Learn Why Your AI Pricing is Failing and How to Fix It from Marcos Rivera
Learn Why Your AI Pricing is Failing and How to Fix It from Marcos Rivera
Learn Why Your AI Pricing is Failing and How to Fix It from Marcos Rivera
Learn Why Your AI Pricing is Failing and How to Fix It from Marcos Rivera
Jan 13, 2026
Jan 13, 2026
Jan 13, 2026
• 12 min read
• 12 min read
• 12 min read
Aanchal Parmar
Aanchal Parmar
Aanchal Parmar
Product Marketing Manager, Flexprice
Product Marketing Manager, Flexprice
Product Marketing Manager, Flexprice
The board meeting always follows the same script.
"Where's our AI strategy?" someone asks. "Competitor X just launched AI features. We need something by Q2."
And just like that, teams scramble. Engineers explore LLMs, Product managers draft roadmaps and Marketing prepares launches.
But there's one conversation nobody's having, “How do we price this thing?”
Marcos Rivera has seen this movie play out hundreds of times. As the former head of pricing at Vista Equity Partners and founder of Pricing I/O, he's priced over 500 B2B SaaS products. And in 2025, he's watching a painful pattern repeat.
"Nobody agrees on what they need to do, where it needs to go in the packaging, what the goal is," Marcos says. "They just got to do AI something. The board wants to see AI."
That vague urgency is creating a pricing nightmare. Companies are copying competitor models that don't fit their use case. They're confusing customers with meaningless metrics like "actions." They're bleeding margins because one AI agent might operate at 60% margins while another runs at 10%.
TL;DR
Most B2B SaaS companies are botching AI pricing because they're copying competitors without understanding their own strategy. After pricing 500+ products, Marcos Rivera identifies three frameworks that actually work:
Framework #1: Underscore the Core - If AI improves your existing features. Fold it into your product and raise prices 10%+ because you're delivering faster time-to-value.
Framework #2: Upgrade the More - AI extends your product into premium territory. Launch as an add-on first, collect 2-4 quarters of data, then decide whether to fold in or keep separate.
Framework #3: Unlock the New - AI creates entirely new capabilities. Price it standalone with fixed + variable structures tied to outcomes (like transactions processed or claims approved). Define the "anti-outcome" to avoid billing disputes.
Key mistakes to avoid: Vague metrics like "credits" or "actions" that customers can't understand. Jumping from pure subscription to pure usage-based pricing without hybrid transitions. Adding pricing complexity on top of product complexity when users are already climbing the AI learning curve.
The safe default when uncertain: Launch as an add-on, watch who buys it and how they use it, then decide based on real data rather than assumptions.
AI is moving too fast for two-year pricing cycles. Review quarterly or bi-quarterly. Strategy clarity comes first you can't price what you can't define.
The old SaaS pricing playbook doesn't work anymore.
But after analyzing hundreds of companies, Marcos has identified three frameworks that actually work for AI pricing. Not theory, what actually drives revenue while protecting margins.
Let's break down why most companies are getting this wrong, and how to fix it.
1. The Current State of AI Pricing Chaos
Walk into any B2B SaaS company right now and ask the product team what their AI strategy is. Then ask sales. Then ask the pricing lead.
You'll get three completely different answers.
This isn't just organizational misalignment, it's creating real financial damage. And Marcos has a front-row seat to the carnage.
2. The "Something with AI" Problem
The pressure is coming from everywhere. Investors want to see AI in the roadmap. Competitors are announcing AI features. Customers are asking about it. So companies rush to ship something, just anything with artificial intelligence baked in.
But what is this AI actually supposed to do?
Marcos is blunt about what he's seeing: Companies can't answer basic questions.
Is this AI enhancing your core product?
Is it extending into new use cases?
Is it targeting a completely different customer?
Without clarity on strategy, pricing becomes impossible. You can't price what you can't define.
3. Three Expensive Mistakes
The chaos is manifesting in predictable patterns:
a. Mistake #1: The Copycat Trap
Someone sees a competitor launch "credits" or charge "per action," and suddenly that's the plan.
Never mind that credits mean nothing to customers.
Never mind that "action" could mean literally anything.
It looks like modern pricing, so it gets copied wholesale.
Marcos recently saw a company introduce "per action" pricing. When he asked what an action was, the answer was: "Well, it could mean this and it could mean that."
That's not a pricing model. That's just confusion with a price tag.
b. Mistake #2: Complexity Stacked on Complexity
Your customers are already trying to figure out how to use AI. They're learning new workflows, new interfaces, new ways of thinking about their work.
And now you're adding pricing models they need a decoder ring to understand?
c. Mistake #3: The Margin Blindspot
Not all AI features are created equal from a cost perspective.
Traditional SaaS could get away with stable, high margins. Build the feature once, serve it to thousands of customers, margins stay intact.
But with AI agents, one might cost you 10% of revenue to operate. Another might cost 40%. And if you're pricing them the same way, you're bleeding money on every transaction.
Why this matters more than ever
Sam Altman and other LLM CEOs are predicting 2025-26 as the "year of AI agents." Marcos thinks they're being optimistic, 2027-28 is more realistic. Humans need time to change processes, rethink workflows, actually adopt these tools.
And that timeline gives you breathing room to get pricing right before the agent revolution fully hits. Companies that figure this out now will dominate when agents become table stakes.
The ones still fumbling with vague "credit" systems and copied pricing models will be stuck explaining to their board why margins collapsed and customers churned.
And there's a better way. And it starts with understanding which of three frameworks actually fits what you're building.
Framework #1: Underscore the Core
Let's start with the simplest and often most overlooked approach to AI pricing.
Underscore the Core
Marcos breaks it down simply: "Underscore the core’ means that your strategy is to enhance what you already do, your search, workflows, decision-making, and core functionality.
If AI makes those things better, faster, or smarter, then you should fold it into the product itself, and reflect that improvement in your pricing.”
This is the unglamorous truth about most AI implementations. You're not building Jarvis from Iron Man. You're making your existing search faster. Your workflows are smoother. Your analytics sharper.
The fundamental job-to-be-done hasn't changed, you're just helping customers complete it with less friction and more speed.
And here's what makes this powerful: some of this AI is visible to users, some runs invisibly in the background.
A customer might see AI-suggested actions in their dashboard, but not see the AI pre-processing data, optimizing queries, or predicting their next need. Both types deliver value. Both justify higher pricing.
When this framework actually fits
The mistake companies make is thinking every AI feature needs its own SKU, its own add-on, its own pricing tier. Sometimes the smartest move is integration, not separation.
You're in Underscore territory when:
Your AI gets users to value faster without changing what that value is
The enhancement feels inevitable, like "of course this should work this way"
Users would struggle to separate "the AI part" from "the product part"
The cost structure remains relatively stable and predictable
Think about spell-check in Microsoft Word. Nobody pays extra for it. Nobody thinks of it as a separate feature. It's just... how Word works now. That's what Underscore the Core AI should feel like.
Fold it In, then raise 10%+
Here's where Marcos pushes back against the 3-5% increases most companies consider.
"I’m not talking about small increases like 5%. I mean taking prices up by 10% or more especially when users can clearly see the impact of the AI, and even when some of that impact is invisible.
Either way, the AI is getting users to value faster. It’s helping them accomplish their jobs and use cases more quickly, more effectively, or more deeply."
Why does 10%+ work when 5% feels aggressive?
Because you're not just adding a feature. You're compressing time. You're eliminating frustration. You're making your product do what customers always wished it would do.
Consider the math from a customer's perspective: If your AI cuts the time to complete a workflow from 30 minutes to 10 minutes, you've just given them back 20 minutes per workflow.
If they run that workflow 50 times a month, that's 16+ hours saved. What's that worth? Way more than 10%.
The trick is making sure customers feel that time compression. If the improvement is invisible or incremental, you'll face resistance. If it's obvious and immediate, the price increase becomes a no-brainer.
Getting this right
Don't lead with "We added AI." Nobody wakes up wanting AI. They wake up wanting their work to be easier.
Your announcement isn't "Introducing AI-powered search!" It's "Find what you need 70% faster with intelligent search that understands context."
The AI is the how. The time saved is the what. The outcome is the why.
Timing the increase: Marcos is clear on this: "Fold it in and raise your prices right? I'm not talking 5% raises."
But when matters as much as how much. Roll the price increase when you launch the AI features, not after. The value jump and the price jump need to happen simultaneously.
Because customers understand paying more for getting more when it's obvious they're getting more. Launch AI in January, raise prices in June, and you're asking them to remember what changed six months ago. Launch both together, and the connection is immediate.
The grandfathering decision: Every company struggles with this. Do existing customers get the new AI at their old price? Or does everyone move to new pricing?
There's no universal answer, but here's Marcos's framework: if the AI delivers meaningfully more value and we're only talking about Underscore the Core, if it does then existing customers are getting that value too.
You can soften the blow with timing (6-month notice, grandfather until next renewal) or with transitional pricing (halfway between old and new for the first year).
But ultimately, if you've truly enhanced your core product, your existing customers are benefiting. They should expect to pay for that benefit.
The companies that get in trouble are the ones who under-communicate. They assume customers will notice the improvements and understand the value. Wrong.
You need to actively message what changed, why it matters, and how it makes their work better.
The margin math that makes or break this
Here's where the rubber meets the road. Underscore the Core only works if your AI costs are relatively stable and predictable.
Marcos is watching companies miss this crucial detail. They fold AI into base pricing, then realize customer usage varies wildly.
One customer's AI enhancement costs them $2/month. Another costs $47/month. Same price point, completely different margins.
"The margins are high and stable, and as you keep building and extending the platform, bolting on modules, adding integrations, the margins kind of stay intact. But not in AI, not with agents.
You can have one agent with a 60% margin and another one with a 10% margin. It just depends on the cost, how much tokens it's consuming and how much users are using it."
This is why Underscore the Core works best for AI that enhances existing features in relatively uniform ways.
If your AI is optimizing database queries in the background, or improving search relevance, or suggesting next actions and these costs don't spike dramatically based on user behavior, flat pricing makes sense.
But if your AI costs are driven by consumption, if heavy users genuinely cost you 10x what light users cost you need to look at Framework #2 or #3.
Trying to force Underscore the Core pricing onto variable-cost AI is how you accidentally build a company that loses money as customers adopt your product.
Which is, you know, the opposite of the goal.
Framework #2: Upgrade the More
Marcos describes this as taking your product "a few steps further" with AI. Your product used to end here. But now, with AI, you can go several steps beyond that endpoint.
"Some companies where it's 'I think we're just taking the things a bit further. We could—our products ended here, but now we can go a few steps further with AI.' And this gets into more complex territory. You want to upgrade the more. This is more jobs, more use cases or more extension of the product."
This isn't about making existing features better. It's about unlocking capabilities that were previously impossible or impractical. You're expanding the scope of what your product can do.
The key distinction from Underscore the Core: not every customer needs this. In fact, many won't. And that's perfectly fine, it's what makes this a premium or add-on play.
When does this framework make sense
You're in Upgrade the More territory when:
Your AI enables new use cases that extend beyond your original value proposition
The capabilities feel "advanced" or "power user" rather than fundamental
Different customer segments have dramatically different appetite for these features
The enhancement is substantial enough that users can clearly distinguish it from the base product
Think of it this way: if someone could use your product effectively without this AI feature, but power users or larger customers would jump at the chance to have it, you're looking at Upgrade the More.
The pricing model: Premium tiers or add-ons
Marcos offers two paths here, and they're not mutually exclusive.
Path 1: Premium Plan Placement Put these AI capabilities in your higher-tier plans. Your Enterprise plan gets AI-powered forecasting. Your Professional plan gets advanced automation. Your Basic plan works just fine without them.
This creates natural segmentation and gives customers a reason to upgrade beyond just "more seats" or "higher usage limits."
Path 2: The Add-On Strategy Offer the AI enhancement as a standalone add-on that any tier can purchase. This works particularly well when you're unsure about demand or still figuring out who values this most.
And here's the safety net Marcos recommends: "If you're not sure, if you're like, 'Well, Marcos, it kind of is all over,' like, that probably means you're not clear with what you're trying to do, but the safest path would be add-on first and then decide if you want to fold it in or keep it separate based on the usage patterns, who's buying it, and what they're doing."
Marcos points to a clear pattern among major players: "Gemini did it, Slack did it, Notion did it, they had the add-on for a while. I think it was like 10 bucks a user, and then they ended up folding it in after that."
The strategy here is straightforward: launch as an add-on, watch what happens, then decide whether to integrate it more deeply into your pricing structure.
These companies didn't immediately try to build the perfect tiered structure. They didn't agonize over whether AI belonged in Professional vs. Enterprise. They launched it as an add-on at around $10 per user, collected data on adoption and usage, and made informed decisions later about integration.
That's the pattern worth following.
The add-on testing strategy: Collecting the right data
If you go the add-on route and Marcos strongly suggests you should when uncertain—you need to know what you're measuring.
Who's buying? Is it your largest customers? Your most sophisticated users? Specific industries or use cases? If only Enterprise customers are buying, that's a signal it belongs in your top tier. If it's spread across all segments, maybe it should be in the core product with
Framework #1.
Usage patterns matter Are customers who buy the add-on using it daily? Weekly? Or did they buy it and barely touch it? High engagement says keep it separate and premium. Low engagement says you might have a product-market fit problem, not a pricing problem.
Willingness to pay indicators How many customers ask about it during sales calls? How often do existing customers inquire about adding it? Are they price-sensitive, or do they buy without hesitation? These signals tell you how much value the market perceives.
The timeline question Marcos suggests giving it 2-4 quarters to see real patterns. One quarter isn't enough you'll catch early adopters but miss the mainstream. A full year might be too long if you're in a fast-moving market.
The sweet spot? Two quarters minimum. Four quarters if you want high confidence in your data.
Sometimes the data tells you something unexpected: different customer segments value the same AI feature for completely different reasons.
A project management tool might find that small teams use AI for automated task assignment (they lack dedicated project managers), while large enterprises use it for cross-project resource optimization (they have too many projects to manually coordinate).
Same AI. Different jobs-to-be-done. Different willingness to pay.
When you see this, you might need hybrid approaches: include basic AI functionality in the core, but gate advanced capabilities as premium features or add-ons. Framework #1 plus
Framework #2.
The companies that struggle are the ones that force a single strategy across diverse customer needs. The ones that win are comfortable with nuance.
The Margin Consideration Redux
Remember Marcos's warning about margin variability? It applies here too, but differently.
With Upgrade the More, you're often dealing with more compute-intensive AI. Advanced forecasting models. Complex automation chains. Deep analytical processing. These features genuinely cost more to deliver.
If your margin on these features is tight, that's actually an argument for keeping them separate. Charge appropriately for the value and the cost. Don't subsidize power users by spreading costs across your entire customer base.
But if margins are healthy and stable even on advanced features, and adoption is broad, that's a signal to fold them into tiered pricing rather than maintaining them as perpetual add-ons.
The key question: Does separating this feature give you pricing flexibility you need, or is it just creating complexity that frustrates customers?
Only your data can answer that.
Framework #3: Unlock the New
Sometimes AI doesn't just enhance or extend your product. Sometimes it breaks open entirely new possibilities—new users, new use cases, new markets you couldn't touch before.
That's when you need the third framework.
Unlock the New: When AI Changes the Game
Marcos describes this as the most transformative approach: "Unlock the new—whole new use case, maybe even a different user, a different TAM, something different. You're able to isolate that, charge it as a standalone, and then you can even bundle it with your core and your platform as well."
This isn't about making your product better or taking it further. This is about doing something fundamentally different. Different enough that it could stand alone. Different enough that it might attract customers who would never buy your core product.
The clearest signal you're in Unlock the New territory: you could spin this into a separate company, and it would make sense.
When This Framework Applies
You're looking at Framework #3 when:
Your AI enables completely new use cases that weren't possible before
You're targeting a different user persona or buying center
The capability could attract a different TAM or market segment entirely
This AI could displace another product category
The technology creates genuine competitive differentiation, not just incremental improvement
This is the realm of agents doing work autonomously. Of AI that doesn't assist humans but handles entire workflows independently. Of capabilities that make customers rethink their entire process, not just optimize it.
The Pricing Model: Standalone with Bundle Options
Marcos is clear on the approach here: price it as a separate product, but create strategic bundle opportunities with your core platform.
Why standalone? Because the value proposition is distinct enough that it deserves its own positioning, its own pricing logic, its own go-to-market motion. Forcing it into your existing pricing structure would confuse customers and likely leave money on the table.
Why bundle options? Because if you already have customer relationships and a platform they trust, offering this new capability as part of a broader solution creates competitive moats. It's harder for a startup to displace you when customers are getting multiple products that work together seamlessly.
"It also gives you some competitive advantages to be able to use it to penetrate new markets," Marcos notes. This is your wedge into territories where your core product never gained traction.
Real-World Examples: The Agent Revolution
This is where the agent companies are playing, and they're writing the playbook in real-time.
Marcos calls out several: "Zendesk and Intercom do it, ChargeFlo does it, DecaGon, all these different companies that have some agentic solution."
What unites them? They're not positioning AI as a feature enhancement. They're positioning autonomous systems that handle outcomes.
The pattern emerging: These companies are using a fixed + variable pricing structure. The fixed component covers configuration, training, onboarding—the setup work. The variable component is tied to a unit of work the agent actually completes.
Marcos breaks down the specifics: "Maybe that unit of work is transactions like authorizations approved like I've seen in health tech or legal discovery packages summarized like in legal tech."
Notice what's happening here: the pricing isn't about usage of the AI. It's about outcomes the AI delivers.
The board meeting always follows the same script.
"Where's our AI strategy?" someone asks. "Competitor X just launched AI features. We need something by Q2."
And just like that, teams scramble. Engineers explore LLMs, Product managers draft roadmaps and Marketing prepares launches.
But there's one conversation nobody's having, “How do we price this thing?”
Marcos Rivera has seen this movie play out hundreds of times. As the former head of pricing at Vista Equity Partners and founder of Pricing I/O, he's priced over 500 B2B SaaS products. And in 2025, he's watching a painful pattern repeat.
"Nobody agrees on what they need to do, where it needs to go in the packaging, what the goal is," Marcos says. "They just got to do AI something. The board wants to see AI."
That vague urgency is creating a pricing nightmare. Companies are copying competitor models that don't fit their use case. They're confusing customers with meaningless metrics like "actions." They're bleeding margins because one AI agent might operate at 60% margins while another runs at 10%.
TL;DR
Most B2B SaaS companies are botching AI pricing because they're copying competitors without understanding their own strategy. After pricing 500+ products, Marcos Rivera identifies three frameworks that actually work:
Framework #1: Underscore the Core - If AI improves your existing features. Fold it into your product and raise prices 10%+ because you're delivering faster time-to-value.
Framework #2: Upgrade the More - AI extends your product into premium territory. Launch as an add-on first, collect 2-4 quarters of data, then decide whether to fold in or keep separate.
Framework #3: Unlock the New - AI creates entirely new capabilities. Price it standalone with fixed + variable structures tied to outcomes (like transactions processed or claims approved). Define the "anti-outcome" to avoid billing disputes.
Key mistakes to avoid: Vague metrics like "credits" or "actions" that customers can't understand. Jumping from pure subscription to pure usage-based pricing without hybrid transitions. Adding pricing complexity on top of product complexity when users are already climbing the AI learning curve.
The safe default when uncertain: Launch as an add-on, watch who buys it and how they use it, then decide based on real data rather than assumptions.
AI is moving too fast for two-year pricing cycles. Review quarterly or bi-quarterly. Strategy clarity comes first you can't price what you can't define.
The old SaaS pricing playbook doesn't work anymore.
But after analyzing hundreds of companies, Marcos has identified three frameworks that actually work for AI pricing. Not theory, what actually drives revenue while protecting margins.
Let's break down why most companies are getting this wrong, and how to fix it.
1. The Current State of AI Pricing Chaos
Walk into any B2B SaaS company right now and ask the product team what their AI strategy is. Then ask sales. Then ask the pricing lead.
You'll get three completely different answers.
This isn't just organizational misalignment, it's creating real financial damage. And Marcos has a front-row seat to the carnage.
2. The "Something with AI" Problem
The pressure is coming from everywhere. Investors want to see AI in the roadmap. Competitors are announcing AI features. Customers are asking about it. So companies rush to ship something, just anything with artificial intelligence baked in.
But what is this AI actually supposed to do?
Marcos is blunt about what he's seeing: Companies can't answer basic questions.
Is this AI enhancing your core product?
Is it extending into new use cases?
Is it targeting a completely different customer?
Without clarity on strategy, pricing becomes impossible. You can't price what you can't define.
3. Three Expensive Mistakes
The chaos is manifesting in predictable patterns:
a. Mistake #1: The Copycat Trap
Someone sees a competitor launch "credits" or charge "per action," and suddenly that's the plan.
Never mind that credits mean nothing to customers.
Never mind that "action" could mean literally anything.
It looks like modern pricing, so it gets copied wholesale.
Marcos recently saw a company introduce "per action" pricing. When he asked what an action was, the answer was: "Well, it could mean this and it could mean that."
That's not a pricing model. That's just confusion with a price tag.
b. Mistake #2: Complexity Stacked on Complexity
Your customers are already trying to figure out how to use AI. They're learning new workflows, new interfaces, new ways of thinking about their work.
And now you're adding pricing models they need a decoder ring to understand?
c. Mistake #3: The Margin Blindspot
Not all AI features are created equal from a cost perspective.
Traditional SaaS could get away with stable, high margins. Build the feature once, serve it to thousands of customers, margins stay intact.
But with AI agents, one might cost you 10% of revenue to operate. Another might cost 40%. And if you're pricing them the same way, you're bleeding money on every transaction.
Why this matters more than ever
Sam Altman and other LLM CEOs are predicting 2025-26 as the "year of AI agents." Marcos thinks they're being optimistic, 2027-28 is more realistic. Humans need time to change processes, rethink workflows, actually adopt these tools.
And that timeline gives you breathing room to get pricing right before the agent revolution fully hits. Companies that figure this out now will dominate when agents become table stakes.
The ones still fumbling with vague "credit" systems and copied pricing models will be stuck explaining to their board why margins collapsed and customers churned.
And there's a better way. And it starts with understanding which of three frameworks actually fits what you're building.
Framework #1: Underscore the Core
Let's start with the simplest and often most overlooked approach to AI pricing.
Underscore the Core
Marcos breaks it down simply: "Underscore the core’ means that your strategy is to enhance what you already do, your search, workflows, decision-making, and core functionality.
If AI makes those things better, faster, or smarter, then you should fold it into the product itself, and reflect that improvement in your pricing.”
This is the unglamorous truth about most AI implementations. You're not building Jarvis from Iron Man. You're making your existing search faster. Your workflows are smoother. Your analytics sharper.
The fundamental job-to-be-done hasn't changed, you're just helping customers complete it with less friction and more speed.
And here's what makes this powerful: some of this AI is visible to users, some runs invisibly in the background.
A customer might see AI-suggested actions in their dashboard, but not see the AI pre-processing data, optimizing queries, or predicting their next need. Both types deliver value. Both justify higher pricing.
When this framework actually fits
The mistake companies make is thinking every AI feature needs its own SKU, its own add-on, its own pricing tier. Sometimes the smartest move is integration, not separation.
You're in Underscore territory when:
Your AI gets users to value faster without changing what that value is
The enhancement feels inevitable, like "of course this should work this way"
Users would struggle to separate "the AI part" from "the product part"
The cost structure remains relatively stable and predictable
Think about spell-check in Microsoft Word. Nobody pays extra for it. Nobody thinks of it as a separate feature. It's just... how Word works now. That's what Underscore the Core AI should feel like.
Fold it In, then raise 10%+
Here's where Marcos pushes back against the 3-5% increases most companies consider.
"I’m not talking about small increases like 5%. I mean taking prices up by 10% or more especially when users can clearly see the impact of the AI, and even when some of that impact is invisible.
Either way, the AI is getting users to value faster. It’s helping them accomplish their jobs and use cases more quickly, more effectively, or more deeply."
Why does 10%+ work when 5% feels aggressive?
Because you're not just adding a feature. You're compressing time. You're eliminating frustration. You're making your product do what customers always wished it would do.
Consider the math from a customer's perspective: If your AI cuts the time to complete a workflow from 30 minutes to 10 minutes, you've just given them back 20 minutes per workflow.
If they run that workflow 50 times a month, that's 16+ hours saved. What's that worth? Way more than 10%.
The trick is making sure customers feel that time compression. If the improvement is invisible or incremental, you'll face resistance. If it's obvious and immediate, the price increase becomes a no-brainer.
Getting this right
Don't lead with "We added AI." Nobody wakes up wanting AI. They wake up wanting their work to be easier.
Your announcement isn't "Introducing AI-powered search!" It's "Find what you need 70% faster with intelligent search that understands context."
The AI is the how. The time saved is the what. The outcome is the why.
Timing the increase: Marcos is clear on this: "Fold it in and raise your prices right? I'm not talking 5% raises."
But when matters as much as how much. Roll the price increase when you launch the AI features, not after. The value jump and the price jump need to happen simultaneously.
Because customers understand paying more for getting more when it's obvious they're getting more. Launch AI in January, raise prices in June, and you're asking them to remember what changed six months ago. Launch both together, and the connection is immediate.
The grandfathering decision: Every company struggles with this. Do existing customers get the new AI at their old price? Or does everyone move to new pricing?
There's no universal answer, but here's Marcos's framework: if the AI delivers meaningfully more value and we're only talking about Underscore the Core, if it does then existing customers are getting that value too.
You can soften the blow with timing (6-month notice, grandfather until next renewal) or with transitional pricing (halfway between old and new for the first year).
But ultimately, if you've truly enhanced your core product, your existing customers are benefiting. They should expect to pay for that benefit.
The companies that get in trouble are the ones who under-communicate. They assume customers will notice the improvements and understand the value. Wrong.
You need to actively message what changed, why it matters, and how it makes their work better.
The margin math that makes or break this
Here's where the rubber meets the road. Underscore the Core only works if your AI costs are relatively stable and predictable.
Marcos is watching companies miss this crucial detail. They fold AI into base pricing, then realize customer usage varies wildly.
One customer's AI enhancement costs them $2/month. Another costs $47/month. Same price point, completely different margins.
"The margins are high and stable, and as you keep building and extending the platform, bolting on modules, adding integrations, the margins kind of stay intact. But not in AI, not with agents.
You can have one agent with a 60% margin and another one with a 10% margin. It just depends on the cost, how much tokens it's consuming and how much users are using it."
This is why Underscore the Core works best for AI that enhances existing features in relatively uniform ways.
If your AI is optimizing database queries in the background, or improving search relevance, or suggesting next actions and these costs don't spike dramatically based on user behavior, flat pricing makes sense.
But if your AI costs are driven by consumption, if heavy users genuinely cost you 10x what light users cost you need to look at Framework #2 or #3.
Trying to force Underscore the Core pricing onto variable-cost AI is how you accidentally build a company that loses money as customers adopt your product.
Which is, you know, the opposite of the goal.
Framework #2: Upgrade the More
Marcos describes this as taking your product "a few steps further" with AI. Your product used to end here. But now, with AI, you can go several steps beyond that endpoint.
"Some companies where it's 'I think we're just taking the things a bit further. We could—our products ended here, but now we can go a few steps further with AI.' And this gets into more complex territory. You want to upgrade the more. This is more jobs, more use cases or more extension of the product."
This isn't about making existing features better. It's about unlocking capabilities that were previously impossible or impractical. You're expanding the scope of what your product can do.
The key distinction from Underscore the Core: not every customer needs this. In fact, many won't. And that's perfectly fine, it's what makes this a premium or add-on play.
When does this framework make sense
You're in Upgrade the More territory when:
Your AI enables new use cases that extend beyond your original value proposition
The capabilities feel "advanced" or "power user" rather than fundamental
Different customer segments have dramatically different appetite for these features
The enhancement is substantial enough that users can clearly distinguish it from the base product
Think of it this way: if someone could use your product effectively without this AI feature, but power users or larger customers would jump at the chance to have it, you're looking at Upgrade the More.
The pricing model: Premium tiers or add-ons
Marcos offers two paths here, and they're not mutually exclusive.
Path 1: Premium Plan Placement Put these AI capabilities in your higher-tier plans. Your Enterprise plan gets AI-powered forecasting. Your Professional plan gets advanced automation. Your Basic plan works just fine without them.
This creates natural segmentation and gives customers a reason to upgrade beyond just "more seats" or "higher usage limits."
Path 2: The Add-On Strategy Offer the AI enhancement as a standalone add-on that any tier can purchase. This works particularly well when you're unsure about demand or still figuring out who values this most.
And here's the safety net Marcos recommends: "If you're not sure, if you're like, 'Well, Marcos, it kind of is all over,' like, that probably means you're not clear with what you're trying to do, but the safest path would be add-on first and then decide if you want to fold it in or keep it separate based on the usage patterns, who's buying it, and what they're doing."
Marcos points to a clear pattern among major players: "Gemini did it, Slack did it, Notion did it, they had the add-on for a while. I think it was like 10 bucks a user, and then they ended up folding it in after that."
The strategy here is straightforward: launch as an add-on, watch what happens, then decide whether to integrate it more deeply into your pricing structure.
These companies didn't immediately try to build the perfect tiered structure. They didn't agonize over whether AI belonged in Professional vs. Enterprise. They launched it as an add-on at around $10 per user, collected data on adoption and usage, and made informed decisions later about integration.
That's the pattern worth following.
The add-on testing strategy: Collecting the right data
If you go the add-on route and Marcos strongly suggests you should when uncertain—you need to know what you're measuring.
Who's buying? Is it your largest customers? Your most sophisticated users? Specific industries or use cases? If only Enterprise customers are buying, that's a signal it belongs in your top tier. If it's spread across all segments, maybe it should be in the core product with
Framework #1.
Usage patterns matter Are customers who buy the add-on using it daily? Weekly? Or did they buy it and barely touch it? High engagement says keep it separate and premium. Low engagement says you might have a product-market fit problem, not a pricing problem.
Willingness to pay indicators How many customers ask about it during sales calls? How often do existing customers inquire about adding it? Are they price-sensitive, or do they buy without hesitation? These signals tell you how much value the market perceives.
The timeline question Marcos suggests giving it 2-4 quarters to see real patterns. One quarter isn't enough you'll catch early adopters but miss the mainstream. A full year might be too long if you're in a fast-moving market.
The sweet spot? Two quarters minimum. Four quarters if you want high confidence in your data.
Sometimes the data tells you something unexpected: different customer segments value the same AI feature for completely different reasons.
A project management tool might find that small teams use AI for automated task assignment (they lack dedicated project managers), while large enterprises use it for cross-project resource optimization (they have too many projects to manually coordinate).
Same AI. Different jobs-to-be-done. Different willingness to pay.
When you see this, you might need hybrid approaches: include basic AI functionality in the core, but gate advanced capabilities as premium features or add-ons. Framework #1 plus
Framework #2.
The companies that struggle are the ones that force a single strategy across diverse customer needs. The ones that win are comfortable with nuance.
The Margin Consideration Redux
Remember Marcos's warning about margin variability? It applies here too, but differently.
With Upgrade the More, you're often dealing with more compute-intensive AI. Advanced forecasting models. Complex automation chains. Deep analytical processing. These features genuinely cost more to deliver.
If your margin on these features is tight, that's actually an argument for keeping them separate. Charge appropriately for the value and the cost. Don't subsidize power users by spreading costs across your entire customer base.
But if margins are healthy and stable even on advanced features, and adoption is broad, that's a signal to fold them into tiered pricing rather than maintaining them as perpetual add-ons.
The key question: Does separating this feature give you pricing flexibility you need, or is it just creating complexity that frustrates customers?
Only your data can answer that.
Framework #3: Unlock the New
Sometimes AI doesn't just enhance or extend your product. Sometimes it breaks open entirely new possibilities—new users, new use cases, new markets you couldn't touch before.
That's when you need the third framework.
Unlock the New: When AI Changes the Game
Marcos describes this as the most transformative approach: "Unlock the new—whole new use case, maybe even a different user, a different TAM, something different. You're able to isolate that, charge it as a standalone, and then you can even bundle it with your core and your platform as well."
This isn't about making your product better or taking it further. This is about doing something fundamentally different. Different enough that it could stand alone. Different enough that it might attract customers who would never buy your core product.
The clearest signal you're in Unlock the New territory: you could spin this into a separate company, and it would make sense.
When This Framework Applies
You're looking at Framework #3 when:
Your AI enables completely new use cases that weren't possible before
You're targeting a different user persona or buying center
The capability could attract a different TAM or market segment entirely
This AI could displace another product category
The technology creates genuine competitive differentiation, not just incremental improvement
This is the realm of agents doing work autonomously. Of AI that doesn't assist humans but handles entire workflows independently. Of capabilities that make customers rethink their entire process, not just optimize it.
The Pricing Model: Standalone with Bundle Options
Marcos is clear on the approach here: price it as a separate product, but create strategic bundle opportunities with your core platform.
Why standalone? Because the value proposition is distinct enough that it deserves its own positioning, its own pricing logic, its own go-to-market motion. Forcing it into your existing pricing structure would confuse customers and likely leave money on the table.
Why bundle options? Because if you already have customer relationships and a platform they trust, offering this new capability as part of a broader solution creates competitive moats. It's harder for a startup to displace you when customers are getting multiple products that work together seamlessly.
"It also gives you some competitive advantages to be able to use it to penetrate new markets," Marcos notes. This is your wedge into territories where your core product never gained traction.
Real-World Examples: The Agent Revolution
This is where the agent companies are playing, and they're writing the playbook in real-time.
Marcos calls out several: "Zendesk and Intercom do it, ChargeFlo does it, DecaGon, all these different companies that have some agentic solution."
What unites them? They're not positioning AI as a feature enhancement. They're positioning autonomous systems that handle outcomes.
The pattern emerging: These companies are using a fixed + variable pricing structure. The fixed component covers configuration, training, onboarding—the setup work. The variable component is tied to a unit of work the agent actually completes.
Marcos breaks down the specifics: "Maybe that unit of work is transactions like authorizations approved like I've seen in health tech or legal discovery packages summarized like in legal tech."
Notice what's happening here: the pricing isn't about usage of the AI. It's about outcomes the AI delivers.
The board meeting always follows the same script.
"Where's our AI strategy?" someone asks. "Competitor X just launched AI features. We need something by Q2."
And just like that, teams scramble. Engineers explore LLMs, Product managers draft roadmaps and Marketing prepares launches.
But there's one conversation nobody's having, “How do we price this thing?”
Marcos Rivera has seen this movie play out hundreds of times. As the former head of pricing at Vista Equity Partners and founder of Pricing I/O, he's priced over 500 B2B SaaS products. And in 2025, he's watching a painful pattern repeat.
"Nobody agrees on what they need to do, where it needs to go in the packaging, what the goal is," Marcos says. "They just got to do AI something. The board wants to see AI."
That vague urgency is creating a pricing nightmare. Companies are copying competitor models that don't fit their use case. They're confusing customers with meaningless metrics like "actions." They're bleeding margins because one AI agent might operate at 60% margins while another runs at 10%.
TL;DR
Most B2B SaaS companies are botching AI pricing because they're copying competitors without understanding their own strategy. After pricing 500+ products, Marcos Rivera identifies three frameworks that actually work:
Framework #1: Underscore the Core - If AI improves your existing features. Fold it into your product and raise prices 10%+ because you're delivering faster time-to-value.
Framework #2: Upgrade the More - AI extends your product into premium territory. Launch as an add-on first, collect 2-4 quarters of data, then decide whether to fold in or keep separate.
Framework #3: Unlock the New - AI creates entirely new capabilities. Price it standalone with fixed + variable structures tied to outcomes (like transactions processed or claims approved). Define the "anti-outcome" to avoid billing disputes.
Key mistakes to avoid: Vague metrics like "credits" or "actions" that customers can't understand. Jumping from pure subscription to pure usage-based pricing without hybrid transitions. Adding pricing complexity on top of product complexity when users are already climbing the AI learning curve.
The safe default when uncertain: Launch as an add-on, watch who buys it and how they use it, then decide based on real data rather than assumptions.
AI is moving too fast for two-year pricing cycles. Review quarterly or bi-quarterly. Strategy clarity comes first you can't price what you can't define.
The old SaaS pricing playbook doesn't work anymore.
But after analyzing hundreds of companies, Marcos has identified three frameworks that actually work for AI pricing. Not theory, what actually drives revenue while protecting margins.
Let's break down why most companies are getting this wrong, and how to fix it.
1. The Current State of AI Pricing Chaos
Walk into any B2B SaaS company right now and ask the product team what their AI strategy is. Then ask sales. Then ask the pricing lead.
You'll get three completely different answers.
This isn't just organizational misalignment, it's creating real financial damage. And Marcos has a front-row seat to the carnage.
2. The "Something with AI" Problem
The pressure is coming from everywhere. Investors want to see AI in the roadmap. Competitors are announcing AI features. Customers are asking about it. So companies rush to ship something, just anything with artificial intelligence baked in.
But what is this AI actually supposed to do?
Marcos is blunt about what he's seeing: Companies can't answer basic questions.
Is this AI enhancing your core product?
Is it extending into new use cases?
Is it targeting a completely different customer?
Without clarity on strategy, pricing becomes impossible. You can't price what you can't define.
3. Three Expensive Mistakes
The chaos is manifesting in predictable patterns:
a. Mistake #1: The Copycat Trap
Someone sees a competitor launch "credits" or charge "per action," and suddenly that's the plan.
Never mind that credits mean nothing to customers.
Never mind that "action" could mean literally anything.
It looks like modern pricing, so it gets copied wholesale.
Marcos recently saw a company introduce "per action" pricing. When he asked what an action was, the answer was: "Well, it could mean this and it could mean that."
That's not a pricing model. That's just confusion with a price tag.
b. Mistake #2: Complexity Stacked on Complexity
Your customers are already trying to figure out how to use AI. They're learning new workflows, new interfaces, new ways of thinking about their work.
And now you're adding pricing models they need a decoder ring to understand?
c. Mistake #3: The Margin Blindspot
Not all AI features are created equal from a cost perspective.
Traditional SaaS could get away with stable, high margins. Build the feature once, serve it to thousands of customers, margins stay intact.
But with AI agents, one might cost you 10% of revenue to operate. Another might cost 40%. And if you're pricing them the same way, you're bleeding money on every transaction.
Why this matters more than ever
Sam Altman and other LLM CEOs are predicting 2025-26 as the "year of AI agents." Marcos thinks they're being optimistic, 2027-28 is more realistic. Humans need time to change processes, rethink workflows, actually adopt these tools.
And that timeline gives you breathing room to get pricing right before the agent revolution fully hits. Companies that figure this out now will dominate when agents become table stakes.
The ones still fumbling with vague "credit" systems and copied pricing models will be stuck explaining to their board why margins collapsed and customers churned.
And there's a better way. And it starts with understanding which of three frameworks actually fits what you're building.
Framework #1: Underscore the Core
Let's start with the simplest and often most overlooked approach to AI pricing.
Underscore the Core
Marcos breaks it down simply: "Underscore the core’ means that your strategy is to enhance what you already do, your search, workflows, decision-making, and core functionality.
If AI makes those things better, faster, or smarter, then you should fold it into the product itself, and reflect that improvement in your pricing.”
This is the unglamorous truth about most AI implementations. You're not building Jarvis from Iron Man. You're making your existing search faster. Your workflows are smoother. Your analytics sharper.
The fundamental job-to-be-done hasn't changed, you're just helping customers complete it with less friction and more speed.
And here's what makes this powerful: some of this AI is visible to users, some runs invisibly in the background.
A customer might see AI-suggested actions in their dashboard, but not see the AI pre-processing data, optimizing queries, or predicting their next need. Both types deliver value. Both justify higher pricing.
When this framework actually fits
The mistake companies make is thinking every AI feature needs its own SKU, its own add-on, its own pricing tier. Sometimes the smartest move is integration, not separation.
You're in Underscore territory when:
Your AI gets users to value faster without changing what that value is
The enhancement feels inevitable, like "of course this should work this way"
Users would struggle to separate "the AI part" from "the product part"
The cost structure remains relatively stable and predictable
Think about spell-check in Microsoft Word. Nobody pays extra for it. Nobody thinks of it as a separate feature. It's just... how Word works now. That's what Underscore the Core AI should feel like.
Fold it In, then raise 10%+
Here's where Marcos pushes back against the 3-5% increases most companies consider.
"I’m not talking about small increases like 5%. I mean taking prices up by 10% or more especially when users can clearly see the impact of the AI, and even when some of that impact is invisible.
Either way, the AI is getting users to value faster. It’s helping them accomplish their jobs and use cases more quickly, more effectively, or more deeply."
Why does 10%+ work when 5% feels aggressive?
Because you're not just adding a feature. You're compressing time. You're eliminating frustration. You're making your product do what customers always wished it would do.
Consider the math from a customer's perspective: If your AI cuts the time to complete a workflow from 30 minutes to 10 minutes, you've just given them back 20 minutes per workflow.
If they run that workflow 50 times a month, that's 16+ hours saved. What's that worth? Way more than 10%.
The trick is making sure customers feel that time compression. If the improvement is invisible or incremental, you'll face resistance. If it's obvious and immediate, the price increase becomes a no-brainer.
Getting this right
Don't lead with "We added AI." Nobody wakes up wanting AI. They wake up wanting their work to be easier.
Your announcement isn't "Introducing AI-powered search!" It's "Find what you need 70% faster with intelligent search that understands context."
The AI is the how. The time saved is the what. The outcome is the why.
Timing the increase: Marcos is clear on this: "Fold it in and raise your prices right? I'm not talking 5% raises."
But when matters as much as how much. Roll the price increase when you launch the AI features, not after. The value jump and the price jump need to happen simultaneously.
Because customers understand paying more for getting more when it's obvious they're getting more. Launch AI in January, raise prices in June, and you're asking them to remember what changed six months ago. Launch both together, and the connection is immediate.
The grandfathering decision: Every company struggles with this. Do existing customers get the new AI at their old price? Or does everyone move to new pricing?
There's no universal answer, but here's Marcos's framework: if the AI delivers meaningfully more value and we're only talking about Underscore the Core, if it does then existing customers are getting that value too.
You can soften the blow with timing (6-month notice, grandfather until next renewal) or with transitional pricing (halfway between old and new for the first year).
But ultimately, if you've truly enhanced your core product, your existing customers are benefiting. They should expect to pay for that benefit.
The companies that get in trouble are the ones who under-communicate. They assume customers will notice the improvements and understand the value. Wrong.
You need to actively message what changed, why it matters, and how it makes their work better.
The margin math that makes or break this
Here's where the rubber meets the road. Underscore the Core only works if your AI costs are relatively stable and predictable.
Marcos is watching companies miss this crucial detail. They fold AI into base pricing, then realize customer usage varies wildly.
One customer's AI enhancement costs them $2/month. Another costs $47/month. Same price point, completely different margins.
"The margins are high and stable, and as you keep building and extending the platform, bolting on modules, adding integrations, the margins kind of stay intact. But not in AI, not with agents.
You can have one agent with a 60% margin and another one with a 10% margin. It just depends on the cost, how much tokens it's consuming and how much users are using it."
This is why Underscore the Core works best for AI that enhances existing features in relatively uniform ways.
If your AI is optimizing database queries in the background, or improving search relevance, or suggesting next actions and these costs don't spike dramatically based on user behavior, flat pricing makes sense.
But if your AI costs are driven by consumption, if heavy users genuinely cost you 10x what light users cost you need to look at Framework #2 or #3.
Trying to force Underscore the Core pricing onto variable-cost AI is how you accidentally build a company that loses money as customers adopt your product.
Which is, you know, the opposite of the goal.
Framework #2: Upgrade the More
Marcos describes this as taking your product "a few steps further" with AI. Your product used to end here. But now, with AI, you can go several steps beyond that endpoint.
"Some companies where it's 'I think we're just taking the things a bit further. We could—our products ended here, but now we can go a few steps further with AI.' And this gets into more complex territory. You want to upgrade the more. This is more jobs, more use cases or more extension of the product."
This isn't about making existing features better. It's about unlocking capabilities that were previously impossible or impractical. You're expanding the scope of what your product can do.
The key distinction from Underscore the Core: not every customer needs this. In fact, many won't. And that's perfectly fine, it's what makes this a premium or add-on play.
When does this framework make sense
You're in Upgrade the More territory when:
Your AI enables new use cases that extend beyond your original value proposition
The capabilities feel "advanced" or "power user" rather than fundamental
Different customer segments have dramatically different appetite for these features
The enhancement is substantial enough that users can clearly distinguish it from the base product
Think of it this way: if someone could use your product effectively without this AI feature, but power users or larger customers would jump at the chance to have it, you're looking at Upgrade the More.
The pricing model: Premium tiers or add-ons
Marcos offers two paths here, and they're not mutually exclusive.
Path 1: Premium Plan Placement Put these AI capabilities in your higher-tier plans. Your Enterprise plan gets AI-powered forecasting. Your Professional plan gets advanced automation. Your Basic plan works just fine without them.
This creates natural segmentation and gives customers a reason to upgrade beyond just "more seats" or "higher usage limits."
Path 2: The Add-On Strategy Offer the AI enhancement as a standalone add-on that any tier can purchase. This works particularly well when you're unsure about demand or still figuring out who values this most.
And here's the safety net Marcos recommends: "If you're not sure, if you're like, 'Well, Marcos, it kind of is all over,' like, that probably means you're not clear with what you're trying to do, but the safest path would be add-on first and then decide if you want to fold it in or keep it separate based on the usage patterns, who's buying it, and what they're doing."
Marcos points to a clear pattern among major players: "Gemini did it, Slack did it, Notion did it, they had the add-on for a while. I think it was like 10 bucks a user, and then they ended up folding it in after that."
The strategy here is straightforward: launch as an add-on, watch what happens, then decide whether to integrate it more deeply into your pricing structure.
These companies didn't immediately try to build the perfect tiered structure. They didn't agonize over whether AI belonged in Professional vs. Enterprise. They launched it as an add-on at around $10 per user, collected data on adoption and usage, and made informed decisions later about integration.
That's the pattern worth following.
The add-on testing strategy: Collecting the right data
If you go the add-on route and Marcos strongly suggests you should when uncertain—you need to know what you're measuring.
Who's buying? Is it your largest customers? Your most sophisticated users? Specific industries or use cases? If only Enterprise customers are buying, that's a signal it belongs in your top tier. If it's spread across all segments, maybe it should be in the core product with
Framework #1.
Usage patterns matter Are customers who buy the add-on using it daily? Weekly? Or did they buy it and barely touch it? High engagement says keep it separate and premium. Low engagement says you might have a product-market fit problem, not a pricing problem.
Willingness to pay indicators How many customers ask about it during sales calls? How often do existing customers inquire about adding it? Are they price-sensitive, or do they buy without hesitation? These signals tell you how much value the market perceives.
The timeline question Marcos suggests giving it 2-4 quarters to see real patterns. One quarter isn't enough you'll catch early adopters but miss the mainstream. A full year might be too long if you're in a fast-moving market.
The sweet spot? Two quarters minimum. Four quarters if you want high confidence in your data.
Sometimes the data tells you something unexpected: different customer segments value the same AI feature for completely different reasons.
A project management tool might find that small teams use AI for automated task assignment (they lack dedicated project managers), while large enterprises use it for cross-project resource optimization (they have too many projects to manually coordinate).
Same AI. Different jobs-to-be-done. Different willingness to pay.
When you see this, you might need hybrid approaches: include basic AI functionality in the core, but gate advanced capabilities as premium features or add-ons. Framework #1 plus
Framework #2.
The companies that struggle are the ones that force a single strategy across diverse customer needs. The ones that win are comfortable with nuance.
The Margin Consideration Redux
Remember Marcos's warning about margin variability? It applies here too, but differently.
With Upgrade the More, you're often dealing with more compute-intensive AI. Advanced forecasting models. Complex automation chains. Deep analytical processing. These features genuinely cost more to deliver.
If your margin on these features is tight, that's actually an argument for keeping them separate. Charge appropriately for the value and the cost. Don't subsidize power users by spreading costs across your entire customer base.
But if margins are healthy and stable even on advanced features, and adoption is broad, that's a signal to fold them into tiered pricing rather than maintaining them as perpetual add-ons.
The key question: Does separating this feature give you pricing flexibility you need, or is it just creating complexity that frustrates customers?
Only your data can answer that.
Framework #3: Unlock the New
Sometimes AI doesn't just enhance or extend your product. Sometimes it breaks open entirely new possibilities—new users, new use cases, new markets you couldn't touch before.
That's when you need the third framework.
Unlock the New: When AI Changes the Game
Marcos describes this as the most transformative approach: "Unlock the new—whole new use case, maybe even a different user, a different TAM, something different. You're able to isolate that, charge it as a standalone, and then you can even bundle it with your core and your platform as well."
This isn't about making your product better or taking it further. This is about doing something fundamentally different. Different enough that it could stand alone. Different enough that it might attract customers who would never buy your core product.
The clearest signal you're in Unlock the New territory: you could spin this into a separate company, and it would make sense.
When This Framework Applies
You're looking at Framework #3 when:
Your AI enables completely new use cases that weren't possible before
You're targeting a different user persona or buying center
The capability could attract a different TAM or market segment entirely
This AI could displace another product category
The technology creates genuine competitive differentiation, not just incremental improvement
This is the realm of agents doing work autonomously. Of AI that doesn't assist humans but handles entire workflows independently. Of capabilities that make customers rethink their entire process, not just optimize it.
The Pricing Model: Standalone with Bundle Options
Marcos is clear on the approach here: price it as a separate product, but create strategic bundle opportunities with your core platform.
Why standalone? Because the value proposition is distinct enough that it deserves its own positioning, its own pricing logic, its own go-to-market motion. Forcing it into your existing pricing structure would confuse customers and likely leave money on the table.
Why bundle options? Because if you already have customer relationships and a platform they trust, offering this new capability as part of a broader solution creates competitive moats. It's harder for a startup to displace you when customers are getting multiple products that work together seamlessly.
"It also gives you some competitive advantages to be able to use it to penetrate new markets," Marcos notes. This is your wedge into territories where your core product never gained traction.
Real-World Examples: The Agent Revolution
This is where the agent companies are playing, and they're writing the playbook in real-time.
Marcos calls out several: "Zendesk and Intercom do it, ChargeFlo does it, DecaGon, all these different companies that have some agentic solution."
What unites them? They're not positioning AI as a feature enhancement. They're positioning autonomous systems that handle outcomes.
The pattern emerging: These companies are using a fixed + variable pricing structure. The fixed component covers configuration, training, onboarding—the setup work. The variable component is tied to a unit of work the agent actually completes.
Marcos breaks down the specifics: "Maybe that unit of work is transactions like authorizations approved like I've seen in health tech or legal discovery packages summarized like in legal tech."
Notice what's happening here: the pricing isn't about usage of the AI. It's about outcomes the AI delivers.
The Delegation Shift: Why Outcome Pricing Works
Here's the fundamental change that makes Framework #3 different: "The delegation is going from the human to the system now, right? And so now that you're delegating to the system, the system can now capture a little more percentage of that value as long as it's super clear that the system is responsible for that value."
This is profound. When a human uses AI as a tool, they're still responsible for the outcome. The human runs the search, reviews the results, makes the decision. AI assists.
But when an agent handles the entire workflow—when it processes the insurance claim, summarizes the legal discovery, approves the authorization—the system is now accountable for the outcome. And that shifts pricing dynamics entirely.
You're no longer charging for access or usage. You're charging for work completed. For problems solved. For outcomes delivered.
The Fixed + Variable Structure
The model Marcos sees working across agentic solutions breaks down into two clear components:
The fixed component covers everything that happens before the agent starts doing work:
Configuration and setup
Training the system on your specific workflows
Onboarding and integration
Basic support and maintenance
This is predictable revenue. It covers your costs for getting the customer live. It creates a baseline you can count on.
The variable component is where the magic—and the margin—happens: It's counting units of work. But here's where companies get tricky: defining what counts and what doesn't.
Defining the Anti-Outcome
Marcos introduces a critical concept that most companies miss: "They're very specific. It's very clear and it's sort of the anti-outcome is what they need to define too. What do I mean? I mean what does not count as an outcome so that way we don't get into arguments about charging."
This is brilliant. It's not enough to say "we charge per claim processed." You need to define:
Does a rejected claim count?
Does a claim that requires human review count?
Does a duplicate submission count?
Does a test transaction count?
The anti-outcome prevents disputes. It creates clarity. It stops customers from feeling nickel-and-dimed when edge cases come up.
Health tech example: If you're charging per authorization approved, you need to specify that failed authorizations, duplicates, and test cases don't count. Only completed, successful authorizations that deliver actual value.
Legal tech example: If you're charging per discovery package summarized, define minimum document counts, exclude corrupted files, clarify what happens when documents are in unsupported formats.
The more specific you are about what doesn't count, the less friction you'll face at billing time.
Milestone-Based Alternatives for Risk-Averse Customers
Not every customer is comfortable with pure variable pricing, even when it's tied to outcomes. Some industries, some company cultures, just prefer predictability.
Marcos sees this adaptation emerging: "For some companies or industries where they're really kind of skittish about variable, they love more predictability, they love other things... maybe it's more milestone driven. Hey when we hit this threshold we get a payment or a fee, maybe we hit that threshold we get a payment and fee."
This is the best of both worlds for cautious buyers:
They get predictability through milestone payments
You get increasing revenue as the agent proves value
Both sides are incentivized for deeper adoption
Example structure:
Milestone 1: Agent processes first 100 units → Payment
Milestone 2: Agent reaches 500 units → Payment
Milestone 3: Agent hits 1,000 units → Payment
Beyond that: Per-unit pricing kicks in
"It allows them to get deeper and deeper with the agent over time, which is exactly what you want," Marcos explains.
The customer isn't betting big upfront. They're paying as value gets proven. And as they see results, the variable component becomes less scary and more exciting.
The Market Penetration Strategy
Here's where Unlock the New becomes genuinely strategic, not just tactical pricing.
If your AI agent can do something your core product couldn't, you can now enter markets where you previously had no shot. Your core platform was too expensive, too complex, or solving the wrong problem. But this agent? It solves a specific pain point those markets actually have.
Price it accordingly. Position it independently. Build a customer base you couldn't access before.
Then and this is where bundling comes in—once they're using your agent successfully, introduce them to your platform. "Hey, you love what the agent does. Imagine if it integrated with our full platform for end-to-end workflow management."
You've just turned an agent customer into a platform customer. That's the penetration strategy.
When Bundling Creates Competitive Moats
Marcos points out the defensive advantage: "You can even bundle it with your core and your platform as well. It also gives you some competitive advantages."
Here's why that matters:
A startup launching a standalone agent can compete on features and price. They're nimble, focused, probably cheaper.
But if you're offering the agent plus the platform plus integrations that make everything work together seamlessly—at a bundled price that's attractive—suddenly that startup has to compete against an ecosystem, not just a product.
Customers choose integrated solutions over best-of-breed point solutions when the integration tax (time, money, complexity) is high enough. Your bundle makes the integration tax zero.
That's a moat.
The Margin Reality Check
Remember Marcos's warning about margin variability? It's most pronounced here.
Agents are expensive to run. Token consumption varies wildly based on task complexity. One customer's workflows might cost you 10% of revenue. Another's might cost 40%.
This is exactly why outcome-based or unit-based pricing exists. You're tying revenue to actual work performed, which naturally correlates with costs incurred.
But you need to do the math. If you're charging $5 per unit of work, and some units cost you $6 to deliver, you're subsidizing adoption. That's fine as a short-term market penetration strategy. It's disastrous as a long-term business model.
The companies winning with Framework #3 are obsessively tracking unit economics. They know exactly what each outcome costs to deliver. They price with margin built in. And they're constantly optimizing their agents to reduce token consumption and infrastructure costs.
Because in this framework, your profit scales with your efficiency, not just with your growth.
The Hybrid vs. Usage-Based Debate
There's a seductive logic to usage-based pricing for AI: customers pay for what they use, and you cover your variable costs. Clean. Fair. Simple.
Except it's not simple at all.
Why Usage-Based Pricing is Trending (And Why It's Tricky)
Marcos sees the appeal clearly: "There's a lot of movement towards usage-based pricing, right? And the reason why is to really cover a lot of that LLM costs and making sure you don't lose your shirt on your margin as your customers adopt your product."
The math makes sense. AI costs vary wildly by usage. Pure subscription pricing means heavy users subsidized by light users, or worse—you lose money on your most engaged customers.
But here's the problem Marcos sees everywhere: "Not everyone is ready for usage-based pricing. Sounds good, right? When you and I say it, right? It sounds good. But there's infrastructure concerns. There's process concerns, there's tracking concerns, there's sales compensation concerns, there's all these different things that you have to set the stage for before you roll into usage-based pricing."
Your billing system needs to handle variable charges. Your sales team needs new compensation models (do they get commissions on usage that happens six months after the sale?). Your customers need to track and predict costs. Your finance team needs new forecasting models.
It's not just a pricing change. It's an operational transformation.
Marcos's Warning: Don't Leap
"My advice is not to just leap in from—say you're charging by seats today—leap from pure seats to pure consumption or pure usage. I think that is a very dangerous way to go about it."
Why dangerous? Because you're asking customers to completely change how they think about your product while simultaneously changing how they budget for it. That's two massive shifts at once.
Customers who understood "$50 per user per month" now have to predict consumption patterns they've never tracked. Finance teams who budgeted fixed costs now face variable expenses. Procurement departments who negotiated annual contracts now need consumption forecasting models.
You're creating friction exactly when you want adoption.
The Hybrid Model: Training Wheels for Consumption
This is where Marcos sees the most success: combining fixed subscription with variable usage components.
"It is a very popular model to go in and try to blend subscription or flat with some usage. And I think that's good because that sort of starts to train the customer and condition them that there's usage that you need to track and it matters. Cuz beforehand they didn't have to pay attention to that stuff."
The psychology here is crucial. You're not throwing customers into the deep end. You're easing them into a world where usage matters.
How this typically works:
Start with a base subscription that includes an allocation or entitlement of usage. Think: $500/month includes 10,000 credits. Or: $2,000/month includes 100 hours of agent work.
Customers get predictability for budgeting. You get a revenue floor. And everyone gets educated about what "usage" actually means in your context.
Then, when customers exceed their allocation: "If you consume your allotment or if you consume your entitlements, there will be—we'll have to bump you up to the next tier or whatever that is."
Notice what's happening: customers are learning to track usage, but within guardrails. They're not facing unlimited variable costs. They're facing a predictable tier system with overages.
Marcos gives a real example: "I've used tools like clay.com where they use seat-based pricing but also use credit-based pricing."
That's the model. Seats for access. Credits for consumption. Predictability plus flexibility.
The Infrastructure Reality
Before you implement any usage-based component, Marcos's checklist of concerns applies:
Tracking: Can you accurately measure and attribute usage? Do you have the instrumentation to know who used what, when?
Billing: Can your systems handle variable charges? Monthly invoices that change based on consumption? Proration? Overages?
Sales compensation: How do reps get paid on consumption that happens months after the deal closes? Do they get ongoing commissions? Residuals?
Customer communication: Can you show customers their usage in real-time? Give them alerts before they hit overages? Provide transparency into what's driving costs?
If you can't answer "yes" to all of these, you're not ready for usage-based pricing. Build the foundation first.
When Pure Usage Actually Works
Marcos isn't saying usage-based pricing is wrong. He's saying the leap from subscription to pure usage is dangerous.
But there are scenarios where usage-based makes complete sense from day one:
You're launching a new product with no legacy pricing to migrate from
Your customers already use usage-based pricing in adjacent tools
The value metric is obvious and customers already track it
Your infrastructure is built for it from the ground up
Starting with usage-based is different from switching to it. If you're starting fresh, you avoid the transition pain.
The Decision Framework
o you've got three frameworks. Your AI features are ready to launch. Sales is asking for pricing. The board wants a strategy.
How do you actually decide?
The Clarity Test
Marcos makes this uncomfortably simple: if you can't clearly articulate which framework fits, your AI strategy isn't clear enough.
"If you're not sure, if you're like, 'Well, Marcos, it kind of is all over,' like, that probably means you're not clear with what you're trying to do."
Before you price anything, answer these questions:
What is the AI actually doing? Not what it could do. Not the vision. What does it do today that customers will pay for?
Is it enhancing, extending, or unlocking? Does it make existing features better (Framework #1)? Take your product further into new territory (Framework #2)? Or create entirely new capabilities (Framework #3)?
Who is the user? Same persona using your product better? Power users wanting advanced features? Or completely different buyers?
What's the cost structure? Stable and predictable? Variable but manageable? Or wildly different based on usage patterns?
If you can't answer these crisply, stop. Fix your strategy first. Pricing can't solve for strategic confusion.
The Safe Default: Add-On First
When Marcos encounters uncertainty, he has a fallback position: "The safest path would be add-on first and then decide if you want to fold it in or keep it separate based on the usage patterns, who's buying it, and what they're doing."
Launch as an add-on. Give it 2-4 quarters. Watch what actually happens, not what you hoped would happen.
Then decide based on real data:
Who's buying it? If it's everyone → consider Framework #1 (fold into core)
How are they using it? If it's power users dominating → Framework #2 (premium tier)
What outcomes are they getting? If it's creating new value pools → Framework #3 (standalone)
The add-on is your reconnaissance mission. You're gathering intelligence before committing to a pricing structure that's painful to change.
Red Flags to Avoid
Marcos has seen enough pricing disasters to know the warning signs:
Vague metrics: If you're charging for "credits" or "actions" that customers can't define, stop. "I saw someone the other day use per action. We're going to charge you per action. I'm like, what the hell does that even mean?"
Copycat pricing: Just because a competitor uses a model doesn't mean it fits your cost structure, customer base, or value proposition. "Resist the temptation to copy. Think about your own value, what you need to do, your specific customers, where you are in your journey, and price for that."
Complexity on complexity: Your customers are already learning how to use AI. Don't add pricing models that require a PhD to understand. "People are already going up the learning curve with adopting AI. Don't put confusing pricing on top of that."
If any of these red flags are waving, pause. Simplify. Get clear.
The One-Quarter Rule
Whatever you choose, give it time to breathe. Marcos used to advocate for the "two by two" rule two quarters to let pricing play out, revisit if you haven't changed in two years.
But AI is different: "Today with AI and agents and the pace in which this is moving, I actually think you should be changing your pricing every two quarters, some even quarterly depending on where your stage is."
The market is moving faster. Customer expectations are shifting. Your costs are evolving as models improve.
Quarterly or bi-quarterly reviews aren't premature optimization anymore. They're necessary adaptations.
Implementation
The Pricing Council Is Non-Negotiable
Marcos is adamant about this: pricing isn't a solo decision, but it needs a leader.
"Pricing is a team sport, but it does need a strong captain, right? And otherwise, nobody's going to do anything."
The structure that works: 5-8 people, cross-functional, meeting on a consistent cadence.
Who's in the room? "Sales representation, customer success, rev ops, of course you want product in there, marketing, all the key folks, finance."
But here's the critical detail most companies miss: it's not the C-suite, and it's not junior ICs. "If you're 30, 50, 80 million, often times it's not the C-suite and it's not like the line workers. It's usually like a director VP level in that meeting in the middle because they're close enough to the action and they also understand the strategy."
These are people who know what's actually happening in deals, in customer conversations, in the product but can also think strategically about where you're headed.
Running Meetings That Actually Matter
Most pricing committees fail because nobody's actually running them. People show up, talk about pricing opinions, nothing gets decided.
Marcos's prescription: "You have to plan out, I would say, a cadence and rhythm for the year. We're going to meet the last Friday of every month to talk about pricing. Here's the agenda. Here's what we're talking about. Here's who needs to be there."
And then the accountability check: "After your first three meetings, send out a short survey to all the attendees to say, was it valuable? The right level of detail, did you need to be there? And just make sure that meeting is super valuable."
If the meeting isn't valuable, kill it. Don't let it become another calendar obligation that everyone resents.
"The companies who get this meeting right, those are the ones that are really high performing. Those are the ones that are monetizing and breaking through."
The Roadmap Integration
Your pricing changes shouldn't be surprises. They need visibility alongside product launches.
"There should be a very visible swim lane for your pricing and packaging or monetization updates. You should be highlighting what you're doing to the pricing and packaging with each release and why."
This isn't bureaucracy. It's alignment. When engineering sees that pricing changes are tied to the Q3 agent launch, they understand the timeline matters. When sales sees that new AI features come with new pricing in Q2, they can prepare customers. When marketing sees the packaging shift planned for next quarter, they can build the campaign.
Pricing in the shadows creates chaos. Pricing in the roadmap creates coordination.
AI Strategy First, Pricing Second
Marcos sees this mistake constantly: teams trying to price AI before they've clarified what the AI is supposed to accomplish.
"A lot of murky AI strategy is going on and that needs to be solved first."
Are you enhancing core features? Expanding use cases for differentiation? Breaking into a new TAM? Displacing another product?
"Whatever you're doing, you have to be super explicit about it. It makes the pricing a lot easier and it makes everyone also behind it."
You can't price what you can't define. Strategy clarity isn't a nice-to-have. It's the prerequisite.
Getting It Right Before Your Competitors Do
The companies that will dominate the next three years aren't the ones with the most sophisticated AI. They're the ones who figured out how to capture the value their AI creates.
You have the three frameworks: Underscore the Core when AI enhances what you do. Upgrade the More when it extends into premium territory. Unlock the New when it creates entirely new capabilities.
The framework matters less than having clarity about which one fits.
Marcos's final advice cuts through the noise: "Resist the temptation to copy. Think about your own value, what you need to do, your specific customers, where you are in your journey, and price for that."
Stop copying competitors. Get your AI strategy straight first. Default to add-ons when uncertain. Avoid vague metrics. And move faster—quarterly reviews aren't premature anymore, they're necessary.
The timeline gives you room to get this right now, before AI pricing becomes table stakes.
FAQ
How should I price AI features in my SaaS product?
Use one of three frameworks based on what your AI does: (1) Underscore the Core - fold AI enhancements into your base product and raise prices 10%+, (2) Upgrade the More - offer AI as a premium tier or add-on for extended capabilities, or (3) Unlock the New - price AI as a standalone product when it creates entirely new use cases or targets different users.
Should I use usage-based pricing for AI features?
Not immediately. Marcos Rivera warns against jumping from subscription to pure usage-based pricing: "That is a very dangerous way to go about it." Start with hybrid models that combine fixed subscription with variable usage components. This trains customers to track consumption while maintaining pricing predictability. Pure usage-based pricing requires infrastructure for tracking, billing systems that handle variability, and new sales compensation models.
What is hybrid pricing for AI?
Hybrid pricing combines a fixed subscription fee with variable usage charges. For example, $500/month includes 10,000 AI credits, with overages moving customers to the next tier. This model "starts to train the customer and condition them that there's usage that you need to track and it matters," while providing budgeting predictability. Companies like Clay.com use seat-based pricing combined with credit-based consumption.
How do you price AI agents?
Price AI agents using a fixed + variable structure. The fixed component covers configuration, training, and onboarding. The variable component ties to units of work completed—like transactions processed, claims approved, or legal documents summarized. Critical: define the "anti-outcome" (what doesn't count) to avoid billing disputes. For risk-averse customers, use milestone-based pricing where payments trigger at specific usage thresholds.
What are AI pricing credits and should I use them?
AI credits are usage units that customers spend to access AI features. However, Marcos cautions against vague credit systems: credits must have clear definitions that customers understand. Avoid pricing "per action" or "per credit" without explaining exactly what constitutes an action or what credits purchase. Confusing metrics add friction when customers are already learning how to use AI.
How often should I change my AI pricing?
Review AI pricing every quarter or every two quarters—much faster than traditional SaaS. Marcos used to recommend the "two by two rule" (two quarters to assess, revisit if unchanged in two years), but "with AI and agents and the pace in which this is moving, I actually think you should be changing your pricing every two quarters, some even quarterly." The market is evolving too quickly for annual pricing reviews.
What is outcome-based pricing for AI?
Outcome-based pricing charges for results delivered rather than usage consumed. It works when "the delegation is going from the human to the system"—when AI agents handle complete workflows autonomously. Examples: charging per insurance claim processed, per legal discovery package summarized, or per authorization approved. This model captures value created by the system and aligns pricing with customer ROI.
The Delegation Shift: Why Outcome Pricing Works
Here's the fundamental change that makes Framework #3 different: "The delegation is going from the human to the system now, right? And so now that you're delegating to the system, the system can now capture a little more percentage of that value as long as it's super clear that the system is responsible for that value."
This is profound. When a human uses AI as a tool, they're still responsible for the outcome. The human runs the search, reviews the results, makes the decision. AI assists.
But when an agent handles the entire workflow—when it processes the insurance claim, summarizes the legal discovery, approves the authorization—the system is now accountable for the outcome. And that shifts pricing dynamics entirely.
You're no longer charging for access or usage. You're charging for work completed. For problems solved. For outcomes delivered.
The Fixed + Variable Structure
The model Marcos sees working across agentic solutions breaks down into two clear components:
The fixed component covers everything that happens before the agent starts doing work:
Configuration and setup
Training the system on your specific workflows
Onboarding and integration
Basic support and maintenance
This is predictable revenue. It covers your costs for getting the customer live. It creates a baseline you can count on.
The variable component is where the magic—and the margin—happens: It's counting units of work. But here's where companies get tricky: defining what counts and what doesn't.
Defining the Anti-Outcome
Marcos introduces a critical concept that most companies miss: "They're very specific. It's very clear and it's sort of the anti-outcome is what they need to define too. What do I mean? I mean what does not count as an outcome so that way we don't get into arguments about charging."
This is brilliant. It's not enough to say "we charge per claim processed." You need to define:
Does a rejected claim count?
Does a claim that requires human review count?
Does a duplicate submission count?
Does a test transaction count?
The anti-outcome prevents disputes. It creates clarity. It stops customers from feeling nickel-and-dimed when edge cases come up.
Health tech example: If you're charging per authorization approved, you need to specify that failed authorizations, duplicates, and test cases don't count. Only completed, successful authorizations that deliver actual value.
Legal tech example: If you're charging per discovery package summarized, define minimum document counts, exclude corrupted files, clarify what happens when documents are in unsupported formats.
The more specific you are about what doesn't count, the less friction you'll face at billing time.
Milestone-Based Alternatives for Risk-Averse Customers
Not every customer is comfortable with pure variable pricing, even when it's tied to outcomes. Some industries, some company cultures, just prefer predictability.
Marcos sees this adaptation emerging: "For some companies or industries where they're really kind of skittish about variable, they love more predictability, they love other things... maybe it's more milestone driven. Hey when we hit this threshold we get a payment or a fee, maybe we hit that threshold we get a payment and fee."
This is the best of both worlds for cautious buyers:
They get predictability through milestone payments
You get increasing revenue as the agent proves value
Both sides are incentivized for deeper adoption
Example structure:
Milestone 1: Agent processes first 100 units → Payment
Milestone 2: Agent reaches 500 units → Payment
Milestone 3: Agent hits 1,000 units → Payment
Beyond that: Per-unit pricing kicks in
"It allows them to get deeper and deeper with the agent over time, which is exactly what you want," Marcos explains.
The customer isn't betting big upfront. They're paying as value gets proven. And as they see results, the variable component becomes less scary and more exciting.
The Market Penetration Strategy
Here's where Unlock the New becomes genuinely strategic, not just tactical pricing.
If your AI agent can do something your core product couldn't, you can now enter markets where you previously had no shot. Your core platform was too expensive, too complex, or solving the wrong problem. But this agent? It solves a specific pain point those markets actually have.
Price it accordingly. Position it independently. Build a customer base you couldn't access before.
Then and this is where bundling comes in—once they're using your agent successfully, introduce them to your platform. "Hey, you love what the agent does. Imagine if it integrated with our full platform for end-to-end workflow management."
You've just turned an agent customer into a platform customer. That's the penetration strategy.
When Bundling Creates Competitive Moats
Marcos points out the defensive advantage: "You can even bundle it with your core and your platform as well. It also gives you some competitive advantages."
Here's why that matters:
A startup launching a standalone agent can compete on features and price. They're nimble, focused, probably cheaper.
But if you're offering the agent plus the platform plus integrations that make everything work together seamlessly—at a bundled price that's attractive—suddenly that startup has to compete against an ecosystem, not just a product.
Customers choose integrated solutions over best-of-breed point solutions when the integration tax (time, money, complexity) is high enough. Your bundle makes the integration tax zero.
That's a moat.
The Margin Reality Check
Remember Marcos's warning about margin variability? It's most pronounced here.
Agents are expensive to run. Token consumption varies wildly based on task complexity. One customer's workflows might cost you 10% of revenue. Another's might cost 40%.
This is exactly why outcome-based or unit-based pricing exists. You're tying revenue to actual work performed, which naturally correlates with costs incurred.
But you need to do the math. If you're charging $5 per unit of work, and some units cost you $6 to deliver, you're subsidizing adoption. That's fine as a short-term market penetration strategy. It's disastrous as a long-term business model.
The companies winning with Framework #3 are obsessively tracking unit economics. They know exactly what each outcome costs to deliver. They price with margin built in. And they're constantly optimizing their agents to reduce token consumption and infrastructure costs.
Because in this framework, your profit scales with your efficiency, not just with your growth.
The Hybrid vs. Usage-Based Debate
There's a seductive logic to usage-based pricing for AI: customers pay for what they use, and you cover your variable costs. Clean. Fair. Simple.
Except it's not simple at all.
Why Usage-Based Pricing is Trending (And Why It's Tricky)
Marcos sees the appeal clearly: "There's a lot of movement towards usage-based pricing, right? And the reason why is to really cover a lot of that LLM costs and making sure you don't lose your shirt on your margin as your customers adopt your product."
The math makes sense. AI costs vary wildly by usage. Pure subscription pricing means heavy users subsidized by light users, or worse—you lose money on your most engaged customers.
But here's the problem Marcos sees everywhere: "Not everyone is ready for usage-based pricing. Sounds good, right? When you and I say it, right? It sounds good. But there's infrastructure concerns. There's process concerns, there's tracking concerns, there's sales compensation concerns, there's all these different things that you have to set the stage for before you roll into usage-based pricing."
Your billing system needs to handle variable charges. Your sales team needs new compensation models (do they get commissions on usage that happens six months after the sale?). Your customers need to track and predict costs. Your finance team needs new forecasting models.
It's not just a pricing change. It's an operational transformation.
Marcos's Warning: Don't Leap
"My advice is not to just leap in from—say you're charging by seats today—leap from pure seats to pure consumption or pure usage. I think that is a very dangerous way to go about it."
Why dangerous? Because you're asking customers to completely change how they think about your product while simultaneously changing how they budget for it. That's two massive shifts at once.
Customers who understood "$50 per user per month" now have to predict consumption patterns they've never tracked. Finance teams who budgeted fixed costs now face variable expenses. Procurement departments who negotiated annual contracts now need consumption forecasting models.
You're creating friction exactly when you want adoption.
The Hybrid Model: Training Wheels for Consumption
This is where Marcos sees the most success: combining fixed subscription with variable usage components.
"It is a very popular model to go in and try to blend subscription or flat with some usage. And I think that's good because that sort of starts to train the customer and condition them that there's usage that you need to track and it matters. Cuz beforehand they didn't have to pay attention to that stuff."
The psychology here is crucial. You're not throwing customers into the deep end. You're easing them into a world where usage matters.
How this typically works:
Start with a base subscription that includes an allocation or entitlement of usage. Think: $500/month includes 10,000 credits. Or: $2,000/month includes 100 hours of agent work.
Customers get predictability for budgeting. You get a revenue floor. And everyone gets educated about what "usage" actually means in your context.
Then, when customers exceed their allocation: "If you consume your allotment or if you consume your entitlements, there will be—we'll have to bump you up to the next tier or whatever that is."
Notice what's happening: customers are learning to track usage, but within guardrails. They're not facing unlimited variable costs. They're facing a predictable tier system with overages.
Marcos gives a real example: "I've used tools like clay.com where they use seat-based pricing but also use credit-based pricing."
That's the model. Seats for access. Credits for consumption. Predictability plus flexibility.
The Infrastructure Reality
Before you implement any usage-based component, Marcos's checklist of concerns applies:
Tracking: Can you accurately measure and attribute usage? Do you have the instrumentation to know who used what, when?
Billing: Can your systems handle variable charges? Monthly invoices that change based on consumption? Proration? Overages?
Sales compensation: How do reps get paid on consumption that happens months after the deal closes? Do they get ongoing commissions? Residuals?
Customer communication: Can you show customers their usage in real-time? Give them alerts before they hit overages? Provide transparency into what's driving costs?
If you can't answer "yes" to all of these, you're not ready for usage-based pricing. Build the foundation first.
When Pure Usage Actually Works
Marcos isn't saying usage-based pricing is wrong. He's saying the leap from subscription to pure usage is dangerous.
But there are scenarios where usage-based makes complete sense from day one:
You're launching a new product with no legacy pricing to migrate from
Your customers already use usage-based pricing in adjacent tools
The value metric is obvious and customers already track it
Your infrastructure is built for it from the ground up
Starting with usage-based is different from switching to it. If you're starting fresh, you avoid the transition pain.
The Decision Framework
o you've got three frameworks. Your AI features are ready to launch. Sales is asking for pricing. The board wants a strategy.
How do you actually decide?
The Clarity Test
Marcos makes this uncomfortably simple: if you can't clearly articulate which framework fits, your AI strategy isn't clear enough.
"If you're not sure, if you're like, 'Well, Marcos, it kind of is all over,' like, that probably means you're not clear with what you're trying to do."
Before you price anything, answer these questions:
What is the AI actually doing? Not what it could do. Not the vision. What does it do today that customers will pay for?
Is it enhancing, extending, or unlocking? Does it make existing features better (Framework #1)? Take your product further into new territory (Framework #2)? Or create entirely new capabilities (Framework #3)?
Who is the user? Same persona using your product better? Power users wanting advanced features? Or completely different buyers?
What's the cost structure? Stable and predictable? Variable but manageable? Or wildly different based on usage patterns?
If you can't answer these crisply, stop. Fix your strategy first. Pricing can't solve for strategic confusion.
The Safe Default: Add-On First
When Marcos encounters uncertainty, he has a fallback position: "The safest path would be add-on first and then decide if you want to fold it in or keep it separate based on the usage patterns, who's buying it, and what they're doing."
Launch as an add-on. Give it 2-4 quarters. Watch what actually happens, not what you hoped would happen.
Then decide based on real data:
Who's buying it? If it's everyone → consider Framework #1 (fold into core)
How are they using it? If it's power users dominating → Framework #2 (premium tier)
What outcomes are they getting? If it's creating new value pools → Framework #3 (standalone)
The add-on is your reconnaissance mission. You're gathering intelligence before committing to a pricing structure that's painful to change.
Red Flags to Avoid
Marcos has seen enough pricing disasters to know the warning signs:
Vague metrics: If you're charging for "credits" or "actions" that customers can't define, stop. "I saw someone the other day use per action. We're going to charge you per action. I'm like, what the hell does that even mean?"
Copycat pricing: Just because a competitor uses a model doesn't mean it fits your cost structure, customer base, or value proposition. "Resist the temptation to copy. Think about your own value, what you need to do, your specific customers, where you are in your journey, and price for that."
Complexity on complexity: Your customers are already learning how to use AI. Don't add pricing models that require a PhD to understand. "People are already going up the learning curve with adopting AI. Don't put confusing pricing on top of that."
If any of these red flags are waving, pause. Simplify. Get clear.
The One-Quarter Rule
Whatever you choose, give it time to breathe. Marcos used to advocate for the "two by two" rule two quarters to let pricing play out, revisit if you haven't changed in two years.
But AI is different: "Today with AI and agents and the pace in which this is moving, I actually think you should be changing your pricing every two quarters, some even quarterly depending on where your stage is."
The market is moving faster. Customer expectations are shifting. Your costs are evolving as models improve.
Quarterly or bi-quarterly reviews aren't premature optimization anymore. They're necessary adaptations.
Implementation
The Pricing Council Is Non-Negotiable
Marcos is adamant about this: pricing isn't a solo decision, but it needs a leader.
"Pricing is a team sport, but it does need a strong captain, right? And otherwise, nobody's going to do anything."
The structure that works: 5-8 people, cross-functional, meeting on a consistent cadence.
Who's in the room? "Sales representation, customer success, rev ops, of course you want product in there, marketing, all the key folks, finance."
But here's the critical detail most companies miss: it's not the C-suite, and it's not junior ICs. "If you're 30, 50, 80 million, often times it's not the C-suite and it's not like the line workers. It's usually like a director VP level in that meeting in the middle because they're close enough to the action and they also understand the strategy."
These are people who know what's actually happening in deals, in customer conversations, in the product but can also think strategically about where you're headed.
Running Meetings That Actually Matter
Most pricing committees fail because nobody's actually running them. People show up, talk about pricing opinions, nothing gets decided.
Marcos's prescription: "You have to plan out, I would say, a cadence and rhythm for the year. We're going to meet the last Friday of every month to talk about pricing. Here's the agenda. Here's what we're talking about. Here's who needs to be there."
And then the accountability check: "After your first three meetings, send out a short survey to all the attendees to say, was it valuable? The right level of detail, did you need to be there? And just make sure that meeting is super valuable."
If the meeting isn't valuable, kill it. Don't let it become another calendar obligation that everyone resents.
"The companies who get this meeting right, those are the ones that are really high performing. Those are the ones that are monetizing and breaking through."
The Roadmap Integration
Your pricing changes shouldn't be surprises. They need visibility alongside product launches.
"There should be a very visible swim lane for your pricing and packaging or monetization updates. You should be highlighting what you're doing to the pricing and packaging with each release and why."
This isn't bureaucracy. It's alignment. When engineering sees that pricing changes are tied to the Q3 agent launch, they understand the timeline matters. When sales sees that new AI features come with new pricing in Q2, they can prepare customers. When marketing sees the packaging shift planned for next quarter, they can build the campaign.
Pricing in the shadows creates chaos. Pricing in the roadmap creates coordination.
AI Strategy First, Pricing Second
Marcos sees this mistake constantly: teams trying to price AI before they've clarified what the AI is supposed to accomplish.
"A lot of murky AI strategy is going on and that needs to be solved first."
Are you enhancing core features? Expanding use cases for differentiation? Breaking into a new TAM? Displacing another product?
"Whatever you're doing, you have to be super explicit about it. It makes the pricing a lot easier and it makes everyone also behind it."
You can't price what you can't define. Strategy clarity isn't a nice-to-have. It's the prerequisite.
Getting It Right Before Your Competitors Do
The companies that will dominate the next three years aren't the ones with the most sophisticated AI. They're the ones who figured out how to capture the value their AI creates.
You have the three frameworks: Underscore the Core when AI enhances what you do. Upgrade the More when it extends into premium territory. Unlock the New when it creates entirely new capabilities.
The framework matters less than having clarity about which one fits.
Marcos's final advice cuts through the noise: "Resist the temptation to copy. Think about your own value, what you need to do, your specific customers, where you are in your journey, and price for that."
Stop copying competitors. Get your AI strategy straight first. Default to add-ons when uncertain. Avoid vague metrics. And move faster—quarterly reviews aren't premature anymore, they're necessary.
The timeline gives you room to get this right now, before AI pricing becomes table stakes.
FAQ
How should I price AI features in my SaaS product?
Use one of three frameworks based on what your AI does: (1) Underscore the Core - fold AI enhancements into your base product and raise prices 10%+, (2) Upgrade the More - offer AI as a premium tier or add-on for extended capabilities, or (3) Unlock the New - price AI as a standalone product when it creates entirely new use cases or targets different users.
Should I use usage-based pricing for AI features?
Not immediately. Marcos Rivera warns against jumping from subscription to pure usage-based pricing: "That is a very dangerous way to go about it." Start with hybrid models that combine fixed subscription with variable usage components. This trains customers to track consumption while maintaining pricing predictability. Pure usage-based pricing requires infrastructure for tracking, billing systems that handle variability, and new sales compensation models.
What is hybrid pricing for AI?
Hybrid pricing combines a fixed subscription fee with variable usage charges. For example, $500/month includes 10,000 AI credits, with overages moving customers to the next tier. This model "starts to train the customer and condition them that there's usage that you need to track and it matters," while providing budgeting predictability. Companies like Clay.com use seat-based pricing combined with credit-based consumption.
How do you price AI agents?
Price AI agents using a fixed + variable structure. The fixed component covers configuration, training, and onboarding. The variable component ties to units of work completed—like transactions processed, claims approved, or legal documents summarized. Critical: define the "anti-outcome" (what doesn't count) to avoid billing disputes. For risk-averse customers, use milestone-based pricing where payments trigger at specific usage thresholds.
What are AI pricing credits and should I use them?
AI credits are usage units that customers spend to access AI features. However, Marcos cautions against vague credit systems: credits must have clear definitions that customers understand. Avoid pricing "per action" or "per credit" without explaining exactly what constitutes an action or what credits purchase. Confusing metrics add friction when customers are already learning how to use AI.
How often should I change my AI pricing?
Review AI pricing every quarter or every two quarters—much faster than traditional SaaS. Marcos used to recommend the "two by two rule" (two quarters to assess, revisit if unchanged in two years), but "with AI and agents and the pace in which this is moving, I actually think you should be changing your pricing every two quarters, some even quarterly." The market is evolving too quickly for annual pricing reviews.
What is outcome-based pricing for AI?
Outcome-based pricing charges for results delivered rather than usage consumed. It works when "the delegation is going from the human to the system"—when AI agents handle complete workflows autonomously. Examples: charging per insurance claim processed, per legal discovery package summarized, or per authorization approved. This model captures value created by the system and aligns pricing with customer ROI.
The Delegation Shift: Why Outcome Pricing Works
Here's the fundamental change that makes Framework #3 different: "The delegation is going from the human to the system now, right? And so now that you're delegating to the system, the system can now capture a little more percentage of that value as long as it's super clear that the system is responsible for that value."
This is profound. When a human uses AI as a tool, they're still responsible for the outcome. The human runs the search, reviews the results, makes the decision. AI assists.
But when an agent handles the entire workflow—when it processes the insurance claim, summarizes the legal discovery, approves the authorization—the system is now accountable for the outcome. And that shifts pricing dynamics entirely.
You're no longer charging for access or usage. You're charging for work completed. For problems solved. For outcomes delivered.
The Fixed + Variable Structure
The model Marcos sees working across agentic solutions breaks down into two clear components:
The fixed component covers everything that happens before the agent starts doing work:
Configuration and setup
Training the system on your specific workflows
Onboarding and integration
Basic support and maintenance
This is predictable revenue. It covers your costs for getting the customer live. It creates a baseline you can count on.
The variable component is where the magic—and the margin—happens: It's counting units of work. But here's where companies get tricky: defining what counts and what doesn't.
Defining the Anti-Outcome
Marcos introduces a critical concept that most companies miss: "They're very specific. It's very clear and it's sort of the anti-outcome is what they need to define too. What do I mean? I mean what does not count as an outcome so that way we don't get into arguments about charging."
This is brilliant. It's not enough to say "we charge per claim processed." You need to define:
Does a rejected claim count?
Does a claim that requires human review count?
Does a duplicate submission count?
Does a test transaction count?
The anti-outcome prevents disputes. It creates clarity. It stops customers from feeling nickel-and-dimed when edge cases come up.
Health tech example: If you're charging per authorization approved, you need to specify that failed authorizations, duplicates, and test cases don't count. Only completed, successful authorizations that deliver actual value.
Legal tech example: If you're charging per discovery package summarized, define minimum document counts, exclude corrupted files, clarify what happens when documents are in unsupported formats.
The more specific you are about what doesn't count, the less friction you'll face at billing time.
Milestone-Based Alternatives for Risk-Averse Customers
Not every customer is comfortable with pure variable pricing, even when it's tied to outcomes. Some industries, some company cultures, just prefer predictability.
Marcos sees this adaptation emerging: "For some companies or industries where they're really kind of skittish about variable, they love more predictability, they love other things... maybe it's more milestone driven. Hey when we hit this threshold we get a payment or a fee, maybe we hit that threshold we get a payment and fee."
This is the best of both worlds for cautious buyers:
They get predictability through milestone payments
You get increasing revenue as the agent proves value
Both sides are incentivized for deeper adoption
Example structure:
Milestone 1: Agent processes first 100 units → Payment
Milestone 2: Agent reaches 500 units → Payment
Milestone 3: Agent hits 1,000 units → Payment
Beyond that: Per-unit pricing kicks in
"It allows them to get deeper and deeper with the agent over time, which is exactly what you want," Marcos explains.
The customer isn't betting big upfront. They're paying as value gets proven. And as they see results, the variable component becomes less scary and more exciting.
The Market Penetration Strategy
Here's where Unlock the New becomes genuinely strategic, not just tactical pricing.
If your AI agent can do something your core product couldn't, you can now enter markets where you previously had no shot. Your core platform was too expensive, too complex, or solving the wrong problem. But this agent? It solves a specific pain point those markets actually have.
Price it accordingly. Position it independently. Build a customer base you couldn't access before.
Then and this is where bundling comes in—once they're using your agent successfully, introduce them to your platform. "Hey, you love what the agent does. Imagine if it integrated with our full platform for end-to-end workflow management."
You've just turned an agent customer into a platform customer. That's the penetration strategy.
When Bundling Creates Competitive Moats
Marcos points out the defensive advantage: "You can even bundle it with your core and your platform as well. It also gives you some competitive advantages."
Here's why that matters:
A startup launching a standalone agent can compete on features and price. They're nimble, focused, probably cheaper.
But if you're offering the agent plus the platform plus integrations that make everything work together seamlessly—at a bundled price that's attractive—suddenly that startup has to compete against an ecosystem, not just a product.
Customers choose integrated solutions over best-of-breed point solutions when the integration tax (time, money, complexity) is high enough. Your bundle makes the integration tax zero.
That's a moat.
The Margin Reality Check
Remember Marcos's warning about margin variability? It's most pronounced here.
Agents are expensive to run. Token consumption varies wildly based on task complexity. One customer's workflows might cost you 10% of revenue. Another's might cost 40%.
This is exactly why outcome-based or unit-based pricing exists. You're tying revenue to actual work performed, which naturally correlates with costs incurred.
But you need to do the math. If you're charging $5 per unit of work, and some units cost you $6 to deliver, you're subsidizing adoption. That's fine as a short-term market penetration strategy. It's disastrous as a long-term business model.
The companies winning with Framework #3 are obsessively tracking unit economics. They know exactly what each outcome costs to deliver. They price with margin built in. And they're constantly optimizing their agents to reduce token consumption and infrastructure costs.
Because in this framework, your profit scales with your efficiency, not just with your growth.
The Hybrid vs. Usage-Based Debate
There's a seductive logic to usage-based pricing for AI: customers pay for what they use, and you cover your variable costs. Clean. Fair. Simple.
Except it's not simple at all.
Why Usage-Based Pricing is Trending (And Why It's Tricky)
Marcos sees the appeal clearly: "There's a lot of movement towards usage-based pricing, right? And the reason why is to really cover a lot of that LLM costs and making sure you don't lose your shirt on your margin as your customers adopt your product."
The math makes sense. AI costs vary wildly by usage. Pure subscription pricing means heavy users subsidized by light users, or worse—you lose money on your most engaged customers.
But here's the problem Marcos sees everywhere: "Not everyone is ready for usage-based pricing. Sounds good, right? When you and I say it, right? It sounds good. But there's infrastructure concerns. There's process concerns, there's tracking concerns, there's sales compensation concerns, there's all these different things that you have to set the stage for before you roll into usage-based pricing."
Your billing system needs to handle variable charges. Your sales team needs new compensation models (do they get commissions on usage that happens six months after the sale?). Your customers need to track and predict costs. Your finance team needs new forecasting models.
It's not just a pricing change. It's an operational transformation.
Marcos's Warning: Don't Leap
"My advice is not to just leap in from—say you're charging by seats today—leap from pure seats to pure consumption or pure usage. I think that is a very dangerous way to go about it."
Why dangerous? Because you're asking customers to completely change how they think about your product while simultaneously changing how they budget for it. That's two massive shifts at once.
Customers who understood "$50 per user per month" now have to predict consumption patterns they've never tracked. Finance teams who budgeted fixed costs now face variable expenses. Procurement departments who negotiated annual contracts now need consumption forecasting models.
You're creating friction exactly when you want adoption.
The Hybrid Model: Training Wheels for Consumption
This is where Marcos sees the most success: combining fixed subscription with variable usage components.
"It is a very popular model to go in and try to blend subscription or flat with some usage. And I think that's good because that sort of starts to train the customer and condition them that there's usage that you need to track and it matters. Cuz beforehand they didn't have to pay attention to that stuff."
The psychology here is crucial. You're not throwing customers into the deep end. You're easing them into a world where usage matters.
How this typically works:
Start with a base subscription that includes an allocation or entitlement of usage. Think: $500/month includes 10,000 credits. Or: $2,000/month includes 100 hours of agent work.
Customers get predictability for budgeting. You get a revenue floor. And everyone gets educated about what "usage" actually means in your context.
Then, when customers exceed their allocation: "If you consume your allotment or if you consume your entitlements, there will be—we'll have to bump you up to the next tier or whatever that is."
Notice what's happening: customers are learning to track usage, but within guardrails. They're not facing unlimited variable costs. They're facing a predictable tier system with overages.
Marcos gives a real example: "I've used tools like clay.com where they use seat-based pricing but also use credit-based pricing."
That's the model. Seats for access. Credits for consumption. Predictability plus flexibility.
The Infrastructure Reality
Before you implement any usage-based component, Marcos's checklist of concerns applies:
Tracking: Can you accurately measure and attribute usage? Do you have the instrumentation to know who used what, when?
Billing: Can your systems handle variable charges? Monthly invoices that change based on consumption? Proration? Overages?
Sales compensation: How do reps get paid on consumption that happens months after the deal closes? Do they get ongoing commissions? Residuals?
Customer communication: Can you show customers their usage in real-time? Give them alerts before they hit overages? Provide transparency into what's driving costs?
If you can't answer "yes" to all of these, you're not ready for usage-based pricing. Build the foundation first.
When Pure Usage Actually Works
Marcos isn't saying usage-based pricing is wrong. He's saying the leap from subscription to pure usage is dangerous.
But there are scenarios where usage-based makes complete sense from day one:
You're launching a new product with no legacy pricing to migrate from
Your customers already use usage-based pricing in adjacent tools
The value metric is obvious and customers already track it
Your infrastructure is built for it from the ground up
Starting with usage-based is different from switching to it. If you're starting fresh, you avoid the transition pain.
The Decision Framework
o you've got three frameworks. Your AI features are ready to launch. Sales is asking for pricing. The board wants a strategy.
How do you actually decide?
The Clarity Test
Marcos makes this uncomfortably simple: if you can't clearly articulate which framework fits, your AI strategy isn't clear enough.
"If you're not sure, if you're like, 'Well, Marcos, it kind of is all over,' like, that probably means you're not clear with what you're trying to do."
Before you price anything, answer these questions:
What is the AI actually doing? Not what it could do. Not the vision. What does it do today that customers will pay for?
Is it enhancing, extending, or unlocking? Does it make existing features better (Framework #1)? Take your product further into new territory (Framework #2)? Or create entirely new capabilities (Framework #3)?
Who is the user? Same persona using your product better? Power users wanting advanced features? Or completely different buyers?
What's the cost structure? Stable and predictable? Variable but manageable? Or wildly different based on usage patterns?
If you can't answer these crisply, stop. Fix your strategy first. Pricing can't solve for strategic confusion.
The Safe Default: Add-On First
When Marcos encounters uncertainty, he has a fallback position: "The safest path would be add-on first and then decide if you want to fold it in or keep it separate based on the usage patterns, who's buying it, and what they're doing."
Launch as an add-on. Give it 2-4 quarters. Watch what actually happens, not what you hoped would happen.
Then decide based on real data:
Who's buying it? If it's everyone → consider Framework #1 (fold into core)
How are they using it? If it's power users dominating → Framework #2 (premium tier)
What outcomes are they getting? If it's creating new value pools → Framework #3 (standalone)
The add-on is your reconnaissance mission. You're gathering intelligence before committing to a pricing structure that's painful to change.
Red Flags to Avoid
Marcos has seen enough pricing disasters to know the warning signs:
Vague metrics: If you're charging for "credits" or "actions" that customers can't define, stop. "I saw someone the other day use per action. We're going to charge you per action. I'm like, what the hell does that even mean?"
Copycat pricing: Just because a competitor uses a model doesn't mean it fits your cost structure, customer base, or value proposition. "Resist the temptation to copy. Think about your own value, what you need to do, your specific customers, where you are in your journey, and price for that."
Complexity on complexity: Your customers are already learning how to use AI. Don't add pricing models that require a PhD to understand. "People are already going up the learning curve with adopting AI. Don't put confusing pricing on top of that."
If any of these red flags are waving, pause. Simplify. Get clear.
The One-Quarter Rule
Whatever you choose, give it time to breathe. Marcos used to advocate for the "two by two" rule two quarters to let pricing play out, revisit if you haven't changed in two years.
But AI is different: "Today with AI and agents and the pace in which this is moving, I actually think you should be changing your pricing every two quarters, some even quarterly depending on where your stage is."
The market is moving faster. Customer expectations are shifting. Your costs are evolving as models improve.
Quarterly or bi-quarterly reviews aren't premature optimization anymore. They're necessary adaptations.
Implementation
The Pricing Council Is Non-Negotiable
Marcos is adamant about this: pricing isn't a solo decision, but it needs a leader.
"Pricing is a team sport, but it does need a strong captain, right? And otherwise, nobody's going to do anything."
The structure that works: 5-8 people, cross-functional, meeting on a consistent cadence.
Who's in the room? "Sales representation, customer success, rev ops, of course you want product in there, marketing, all the key folks, finance."
But here's the critical detail most companies miss: it's not the C-suite, and it's not junior ICs. "If you're 30, 50, 80 million, often times it's not the C-suite and it's not like the line workers. It's usually like a director VP level in that meeting in the middle because they're close enough to the action and they also understand the strategy."
These are people who know what's actually happening in deals, in customer conversations, in the product but can also think strategically about where you're headed.
Running Meetings That Actually Matter
Most pricing committees fail because nobody's actually running them. People show up, talk about pricing opinions, nothing gets decided.
Marcos's prescription: "You have to plan out, I would say, a cadence and rhythm for the year. We're going to meet the last Friday of every month to talk about pricing. Here's the agenda. Here's what we're talking about. Here's who needs to be there."
And then the accountability check: "After your first three meetings, send out a short survey to all the attendees to say, was it valuable? The right level of detail, did you need to be there? And just make sure that meeting is super valuable."
If the meeting isn't valuable, kill it. Don't let it become another calendar obligation that everyone resents.
"The companies who get this meeting right, those are the ones that are really high performing. Those are the ones that are monetizing and breaking through."
The Roadmap Integration
Your pricing changes shouldn't be surprises. They need visibility alongside product launches.
"There should be a very visible swim lane for your pricing and packaging or monetization updates. You should be highlighting what you're doing to the pricing and packaging with each release and why."
This isn't bureaucracy. It's alignment. When engineering sees that pricing changes are tied to the Q3 agent launch, they understand the timeline matters. When sales sees that new AI features come with new pricing in Q2, they can prepare customers. When marketing sees the packaging shift planned for next quarter, they can build the campaign.
Pricing in the shadows creates chaos. Pricing in the roadmap creates coordination.
AI Strategy First, Pricing Second
Marcos sees this mistake constantly: teams trying to price AI before they've clarified what the AI is supposed to accomplish.
"A lot of murky AI strategy is going on and that needs to be solved first."
Are you enhancing core features? Expanding use cases for differentiation? Breaking into a new TAM? Displacing another product?
"Whatever you're doing, you have to be super explicit about it. It makes the pricing a lot easier and it makes everyone also behind it."
You can't price what you can't define. Strategy clarity isn't a nice-to-have. It's the prerequisite.
Getting It Right Before Your Competitors Do
The companies that will dominate the next three years aren't the ones with the most sophisticated AI. They're the ones who figured out how to capture the value their AI creates.
You have the three frameworks: Underscore the Core when AI enhances what you do. Upgrade the More when it extends into premium territory. Unlock the New when it creates entirely new capabilities.
The framework matters less than having clarity about which one fits.
Marcos's final advice cuts through the noise: "Resist the temptation to copy. Think about your own value, what you need to do, your specific customers, where you are in your journey, and price for that."
Stop copying competitors. Get your AI strategy straight first. Default to add-ons when uncertain. Avoid vague metrics. And move faster—quarterly reviews aren't premature anymore, they're necessary.
The timeline gives you room to get this right now, before AI pricing becomes table stakes.
FAQ
How should I price AI features in my SaaS product?
Use one of three frameworks based on what your AI does: (1) Underscore the Core - fold AI enhancements into your base product and raise prices 10%+, (2) Upgrade the More - offer AI as a premium tier or add-on for extended capabilities, or (3) Unlock the New - price AI as a standalone product when it creates entirely new use cases or targets different users.
Should I use usage-based pricing for AI features?
Not immediately. Marcos Rivera warns against jumping from subscription to pure usage-based pricing: "That is a very dangerous way to go about it." Start with hybrid models that combine fixed subscription with variable usage components. This trains customers to track consumption while maintaining pricing predictability. Pure usage-based pricing requires infrastructure for tracking, billing systems that handle variability, and new sales compensation models.
What is hybrid pricing for AI?
Hybrid pricing combines a fixed subscription fee with variable usage charges. For example, $500/month includes 10,000 AI credits, with overages moving customers to the next tier. This model "starts to train the customer and condition them that there's usage that you need to track and it matters," while providing budgeting predictability. Companies like Clay.com use seat-based pricing combined with credit-based consumption.
How do you price AI agents?
Price AI agents using a fixed + variable structure. The fixed component covers configuration, training, and onboarding. The variable component ties to units of work completed—like transactions processed, claims approved, or legal documents summarized. Critical: define the "anti-outcome" (what doesn't count) to avoid billing disputes. For risk-averse customers, use milestone-based pricing where payments trigger at specific usage thresholds.
What are AI pricing credits and should I use them?
AI credits are usage units that customers spend to access AI features. However, Marcos cautions against vague credit systems: credits must have clear definitions that customers understand. Avoid pricing "per action" or "per credit" without explaining exactly what constitutes an action or what credits purchase. Confusing metrics add friction when customers are already learning how to use AI.
How often should I change my AI pricing?
Review AI pricing every quarter or every two quarters—much faster than traditional SaaS. Marcos used to recommend the "two by two rule" (two quarters to assess, revisit if unchanged in two years), but "with AI and agents and the pace in which this is moving, I actually think you should be changing your pricing every two quarters, some even quarterly." The market is evolving too quickly for annual pricing reviews.
What is outcome-based pricing for AI?
Outcome-based pricing charges for results delivered rather than usage consumed. It works when "the delegation is going from the human to the system"—when AI agents handle complete workflows autonomously. Examples: charging per insurance claim processed, per legal discovery package summarized, or per authorization approved. This model captures value created by the system and aligns pricing with customer ROI.
Aanchal Parmar
Aanchal Parmar
Aanchal Parmar
Aanchal Parmar
Aanchal Parmar heads content marketing at Flexprice.io. She’s been in the content for seven years across SaaS, Web3, and now AI infra. When she’s not writing about monetization, she’s either signing up for a new dance class or testing a recipe that’s definitely too ambitious for a weeknight.
Aanchal Parmar heads content marketing at Flexprice.io. She’s been in the content for seven years across SaaS, Web3, and now AI infra. When she’s not writing about monetization, she’s either signing up for a new dance class or testing a recipe that’s definitely too ambitious for a weeknight.
Aanchal Parmar heads content marketing at Flexprice.io. She’s been in the content for seven years across SaaS, Web3, and now AI infra. When she’s not writing about monetization, she’s either signing up for a new dance class or testing a recipe that’s definitely too ambitious for a weeknight.
Aanchal Parmar heads content marketing at Flexprice.io. She’s been in the content for seven years across SaaS, Web3, and now AI infra. When she’s not writing about monetization, she’s either signing up for a new dance class or testing a recipe that’s definitely too ambitious for a weeknight.
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