Cosmo Tech | The Decision Twin Platform for Supply Chain, Finance & Asset Leaders at Fortune 500 Scale | Simulate Tomorrow. Decide Today.

I specialize in the messy middle of B2B SaaS scaling—taking companies from erratic growth to a disciplined, high-performance revenue engine.

Most sales teams don’t fail because they lack talent; they fail because of ambiguity.
When forecasts can’t be trusted, pipelines move only through founder heroics, and managers report activity instead of owning outcomes, you don’t need another playbook—you need an operating system.

My operating philosophy: Cadence | Ownership | Process

I install a repeatable flywheel of operational excellence:

Cadence
A rigorous Rhythm of Business—daily, weekly, and monthly operating cadence that surfaces execution risk early, before it becomes a board-level surprise.

Ownership
Managers own outcomes, not just activity. Forecasts are built bottom-up so leadership can commit without caveats.

Process
Qualification and execution are standardized before adding headcount, ensuring hiring is tied to capacity and real revenue impact—not hope.

The proof (Ekahau — SVP, Global Sales, 2019–2024)
	•	Revenue: Scaled $25M → $65M ARR in five years
	•	EBITDA: Increased $7M → $32M (margins ~20% → ~50%)
	•	Team: Built a 130-person global organization while reducing per-employee cost 25%

Selected wins
	•	Healthentic: Doubled ACV ($5M → $10M) and drove 10× pipeline growth through GTM transformation
	•	ControlCase: Delivered 34% YoY growth and 300% expansion revenue growth

Currently

Available for VP of Sales or Fractional GTM leadership with B2B SaaS companies $5M–$30M ARR. I also lead SalesGSS (revenue operating frameworks) and DigitalRegIntel (regulatory intelligence for AI/fintech).

The Senator perspective

Two terms as a Washington State Senator taught me the value of high-stakes negotiation and public accountability. I bring that same discipline to sales: clear ownership and execution when the pressure is real.

“Steve is ‘Instant Offense’ for sales—rare and valuable. World-class at creating strategic, data-driven sales teams.”
— Jeff O’Mara, CEO (Healthentic) / GM (Ekahau)

Steve Litzow

AI didn’t kill sales reps.
It killed excuses.

In 2026, AI isn’t a productivity tool — it’s a truth serum.

It exposes:
• Broken process
• Weak management
• Fake activity dressed up as “effort”

If your sales motion is tight, AI compresses time-to-decision.
If it’s sloppy, AI just produces more noise — faster.

Most $10M–$50M ARR teams are about to learn this the hard way.

This is Week 1 of the SalesGSS 2026 Operating Series.
Eight weeks. Eight uncomfortable truths.

AI didn’t come for your reps.
It came for your excuses.

Comment “2026” if you want the series.

VP of Sales / Fractional VP (B2B SaaS, VC & PE-backed) for $5M–$30M B2B SaaS | Scaled $25M → $65M ARR | SalesGSS

Your closers aren’t the problem.
Your committee is.

Roughly half of qualified B2B deals don’t lose.
They stall.
“No decision.”

At $10M–$50M ARR, the pattern is consistent:
• Strong champion
• Positive calls
• Deal “feels good”

But it’s single-threaded.

One relationship trying to carry internal selling across:
Finance. IT. Security. Procurement. Operations.

The closer is persuasive.
The organization isn’t aligned.

With 10+ stakeholders involved, persuasion no longer scales.

Elite closers in 2026 aren’t smoother.
They’re better at internal selling.

They multi-thread early.
They equip champions.
They sell the decision process.

Quick diagnostic:

Can your reps name five stakeholders—and each one’s priority—in their top deals?

If not, you’re optimized for persuasion when you need consensus.

Week 2 of the SalesGSS 2026 Operating Series.
(Week 1: AI doesn’t fix execution—it exposes it.)

Comment “2026” if you want the series.

If you can’t prove value in the first 60 days, you don’t have a deal.

In 2026, long sales cycles aren’t what kill deals.

Delayed proof does.

Buyers will wait longer to buy —
but they won’t wait longer to know if they made the right decision.

57% of B2B buyers expect positive ROI within 3 months — not at renewal.

If a champion can’t point to a concrete win inside 60 days:

Confidence erodes
Finance steps in
“Commit” turns into “let’s wait and see”

That’s why the strongest teams aren’t closing faster.

They’re proving faster.

They define Day-60 success before the contract is signed.
They scope for proof, not size.
They treat time-to-value as a deal quality metric, not a CS problem.

If value only shows up at renewal, the deal was insured by hope — not evidence.

This is Week 3 of the SalesGSS 2026 Operating Series.
Next week: why slow product cadence is becoming a silent deal-killer.

This week’s regulatory signal isn’t clarity — it’s formation.

In the U.S., digital asset market structure legislation stalled again. In the EU, regulators are already questioning AI Act transparency practices well ahead of enforcement.

Different jurisdictions. Different tools. Same supervisory pattern.

Expectations are forming before rules finalize — and long before penalties appear.

That’s what shows up later in:
- Board and audit committee questions
- nvestor and acquirer diligence
- Bank and payment-partner reviews

Not “what did the law eventually say?”
But “what did leadership understand and monitor as expectations evolved?”

I publish a short Digital Regulatory Intelligence brief each Tuesday that documents these early supervisory signals — not predictions, not legal conclusions — just what’s forming in real time.

Most regulatory risk doesn’t show up as a rule violation.

It shows up later as a question regulators now ask routinely in enforcement actions:

“When did you become aware of this risk?”

Across new digital regulations and recent enforcement matters, regulators have shifted from checklist compliance to supervisory posture — an expectation of continuous, documented awareness.

Exposure today isn’t just what you did.
It’s what you reasonably knew — and when you knew it.

That’s why this is increasingly a board-level issue, not a compliance exercise.

Curious how others are documenting supervisory awareness before it’s asked for.

When supervision exists only in email, it doesn’t exist at all.

This week didn’t bring a sweeping new regulation.
That’s exactly the risk.

Across the U.S. and EU, regulators continued applying existing authority while legislative clarity remains unresolved. That combination — active enforcement discretion + unresolved rules — is where most future compliance failures are born.

Fast-forward 24–36 months and the question won’t be:

“Was the guidance clear?”

It will be:
“What did you know — when did you know it — and how was that knowledge supervised?”

This week alone:

SEC and CFTC signaled regulatory coordination is moving forward without Congress
EU AI Act transparency expectations quietly locked in
FinCEN delayed rules while Treasury intensified enforcement
Enforcement volume dropped — but guidance-based expectations accelerated

None of this required a new rule. All of it creates exposure.

If your supervisory awareness lives in emails, Slack threads, or people’s heads, it won’t survive diligence, exams, or enforcement.

That’s why we published this week’s DigitalRegIntel Supervisory Brief — and why we believe supervisory awareness needs a system of record, not a newsletter.

Because when the question comes,
explanations don’t count. Records do.

This week’s brief formally documents each supervisory signal referenced above.

#RegulatoryCompliance #DigitalAssets #AIGovernance #RegTech #Compliance

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?

Most pricing consultancies hire from McKinsey or Simon-Kucher.
We hired Emily Sanz from American Airlines and British Airways.

When I was building out the Pricing I/O team, I had a choice. 
I could hire the typical profile: ex-consultants who'd spent their careers making slide decks about pricing but never actually owned a P&L.

Or I could find people who'd lived pricing in industries that do it better than almost anyone.

Emily had spent years in airline pricing. The joint business between them. 
She'd done consumer pricing, B2B pricing, and everything in between.

Airlines invented revenue management. 
They've been optimizing for 50+ years. 

They deal with infinite data points, razor-thin margins, and customers who compare prices obsessively. If you can do pricing there, you can do it anywhere.

What Emily brought to Pricing I/O that we couldn't have gotten from a traditional consultant:

→ An obsession with customer segmentation. 
Airlines don't have "3 tiers." They have hundreds of price points for the same seat, because different customers have different willingness to pay.

→ The concept of "quality of revenue." 
Not just how much you closed, but what kind of revenue it is. A discounted seat to fill the plane is not the same as a full-fare business traveler. Same applies to SaaS.

→ A data-first mindset. 
In aviation, you don't guess. You measure, test, and iterate constantly.

When she joined back in 2023, I asked her what surprised her most about SaaS pricing.

Her answer was about how little data most companies actually use. 
They have it. They just don't look at it.

We don't hire career consultants who've never operated.
We hire people who've lived pricing in industries that do it better than anyone, and bring those lessons to SaaS.

That's how we work in the trenches differently.

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

Four years ago, it was just me and a laptop.
This room held 25 people last meeting.

We gathered the Pricing I/O team for our end-of-year off-site, and something hit me during the planning session.

We're not just growing. 
We're building something that compounds.

→ 30% revenue growth year over year.
→ Pipeline doubled since Ali joined in Q3.
→ Client satisfaction scores hitting 10 out of 10.

But here's what really gets me: the debates in that room.

Some of them going back and forth on engagement models. Others challenging assumptions. 

The whole team leaning into problems that didn't exist when we were a team of four.

I used to think scaling a consulting firm meant losing the magic.
Turns out, with the right people, the magic multiplies.

2026 is going to be even better. 

We're cooking up something new, can't share details yet, but let's just say it involves working code and a launch window in Q1/Q2.

Stay tuned.

The best hires don't just check boxes, they make you rethink what the boxes should be.

Lauren DeGirolamo, MBA, CPP joined us as a Pricing Strategist back in December.

Before Pricing I/O, she was Director of Strategic Pricing at Baker Distributing, leading a team of 9. Before that, she spent nearly three years at Resideo navigating pricing through supply chain chaos.

One thing from her background that stuck with me: she restructured a pricing matrix from over 1,400 customer conditions down to 7.

That's not optimization. That's the kind of simplify-the-complex thinking that separates good pricing people from great ones.

When I asked her what surprised her most about joining us, she didn't mention the work or the clients.

She mentioned our offsite.
"This was the first time I had ever seen a CEO involving his entire team in high-level strategic decisions, listening with open ears to hear all perspectives."

That one got me.

Because that's exactly the culture we're building. 
Not top-down mandates. Real debate. Real input. Real ownership.

Lauren called it "the secret weapon behind Pricing I/O's success."
I'd have to agree.

Her spiciest pricing take?
"Lowering prices too much can actually hurt market share, it signals to enterprise buyers that your product is less capable."

Strong opinions backed by experience. That's what we look for.

Oh, and her karaoke song is "Remember the Name" by Fort Minor. 
If you know, you know.

Welcome to the team, Lauren. 
Love seeing you win already.

25 people. $10M bootstrapped. Zero outside funding.
And the culture is the best I've ever been a part of.

I'm not saying this to brag. 
I'm saying it because it almost didn't happen this way.

The original plan was me, myself, and I.

But here's what I've learned building Pricing I/O:
The company you build reflects the problems you solve.

We help companies capture value. 
So we had to build a company that captures its own value, through people, not just process.

We teach teams to own pricing long-term. 
So we had to build a team that owns their work, not just executes tasks.

We work in the trenches with clients. 
So we had to hire people who want to get their hands dirty, not just advise from a distance.

Every value we preach to clients, we had to live internally first.

That's what makes the culture real. It's not a poster on a wall. 
It's the business model.

We're still figuring it out. Still learning. Still making mistakes.

But building in public means being honest about the journey, not just the wins.

Clio scaled from 250 to 1,000+ employees.
Their pricing strategy? "Sporadic" and "on a needs basis."

Here's the thing most people miss about hyper-growth: 
the faster you scale, the more your pricing cracks show.

When we started working with Clio, they had a cultural resistance to monetization. 

The team genuinely wanted to deliver "outsized value" to customers, which sounds noble until you realize it was capping their ability to reinvest in the very innovation customers were asking for.

So we ran a full revision of all their products and service offerings.

The foundation? Their own "law firm maturity model", a blueprint that let us design pricing and packaging that grows alongside their customers, not against them.

The results hit across the board: NRR improved. ARPA improved. LTV improved.

But here's what changed the game long-term:
Pricing is now part of every single new product launch and release at Clio.

Not an afterthought. Not a "we'll figure it out later." A strategic lever from day one.
That's the shift. 

→ From reactive to proactive. 
→ From sporadic to systematic.

And it doesn't happen by accident, it happens when you treat pricing like the growth engine it actually is.

Me and Min S. FitzGerald talked about it on my podcast.
You can watch the full episode on Pricing IO Youtube channel.

Most pricing projects start with excitement and end with a deck nobody opens.
I've seen it happen dozens of times.

Great research. 
Solid recommendations. 
And then... nothing changes.

Why?

Sometimes it's because nobody owned the implementation.
Sometimes it's because the company wasn't ready, they didn't have a clear product strategy, and pricing can't fix that.

But when the foundation is solid, and you have a partner who works in the trenches with your team?

That's when pricing goes from "expense" to "investment."
Swipe through for what actually works, and what doesn't.

Failure

How long does it take your company to launch a new pricing model?

If the answer is "we'd need to check with engineering" or "probably a quarter," you have an infrastructure problem.

Most billing systems were architected when pricing was three simple tiers and annual contracts. 

Adding each new capability felt manageable, usage tracking here, credits there, custom packages for enterprise.

But step back and look at what you've built right now. It's infrastructure. 
Complex, mission-critical infrastructure that nobody planned for.

Now there's a gap opening up. 
Some companies can test new pricing in days. 
Others need months for basic changes.

Some can close deals with flexible packages. 
Others lose deals because their system literally can't support what the customer needs.

That gap isn't about engineering talent. 
It's about whether your monetization infrastructure was designed for the world we're in now or the world we left behind.

Tomorrow, Manish Choudhary from Flexprice and I are walking through how this shift happened, why it caught everyone off guard, and what separates companies that can move fast from companies that are stuck.

How Monetization Became Infrastructure and No One Noticed
January 15, 10:00 AM EST
Register here ↓
https://skd.so/LBzBi3

Vibe coding is going to kill more B2B SaaS companies than AI competition will.
Here's why ↓

Right now, there are CEOs downgrading every SaaS subscription to the cheapest tier, and building custom tools on top with Lovable, Bolt, and Claude.

The CRM becomes "just a dumb database."
The premium analytics features? Vibe-coded in an afternoon.
The RevOps workflows you charge extra for? Replaced by something stitched together over a weekend.

The implication for pricing is massive:
Differentiation that used to last 5-6 years in client-server days... then 1-2 years in SaaS... now lasts weeks or days.

When your customer can vibe-code a "good enough" version of your premium features themselves, your pricing power evaporates.

So what becomes the moat?
Two things:
→ Distribution (can they find you before they build it themselves?)
→ Depth of integration (are you so embedded they can't rip you out?)
→ Data (do you have proprietary data they can't replicate?)

If your pricing strategy assumes feature differentiation will protect you, you're already behind.

The companies that win will price for outcomes and depth of integration, not feature access.

Because features are getting commoditized faster than anyone predicted.
How are you thinking about this in your pricing model?

Credit-based pricing is an admission that you don't understand your value yet. 
I've been watching the AI pricing space closely, and here's what I'm seeing across dozens of companies ↓

They're adopting credits and tokens not because it's the best model, but because they're uncertain about two things:
→ What use cases will actually drive adoption
→ What value those use cases deliver

So they default to tracking costs (tokens, compute, API calls) instead of outcomes. 
And here's the thing, that's not necessarily wrong at this stage.

But there's an important distinction to make here.

Cost-plus credits tied to tokens or compute? 
That's transitional. 

When you're genuinely uncertain about value delivery, it buys you runway to learn. 
But it shouldn't be a destination.

Action-oriented credits tied to meaningful outcomes? 
That's different. 

Companies like Clay AI and HubSpot have built successful credit systems because the credits correlate to actions that derive value, updating a contact, enriching a record, taking a step that moves the business forward. 

Easy to understand, easy to connect to ROI.

The difference is whether credits measure what you spend or what the customer gains.

Three questions to ask yourself if you're running credits today:
1/ Are you tracking which actions correlate with customer success?
2/ Do you know which use cases drive the most willingness to pay?
3/ Do your credits reflect cost consumption or value delivered?

If your credits are just a proxy for compute, you're not pricing strategically, you're billing for infrastructure.

What's your take, are your credits measuring cost or value?

AI companies can capture more value than SaaS ever could.
Here's why ↓

On-premise software captured roughly 5% of the value it created. 
You were paying for access, that's it.

SaaS changed the game. 
Suddenly you could track activity, usage, outcomes. 
Value capture jumped to 10-15%.

AI changes it again. 
Usage patterns vary wildly. 
One customer might use 10x what another does, and get 10x the value.

The companies figuring this out are capturing far more than the old SaaS playbook allowed.

But here's what most are getting wrong:
They're still pricing AI like it's SaaS. Flat seats. Fixed tiers. One-size-fits-all.

That made sense when usage was predictable. It doesn't when one user generates 50 API calls and another generates 5,000.

The shift isn't just about pricing models. It's about how you think about value exchange entirely.

Your CFO now needs data science skills to understand AI unit economics. Performance margins can swing from 10% to 75% depending on which model you're running.

This is a different game with different rules.
Are you still playing by the old ones?

You're not going to lose your customers if you raise your prices.
I know. It doesn't feel that way.

I've heard this from hundreds of founders and CEOs. 
Smart people. Running great companies. 
Paralyzed by the thought that one pricing change will undo everything they've built.

Here's what actually happens:
We help them build the case. 
Research. Data. Competitive benchmarks. 
They see the evidence that they're underpriced.

Then comes the hard part: actually telling customers.
They start with a few. Hold their breath. Wait for the backlash.

And most of the time?
Customers say yes.

Not all of them. But enough. 
And suddenly the fear dissolves. 

They realize they were leaving money on the table for years because of a story they told themselves.

Here's what we've learned from 400+ engagements:
We almost never recommend lowering prices across the board.

What we find is companies are underpriced in segments they didn't realize, while over-discounting in others.

The problem isn't usually "we charge too much."
It's "we're not charging for the right things, to the right people, in the right way."

If you've been putting off a price increase because you're scared of the reaction, you're not alone.

But the fear is almost always bigger than the reality.

Nobody wakes up thinking "today's the day I fix pricing."
But by the time pricing feels urgent, you've already paid the price.

It usually gets pushed. Delayed. Deprioritized.

There's always something more urgent, a product launch, a reorg, a board deck due Friday.
Meanwhile, the warning signs pile up quietly.

Your best rep starts discounting 30% "just to stay competitive." Finance notices tier 1 is overflowing while tiers 2 and 3 collect dust. A prospect tells you "you're too expensive", then signs with a competitor charging 40% more.

Each one feels manageable in isolation.

But together? They're symptoms of pricing that stopped reflecting your value a long time ago.
The hardest part isn't the fix. 

It's admitting you've been leaving money on the table while focused on everything else.

I've seen this exact pattern in hundreds of companies. 

If you want to win it, start by recognizing the early signals and acting before the damage compounds quarter over quarter.

How many of these signals are showing up at your company right now?

$300K to $30M ARR in two and a half years.
100x growth.

One change: the pricing metric.

The product was a dispatching tool for insurance companies. Seat-based pricing.

Here's the problem, the better we performed, the less revenue we got.

Customers optimized routes, needed fewer seats, and downsized at renewal.
We were penalizing ourselves for delivering value.

So we tested five or six different metrics. Ran correlations. Talked to customers.

Per claim made the most sense.

It aligned with how insurance companies already tracked value and cost. 

Claims scaled with their business. Our revenue finally moved with theirs.

That's when I realized: pricing isn't a math problem. It's a value alignment problem.

The metric you choose determines whether you grow with your customers, or get left behind.

If you're adding AI to your SaaS product, you're about to face a pricing decision that most companies get wrong.

Do you charge per seat? Per usage? Per outcome? Some combination?

The answer depends on how you layer your pricing model. And there are really only three layers to work with.

Base 
Your platform, features, functionality. What customers get access to regardless of how much they use.

Allowance 
What's included before overage kicks in. This is where most companies mess up. Too little creates friction before customers see value. Too much kills your expansion revenue.

Meter 
The unit you charge for when they scale. Get this wrong and customers game the system. Get it right and revenue grows with their success.

Every hybrid pricing model is just a different combination of these three layers.

Access Tiers? Just Base. 
Platform fee plus usage? Base + Meter. 
Credits with overage? All three layers working together.

I broke down five models we see working in the market right now, with specific examples like Intercom Fin's $0.99/resolution and when each model fits best.

Here's what I've learned after 400+ pricing engagements: the model you pick matters less than whether it matches how your customers actually buy and scale.

Most companies copy competitors. 
The ones that win design for their own customer journey.
Which model fits how your customers grow?

Labeling something as "AI" actually lowers trust.
I know. It sounds backwards.

Every company is racing to slap "AI-powered" on their product. 
The assumption is it increases perceived value.

The data says otherwise.
In willingness-to-pay research, the AI label barely moves the needle on what customers will pay. And in some cases, it actively creates skepticism.

Why?
Because the market is shifting from "play AI" to "may I", as in, "may I see proof this actually works?"

Early adopters bought the hype. 
The next wave of buyers wants evidence.

ClickUp figured this out. 
Their standard plan is $7. Their AI plan is $28, 4x the price.

But they didn't just slap "AI" on it and hope for the best.
Their positioning: "This replaces 4-5 other AI tools you're already paying for."

The value prop isn't "we have AI." 
It's "we simplify and centralize what you're already doing across multiple tools."

Simplification + consolidation = worth the premium.

The companies winning at AI pricing aren't selling AI. They're selling outcomes that happen to be powered by AI.

What are you actually selling?

If your sales team doesn't believe your solution is worth the price, they won't sell it.

They'll discount it. They'll apologize for it. They'll position it as "a bit expensive, but..."

I had a client where the salesperson literally felt bad charging customers. 
Not because the product was bad. Because SHE didn't believe it was worth what they were asking.

That feeling? It bleeds into every demo, every negotiation, every close.

You can have the perfect pricing model. The perfect packaging. The perfect positioning.

But if your own team doesn't buy what they're selling, your customers sure as hell won't.

The problem isn't your price. 
It's that you never convinced your team of the value first.

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Pragmatic Marketing, Product Management Certification Level 4

Invited as guest lecturer for lab to market capstone courses to speak on topics of product management, general management, and technology trends

I managed to work my way through all four years of college while achieving Provost honors.

Warren College - Minored in Japanese Language and Studies.

Hi, I'm Marcos Rivera

•  I have 25+ years of pricing expertise in Tech
•  I was the Head of Pricing for Vista Equity Partners
•  I have helped price 400 products...and counting​
•  I teach pricing confidence from MBA students to world-class CEOs
•  I am an award-winning author, speaker, investor, and entrepreneur

Here's My Story in a Nutshell

I was raised by a single mother in a one-bedroom apartment in the Bronx. The streets were hard on me, but I survived by applying what my mom taught me:  Stay curious, take the lead, and give help.

Fast forward to today, Software as a Service has transformed almost every business, from music to marketing to medicine. I learned a lot straddling dual roles as a product executive and pricing expert my entire career, but my AHA moment came to me after noticing an unwavering pattern:

Most B2B SaaS leaders spend time building value but not enough time capturing it!

So in the spirit of staying curious, I discovered that many B2B SaaS leaders see pricing as a "black box"; they guess, fumble, and wing it most of the time.  They needed help!

From there, I decided to take the lead and help B2B SaaS businesses by dedicating a practice that teaches them step-by-step directions for monetizing value and unlocking growth.

And this is where our story begins. Join us and take control of your most powerful growth lever - Pricing.  It would be my honor to help you! Send me a DM or head to our website: www.pricingio.com

You can also order my debut book, Street Pricing: A Pricing Playlist for Hip Leaders in B2B SaaS (link in bio)

Go capture that value!

Marcos

For this benchmark report, PricingSaaS has examined over 400 B2B SaaS companies across 10 different categories to uncover AI monetization strategies, usage based pricing metrics and created a recap of changes in Q1 of 2024.

We hope you find it helpful and insightful.

2024 B2B SaaS Pricing Q1 Benchmarks & Models

Pricing I/O is a training and coaching boutique helping high-growth B2B SaaS companies accelerate ARR growth and market share. We unlock growth by taking a simple, powerful, and easy to understand approach to monetization – we call this approach the 5Q Pricing Framework.

Pricing I/O: Your B2B SaaS Pricing Experts

25 years of pricing. 500+ SaaS companies.
One conversation where I held nothing back.

Rohit asked me the questions most people are afraid to ask:
→ What's the single biggest pricing mistake you see repeated?
→ How do you know if it's a pricing problem or something else?
→ Should AI features be bundled or priced separately?
→ What made you change your mind on pricing cadence?

I shared the 100x growth story I don't tell often.

Admitted where I got it wrong.

And broke down how to actually think about pricing AI, not the theory, the real decisions.

If you've got 35 minutes, this one's worth it.

Watch the full episode of The AI Pricing Podcast ↓
https://skd.so/OCIaV6

Should you charge for AI separately or bake it into the product?
I get this question constantly. 

And the honest answer is: it depends on what your AI actually does.

I've seen companies destroy expansion revenue by giving AI away for free when they should have charged. 

I've also seen companies kill adoption by charging separately for AI that should have been invisible.

The mistake is thinking there's one right answer. There isn't.

After working through this with dozens of companies, from $10M ARR startups to PE-backed portfolio companies, I keep coming back to three distinct approaches.

The first works when AI enhances the existing experience. 
Makes things faster, smarter, better, but the job-to-be-done is the same. 

Think autocomplete, smart suggestions. 
In this case, don't charge separately. 
Bake it in and raise prices across all plans.

The second works when AI unlocks adjacent use cases. 
Same user, but now they can do something they couldn't before. 
This is your premium tier or add-on play.

The third works when AI creates entirely new users or workflows. 
Different audience, different product. 

This needs its own identity, maybe bundled, maybe standalone.

The question isn't "should we charge for AI?" It's "where does AI create value, and for whom?"

Which of these three describes your AI?

Most SaaS companies treat usage limits as technical guardrails.
Big mistake.

They’re your upgrade engine.

Data retention windows. 
Source limits. 
Processing hours. 

These aren’t just product constraints, they’re monetization strategy.

The key: Set limits where customers naturally hit them as they get MORE value. Not where they get frustrated.

When customers hit limits, they’re succeeding. That’s when they upgrade.

Here’s what that looks like:
→ Starter: 3 sources, 30-day retention
→ Pro: 10 sources, 90-day retention
→ Enterprise: Unlimited, 1 year+

That structure creates a $47K ARR expansion path per customer.
These should feel like growth milestones, not arbitrary restrictions.

What usage limit is driving the most upgrades in your model?

Your packaging model isn't broken because it's "wrong."
It's broken because you outgrew it.

Here are 5 signals I see when companies need to evolve their packaging:

1. Sales is discounting on every deal to "make it fit."

When your tiers don't match how customers buy, reps start hacking. Custom bundles. Creative discounting. Anything to close.

That's not a sales problem. It's a packaging problem.

2. Customers cluster in one tier while others sit empty.
If 80% of customers are in your middle tier and the other two barely move, your packaging isn't giving them a reason to choose.

3. You're building features no one's paying for.
Product ships something great. It gets buried in "Enterprise." No one upgrades for it.

That's a signal the feature deserves its own packaging conversation.

4. Expansion revenue is flat even though usage is up.
Customers are using more. Getting more value. But NRR isn't moving.

Your model isn't capturing the growth happening inside accounts

5. The pricing conversation dominates every sales call.
When customers spend more time asking "what's included where?" than talking about their problems, packaging is creating friction, not reducing it.

None of these mean you picked the wrong model.
They mean your business evolved and your packaging didn't keep up.

Here's the thing about packaging: it's not a one-time decision.

Companies that treat it like infrastructure instead of a living system end up stuck, either too rigid to capture value or too complex to sell efficiently.

The best packaging matches your stage. 
And as you grow, it should grow too.

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taplio

The Battle

Steve Litzow

Marcos Rivera

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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);}Steve Litzow's LinkedIn Performance

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