Fairly standard pricing stuff.

Six months of engineering frustration later, and we canceled the add on. The engineers couldn’t get it to work, and the business was frustrated with how much time they were spending away from core product.

We projected a 20% lift in net dollar retention… and it just sat there. Languishing. Never to be captured. All because our pricing architecture was a mess of hard-coded “IF/THEN” statements.

Price is a number. Pricing is an architecture.

You have to invest in the systems that not only preserve pricing agility but unlock revenue agility.

Run A/B tests constantly. Make changes to pricing in config, not code. Let GTM tinker, test and refine without having to pull engineers off core product.

That’s what we built with Schematic. I didn’t want any other founders to suffer through what I had to the hard way.

Pricing architecture will be to monetization what data warehouses were to analytics.

A decade ago, hardly anybody budgeted for a data warehouse… now you can’t run GTM without one.

Pricing’s about to have the same reckoning.

Right now, most SaaS companies still treat pricing as a project; it’s a strategy exercise that ends with a few rows in Stripe and a slide deck for the board. But as product lines, usage models, and hybrid motions expand, those static decisions break.

The problem isn’t that companies chose the wrong price. It’s that they built on the wrong architecture.

Data warehouses changed analytics because they decoupled storage, compute, and access — freeing teams to ask better questions faster.

Pricing architecture does the same thing for monetization. It decouples pricing logic from code, so teams can ship changes without re-engineering billing, entitlements, or packaging every time the market moves.

The shift isn’t about billing sophistication. It’s about pricing agility.

And just like with data, the companies that build the layer early will outlearn everyone else.

Because in the end, pricing agility is revenue agility… and revenue agility is revenue growth.

"There's a predictability vs. flexibility tradeoff we try to help our customers navigate.

We have customers who really value usage-based pricing. They really like the flexibility month to month. 

Then we have customers who do an annual budgeting process. And they can't go back to that budget mid-year and say I need more, because we didn't plan our adoption correctly. They also don't want to under purchase."

- Joe Ryan, CRO at Expo

Tune in to the full episode to learn from Joe, Expo's CRO, and Jon Samp, Expo's Head of Product. Link in comments.

Your product catalog is a poison pill hiding in plain sight.

Had a great conversation with Kayvan Dastgheib-Beheshti on our Monetizing AI Podcast a few weeks ago.

The big takeaway for me? "Treat your product catalog AS a product."

It has immense downstream implications when ownership of the product catalog is messy, distributed, or worse, unknown. It touches GTM, Revenue, Finance, your taxes... almost everything. So treat it like it's as important as your core product... because it is one of your core products.

Check out the clip from the episode, full cut in the comments 👇

SaaS companies could learn a LOT from airlines.

Love them or hate them, airlines know how to optimize pricing. Much like telecoms figured out how to charge based on usage, airlines kinda cornered the market on dynamic pricing.

They have all the data of who's searching for flights, to where, for when, and can test (in realtime) how much you're likely to pay for said ticket.

Now I'm not saying SaaS companies should charge every client a different amount... but that level of pricing agility equates directly to revenue growth for airlines. And for years, SaaS did the exact opposite: per-seat pricing, revisited every couple of years, thanks for playing.

When AI means you can scale MUCH faster, but that scaling brings with it compressed margins and hard inference costs, you can't rely on per-seat pricing any more. It just doesn't cut it.

Some thoughts:

Add ons aren't necessary to most of your customers... but they're mission critical to some portion of them.

If you can identify a specific feature that's driving immense value to a specific subset of your user base, it might make a lot of sense to unbundle that from the core pricing tier and offer as an add-on.

I identified one such opportunity when leading Growth at a startup as it went from $10MM ARR → $40MM. We projected we could lift net dollar retention 20% by unbundling a specific feature.

Six months later, we had spent hundreds of thousands in hard costs to build that... and the business scrapped it.

Engineering couldn't make it work with our pricing architecture, the C-suite lost patience, and the company couldn't afford that many engineers yanked off core product for any longer.

It cost us millions in incremental revenue that year and every year thereafter.

It was the "aha" moment that led us to founding Schematic. It blew my mind that it took 6 months to unbundle a feature into an add on (and we couldn't even do it!).

So we built the application layer that removes pricing and billing from your codebase, and turns it all into config. That way, GTM, Growth, Sales, whoever can tinker, test, tweak and roll out new pricing without engineering having to touch a single line of code.

I'd love to show you how.

Yesterday, I had a call with a pricing leader at a great software company. 

We unpacked the opportunity the company has to transform pricing into a growth lever. 

As we explored the situation, we articulated something that I have felt for a while, but haven't put into words. 

I've usually thought about pricing & packaging from two angles: 1) how it's operationalized as a practice; and 2) the systems architecture that governs how it evolves in concert with a maturing product and market.

But yesterday, another angle surfaced. 

What is a company's culture around pricing? Suer this gets into how pricing is operationalized. 

But it's also discrete and separate. 

You can have a great pricing leader. You can have what looks like a well constructed pricing committee that is ambitious and ready to go drive iteration. 

You can have agile, composable architecture. 

But what if your executives don't really want to lean in together to make pricing a consistent driver? What if every idea is met with skepticism? 

That's a cultural problem. 

If you're a CEO reading this, ask yourself  a couple of questions: 
1. are we too precious with our pricing?
2. do we have a mindset of "yes, and..." or do we gravitate toward, "no, that would never work."

Pricing in a great company is many things... 

It's how you operationalize change as your product evolves with your market. 

It's how you architect the interaction of key business systems (entitlements, product catalog, metering, etc).

It's literally half your revenue equation (price x quantity = revenue). 

And it's also how you create the cultural conditions to take risks and learn from them.

We all talk about pricing and packaging as a crucial growth lever that companies too often under-utilize. 

But what those statements usually don’t highlight is the underlying systems problems that destroy pricing & packaging agility. 

If you're a product or engineering leader thinking about your approach to managing pricing and packaging... 

If you're a CEO who wishes you could move faster on pricing & packaging and leave a lot less money on the table... 

Then you need to understand the architecture of monetization in B2B SaaS and AI. 

There are 5 components that support a more flexible pricing & packaging system. 

Companies who operate pricing & packaging well will understand each of these systems in detail and architect their monetization accordingly.

pricing architecture 

Was chatting with a growth engineer at an AI-native company this week. $50MM raised… and even they're struggling with pricing & monetization.

The company needs to support both PLG and enterprise sales offerings without cannabalizing sales-led revenue.

They need to launch hybrid billing (feature-based tiers + credits).

They need to be able to iterate and test pricing and packaging without massive engineering overhauls to do it.

They're in the same boat as almost every single high-growth company we talk to — a lot of monetization complexity to build/support without the pricing architecture that makes it easy.

These are the problems we've designed Schematic  to solve.

On the call, I screenshared a code snippet — what it looks like to ping Schematic for entitlement and usage in one API call, instead of building & maintaining a ton of entitlement infrastructure in-house.

“I totally get it," he said, "it's feature flags & entitlements for monetization and pricing. We've tried to build this in house and we're hitting a ton of snags... We have multiple systems, and no one is particularly happy about our current setup.”

This isn’t a legacy midmarket company’s tech stack… this is an AI company founded and built as next gen tooling... yet their pricing architecture is throttling their pace.

So often the leaders we talk to think they have a billing problem. What they have is a 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

The real issue is the inability to slice and dice offerings, test and segment, iterate, and learn — especially across sales-led and product-led motions, or multi-product businesses.

Usage-based billing systems are designed to consume events from products to then inform invoices... they're not designed to enable the commercial tinkering on pricing & packaging that drives growth.

The fix? Pricing and packaging must be decoupled from both code and billing.

We can deploy software with flags. We can learn quickly. That type of pace & flexibility needs to exist in pricing & packaging.

As Scott Mitchell, former CTO of Salesloft  said, "we need a separation of concerns. Decoupling pricing and packaging from application code is just good application design. The application should care about great functionality & correct access. Not pricing."

You can absolutely keep stacking internal tools, flags, hard-coded entitlements tied to plan IDs in your billing system. You can absolutely keep reconciling things in Retool and treating monetization like a second-class citizen. You can even build a really elegant entitlement layer… if you want that to be your core competency.

But what if you don’t?

What if you want pricing agility? What if you want to stop leaving so much money and speed on the table?

Pricing IS product. Treat it as a first-class citizen, and you gain revenue agility... and revenue agility = revenue growth.

I've gotten to know Rob Litterst, John Kotowski, and Ulrik Lehrskov-Schmidt over the last few years. 

They're great founders, builders, and educators. 

What PricingSaaS and WillingnessToPay.com have done for the monetization community to date is massive. 

Just the beginning, and we're super excited to partner with them.

Cofounder PricingSaaS: We help SaaS leaders nail pricing and packaging. Join our free pricing community👇

So excited to welcome the first batch of PricingSaaS partners 🚀

Pricing and packaging is quickly going from an afterthought to a Day 1 problem, and we're proud to work with the companies building the infrastructure for the next era of SaaS and AI monetization.

Allow me to introduce them 👇


→ DigitalRoute - Intelligent Usage Data Platform with roots in telecom. When telecom made the shift to mobile, it created a data tsunami. DigitalRoute helped telecom leaders get a grip on usage data and create the seamless monetization strategies that we've come to expect today. They believe the AI wave will have a similar impact on SaaS, and are already working with leaders like SAP and Adobe to power their next wave of pricing.


→ Metronome - Founder Scott Woody was a product leader at Dropbox and saw first-hand how hard it is to manage usage-based pricing models. After watching a team of 50 engineers run into seemingly endless operational complexity, he decided to build a better way. Metronome is a usage-based billing platform build to face those complexities head on. They started by serving infrastructure players like Cockroach Labs and Cribl, but have since expanded to serve the top players in AI, including OpenAI, Anthropic, LangChain, and NVIDIA.


→ Nue.io - Modern RevOps platform that provides CPQ, billing, and analytics for every revenue model, every channel, and every sales motion. Founded by leaders who built Zuora and Salesforce CPQ, Nue wanted to build a unified Quote-to-Cash solution for the next era of SaaS leaders. They now power end-to-end revenue lifecycle management for companies like OpenAI, Glean, and Chili Piper. 


→ Schematic - Founder Fynn Glover saw firsthand that pricing, billing, and finance systems were often disconnected from the product itself, which made it incredibly difficult for teams to iterate on pricing. He built Schematic to fix that, and they're powering monetization for SaaS leaders like BigCommerce and agentic AI companies like Wobby. Schematic is built on Stripe and is the simplest way to innovate on pricing from day one.


And of course, we still partner closely with Ulrik Lehrskov-Schmidt, Christopher Truce  and the exceptional team at WillingnessToPay.com. 


As we close out 2025, it's high-time to take another look at pricing and packaging, both your strategy and the infrastructure that powers it. 

If that's you, DM me. 

I'd love to learn more and see if one of our partners can help 🫡


PS. We'll be bringing on more partners. If you are interested, we'd love to hear from you too.

Your revenue equation — every company’s revenue equation — is very simple:
𝗣𝗿𝗶𝗰𝗲 𝘅 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝘆.

That’s it. R = PxQ.

Sure, packaging can make things seem more complex when you’re layering partial tiers, hybrid selling, usage-based billing, premium add-ons, etc. into your sales motion… but the fundamental math doesn’t change.

How much of your product/service can you sell, and how much can you sell it for? That’s it.

But I would bet 90%+ of founders (and I was guilty of this for a long time) focus the vast majority of their time, energy, mental capital, engineering resources, etc. on the “quantity” side of that equation.

Yet it’s only half the equation.

You’re shipping new product every day. Your product’s value is maturing every day. 

Is your product roadmap lining up with your pricing & packaging roadmap?

If not, ask yourself why. It’s 50% of the revenue equation. 

Every week that your monetization lags your delivered value, you’re missing out on that revenue (and that revenue compounds).

An inflexible pattern we see in billing implementations is having your application know the billing state of the customer. Makes it really hard to support customer exceptions. Makes it really hard to evolve pricing & packaging as your product grows.

As Scott Mitchell once said to me, "It's just bad application design. There should be a separation of concerns. Your application should care about great functionality & correct access. Not pricing & plan logic."

Billing ≠ pricing. They are fundamentally different things whose influence span entirely different domains.

Billing is about managing subscriptions or sending out invoices,  things like that.

Pricing is about monetization. It's an entire architecture that scales (or limits) your revenue.

Likewise, "price" is a number... "pricing" is an architecture.

In the world of seat-based SaaS subscriptions, the difference was much harder to see. But as per-seat billing plummets in favor of hybrid billing, usage-based billing, or outcome-based billing, your architectural layer either empowers those motions... or sets you up on an infinite roadmap.

taplio

The Battle

Marcos Rivera

Fynn Glover

Followers

Posts

Engagements

Likes

Performance ScoreMarcos Rivera's LinkedIn Performance

Performance ScoreFynn Glover's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Marcos Rivera

Fynn Glover

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Marcos Rivera's LinkedIn Performance

Performance ScoreFynn Glover's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreMarcos Rivera's LinkedIn Performance