I think my experience at Notarize is relevant here, not because it is AI but because it offloads a core task (i.e. a mortgage closing) from someone. We "sell the work" by offloading the task (albeit to other people, not to AI) and allow our customers to repurpose employee time to other accretive tasks or to alter their staffing models. Much of our ROI story is rooted in that outcome. The primary challenge though is to actually get customers to adopt above a threshold required to change either their internal operations or staffing models. The beginning of a customer's adoption curve is the worst - they're forced to run bifurcated processes (actually costing more) AND the actual people you aim to offset are often critical in managing that transition. At Notarize, I've cribbed some thoughts from the medical and hospital industries which believes that a 30% adoption rate is required to see the ROI of actual process change, beyond which they can actually adjust the "standard of care" and make that new/better process the new normal. Getting to 30% is really hard and we've obsessed over doing just that, which is to convince our customers to make us the standard of care for mortgage closings, auto sales, you name it. I think it will be especially hard for AI in some of the industries you outline above, particularly legal/compliance/etc. Why? Everyone says LLMs drift, but that will surely be solved. Many of the things you described are considered the practice of law and people will need to adjudicate UPL claims. Fun!! I think the real issue is regulatory headwinds. Specifically, regulators are terrified of algorithms/machine learning/AI systems instituting global systemic bias. Take a property appraisal, which everyone agrees should be digitized. Regulators would rather local consumers connect with local appraisers to disperse and decentralize the bias - an African American consumer may get an African Appraiser and "win" on one and another might get a racist white appraiser and "lose" on another. That is better to them than a one nebulous system making judgements they cannot assess. And the government has no ability, mandate, or agency even to test these models. So how is a large bank who is constantly sued for unfair lending practices able to adopt these systems? If anything, recent advancements are showing any/all of these issues can be solved... but for AI to advance as you outline, it needs to think much more deeply about bias and how to instill the confidence required to take over from the humans who are obviously slow, but easier to regulate and more random in their outcomes.
such a great point Pat. Thank you for posting. I love your 30% adoption threshold... that does seem key. and yes the big question on regulation and what impact that's going to have is still an unknown...
In a way this has already been done in other industries besides AI - services like DoNotPay or Orkin have taken traditional industries, figured out optimizations at scale / how to automate the "dirty work", and profited in huge ways. The issue is - scaling the sales and implementation (well) is harder than just throwing AI at it. Great read!
I run a startup (Lexoo) where we both sell the legal work (in our case outsourced BAU contract negotiation) and sell software, so can provide some insight on this. For the 'selling the work' we use a team of in-house lawyers who use a lot of our own tech to be efficient. We can definitely charge a lot more there than on the software, as we indeed compete with services companies like law firms as opposed to software prices.
Separately, we license some of our tech to customers who may not want to outsource. Here, we are forced by the market to charge typical per seat pricing.
However, one limitation on the services side, is that a lot of customers (our customers are companies with in-house legal teams) have fundamental reasons why they don't want to outsource. So the market size in terms of # of companies who can buy outsourced legal services for work they typically do themselves, seems lower than the # of companies who consider buying our software.
The other hard bit about selling the 'work', is the expected standards in terms of how 'custom' it is are way higher compared to what clients expect from our software. So we end up having to hire quite experienced lawyers to basically enable the 'final 10% of quality control to happen. So that's a bit of a scaling constraint to selling the work.
In the lens of productivity cost and job-to-be-done:
SaaS is just cheaper expression of productivity compares to your own resources. Naturally you would opt to use them to get the job done.
LLM reduces the cost of productivity by 2-3 orders of magnitude. Startups should focus on finding the job that require high productivity cost and address those with LLM.
How do firms differentiate themselves though if everyone uses this? If I’m a lawyer and, after the gains from a first mover advantage, how do you market yourself? If you’re selling the work then it feels like every law firm becomes a glorified marketing/brand play. Some might argue that’s true today, but I still feel like reputation, experience, etc were the defining characteristics of that specific profession.
Would this not be the same as making the case to build your own CRM nowadays as a way to get competitive advantage? So why use Salesforce if all other firms use it as their CRM... It will get commoditized and companies/firms will continue to double on what makes them different.
I work for a Workers Compensation law firm for 13 years and currently work on a saas startup app for Workers Compensation lawyers. My co-founder and I are interested in presenting our platform to you. I think you are the perfect partner for us. Please let me know if you are interested. Thx and I look forward to hearing back.
Would love to connect with you and the founder of Automate Ventures to discuss further in the New Year. We're launching professional services companies that follow the model you describe.
Great Post / Tech Forecasting - So much going on here that can be extrapolated even further. It will be interesting to see which SaaS apps adopt new tech and evolve and which get passed by new AI based services. Also interested to see how the sales model evovles - will it too use AI? AI selling AI?Traditional SaaS (great productivity enhancer that I use in my company) seems a bit tired - there is less and less differentiation as all claim to do everything.
Love the concept! Wondering which party should be held accountable if errors occur - the startup that created the "work," the end user using it, the AI model itself, someone else? There can be insurance opportunity created around that too.
Great read. Law is also a great domain for this, because costs like this are so often passed onto the end client. The end client is happy because it probably cost less than what a lawyer would have cost. The law firm is happy because they can increase matter flow and focus on adding high-leverage strategic value, which is worth a much higher hourly rate.
There’s a new SaaS pricing fad making the rounds: outcome-based pricing. And in AI, particularly contact center AI and multi-use agent platforms, I don’t see a strong case for it.
Here’s why:
1. AI Is Replacing Labor, But Labor Was Never Priced on Outcomes
If AI agents are replacing contact center workers, then we should look at how BPOs (Business Process Outsourcing providers) price today. Spoiler: they don’t use outcome-based pricing. Despite decades of calls being handled, few BPOs operate on a “you only pay us if the issue is resolved” model. Why?
Attribution is messy. Is a ticket resolved if the customer stops responding?
Rework is common. What if they call back two days later for the same issue?
Resolution is subjective. In sales, does a transfer count? A nudge? Only a closed deal?
AI doesn’t change this — it just scales it. The fuzziness remains.
2. Outcome-Based Pricing Doesn’t Scale Across Use Cases
AI agents aren’t single-function tools. They're multi-use platforms. One customer uses it for L1 support. Another for routing. Another for sales qualification.
But “outcomes” mean vastly different things across these verticals:
In support, is it resolution? CSAT?
In sales, is it a closed deal? A qualified lead?
In back office ops, what even counts as a win?
Trying to design pricing logic that accounts for every permutation becomes a pricing ops nightmare. Worse, it slows down adoption because procurement teams don’t know how to forecast spend.
3. AI Infra Costs Are Falling – Buyers Want to Capture the Delta
Everyone knows where foundational model costs are headed: down. And fast.
If your AI agent becomes cheaper to run each quarter, do you pass those savings to customers? Or lock them into rigid outcome-based contracts where they can't realize efficiency gains?
In reality, outcome-based pricing often hides high costs behind complex contractual terms. It's less about customer alignment and more about risk-shifting — and in many cases, about masking an overpriced product with an attractive narrative.
4. Value-Based ≠ Outcome-Based — Don’t Confuse the Two
Here’s the critical distinction:
Value-based pricing means your price reflects the value you create.
Outcome-based pricing means you only get paid if a specific result happens.
You can be deeply aligned with customer value — and even justify premium pricing — without tying revenue to uncertain or subjective outcomes.
This is where many people get confused.
Reconciling with the Vertical SaaS Playbook
Outcome-based pricing can work—but only in very specific contexts. In vertical SaaS, where the solution is purpose-built to solve high-value problems within a tightly defined industry (think healthcare claims, supply chain optimization, or fraud detection), the logic holds.
Why?
Because in these verticals:
You own the entire problem space, not just one sliver like support or a sales nudge.
You can track concrete, repeatable results—claims processed, fraudulent transactions prevented, cost per shipment reduced.
The buyer isn’t experimenting with a toolkit—they’re solving a mission-critical problem with clear ROI.
✅ In these cases, outcomes can justify premium pricing—not as a conditional revenue share, but as a value-based premium for delivering predictable impact.
Now try applying that same logic to a general-purpose AI platform that spans dozens of use cases across dozens of industries. It unravels quickly. What counts as a resolved support case? What if the user ghosts? What if a sale is "nudged" but not closed?
Outcomes become fuzzy, inconsistent, and highly contingent on downstream events that the platform neither controls nor can fairly be judged by.
That’s why outcome-based pricing sounds attractive in theory—but breaks in practice when you try to scale it horizontally.
Final Word
AI vendors don’t need to bet on gimmicky pricing to prove value.
If you’re truly building transformational software, show me:
Clear tiers aligned with business use
Transparent usage models customers can predict and plan for
Optional add-ons for performance enhancement — not buried fees tied to arbitrary KPIs
Pricing should create trust, not confusion. Clarity scales. Contracts don’t.
There’s a new SaaS pricing fad making the rounds: outcome-based pricing. And in AI, particularly contact center AI and multi-use agent platforms, I don’t see a strong case for it.
Here’s why:
1. AI Is Replacing Labor, But Labor Was Never Priced on Outcomes
If AI agents are replacing contact center workers, then we should look at how BPOs (Business Process Outsourcing providers) price today. Spoiler: they don’t use outcome-based pricing. Despite decades of calls being handled, few BPOs operate on a “you only pay us if the issue is resolved” model. Why?
Attribution is messy. Is a ticket resolved if the customer stops responding?
Rework is common. What if they call back two days later for the same issue?
Resolution is subjective. In sales, does a transfer count? A nudge? Only a closed deal?
AI doesn’t change this — it just scales it. The fuzziness remains.
2. Outcome-Based Pricing Doesn’t Scale Across Use Cases
AI agents aren’t single-function tools. They're multi-use platforms. One customer uses it for L1 support. Another for routing. Another for sales qualification.
But “outcomes” mean vastly different things across these verticals:
In support, is it resolution? CSAT?
In sales, is it a closed deal? A qualified lead?
In back office ops, what even counts as a win?
Trying to design pricing logic that accounts for every permutation becomes a pricing ops nightmare. Worse, it slows down adoption because procurement teams don’t know how to forecast spend.
3. AI Infra Costs Are Falling – Buyers Want to Capture the Delta
Everyone knows where foundational model costs are headed: down. And fast.
If your AI agent becomes cheaper to run each quarter, do you pass those savings to customers? Or lock them into rigid outcome-based contracts where they can't realize efficiency gains?
In reality, outcome-based pricing often hides high costs behind complex contractual terms. It's less about customer alignment and more about risk-shifting — and in many cases, about masking an overpriced product with an attractive narrative.
4. Value-Based ≠ Outcome-Based — Don’t Confuse the Two
Here’s the critical distinction:
Value-based pricing means your price reflects the value you create.
Outcome-based pricing means you only get paid if a specific result happens.
You can be deeply aligned with customer value — and even justify premium pricing — without tying revenue to uncertain or subjective outcomes.
This is where many people get confused.
Reconciling with the Vertical SaaS Playbook
Outcome-based pricing can work—but only in very specific contexts. In vertical SaaS, where the solution is purpose-built to solve high-value problems within a tightly defined industry (think healthcare claims, supply chain optimization, or fraud detection), the logic holds.
Why?
Because in these verticals:
You own the entire problem space, not just one sliver like support or a sales nudge.
You can track concrete, repeatable results—claims processed, fraudulent transactions prevented, cost per shipment reduced.
The buyer isn’t experimenting with a toolkit—they’re solving a mission-critical problem with clear ROI.
✅ In these cases, outcomes can justify premium pricing—not as a conditional revenue share, but as a value-based premium for delivering predictable impact.
Now try applying that same logic to a general-purpose AI platform that spans dozens of use cases across dozens of industries. It unravels quickly. What counts as a resolved support case? What if the user ghosts? What if a sale is "nudged" but not closed?
Outcomes become fuzzy, inconsistent, and highly contingent on downstream events that the platform neither controls nor can fairly be judged by.
That’s why outcome-based pricing sounds attractive in theory—but breaks in practice when you try to scale it horizontally.
Final Word
AI vendors don’t need to bet on gimmicky pricing to prove value.
If you’re truly building transformational software, show me:
Clear tiers aligned with business use
Transparent usage models customers can predict and plan for
Optional add-ons for performance enhancement — not buried fees tied to arbitrary KPIs
Pricing should create trust, not confusion. Clarity scales. Contracts don’t.
This is so smart.
I just keep reading and rereading it.
So smart.
I think my experience at Notarize is relevant here, not because it is AI but because it offloads a core task (i.e. a mortgage closing) from someone. We "sell the work" by offloading the task (albeit to other people, not to AI) and allow our customers to repurpose employee time to other accretive tasks or to alter their staffing models. Much of our ROI story is rooted in that outcome. The primary challenge though is to actually get customers to adopt above a threshold required to change either their internal operations or staffing models. The beginning of a customer's adoption curve is the worst - they're forced to run bifurcated processes (actually costing more) AND the actual people you aim to offset are often critical in managing that transition. At Notarize, I've cribbed some thoughts from the medical and hospital industries which believes that a 30% adoption rate is required to see the ROI of actual process change, beyond which they can actually adjust the "standard of care" and make that new/better process the new normal. Getting to 30% is really hard and we've obsessed over doing just that, which is to convince our customers to make us the standard of care for mortgage closings, auto sales, you name it. I think it will be especially hard for AI in some of the industries you outline above, particularly legal/compliance/etc. Why? Everyone says LLMs drift, but that will surely be solved. Many of the things you described are considered the practice of law and people will need to adjudicate UPL claims. Fun!! I think the real issue is regulatory headwinds. Specifically, regulators are terrified of algorithms/machine learning/AI systems instituting global systemic bias. Take a property appraisal, which everyone agrees should be digitized. Regulators would rather local consumers connect with local appraisers to disperse and decentralize the bias - an African American consumer may get an African Appraiser and "win" on one and another might get a racist white appraiser and "lose" on another. That is better to them than a one nebulous system making judgements they cannot assess. And the government has no ability, mandate, or agency even to test these models. So how is a large bank who is constantly sued for unfair lending practices able to adopt these systems? If anything, recent advancements are showing any/all of these issues can be solved... but for AI to advance as you outline, it needs to think much more deeply about bias and how to instill the confidence required to take over from the humans who are obviously slow, but easier to regulate and more random in their outcomes.
such a great point Pat. Thank you for posting. I love your 30% adoption threshold... that does seem key. and yes the big question on regulation and what impact that's going to have is still an unknown...
"We need one throat to choke" is a big requirement for a lot of businesses and operators. Like the old Reddit group--WHO'S REPONIBLE? (sic)
In a way this has already been done in other industries besides AI - services like DoNotPay or Orkin have taken traditional industries, figured out optimizations at scale / how to automate the "dirty work", and profited in huge ways. The issue is - scaling the sales and implementation (well) is harder than just throwing AI at it. Great read!
This is a phenomenal take.
Great read!
Great article!
I run a startup (Lexoo) where we both sell the legal work (in our case outsourced BAU contract negotiation) and sell software, so can provide some insight on this. For the 'selling the work' we use a team of in-house lawyers who use a lot of our own tech to be efficient. We can definitely charge a lot more there than on the software, as we indeed compete with services companies like law firms as opposed to software prices.
Separately, we license some of our tech to customers who may not want to outsource. Here, we are forced by the market to charge typical per seat pricing.
However, one limitation on the services side, is that a lot of customers (our customers are companies with in-house legal teams) have fundamental reasons why they don't want to outsource. So the market size in terms of # of companies who can buy outsourced legal services for work they typically do themselves, seems lower than the # of companies who consider buying our software.
The other hard bit about selling the 'work', is the expected standards in terms of how 'custom' it is are way higher compared to what clients expect from our software. So we end up having to hire quite experienced lawyers to basically enable the 'final 10% of quality control to happen. So that's a bit of a scaling constraint to selling the work.
This is a really great post. I should come back to read this again.
Memo to myself: https://share.glasp.co/kei/?p=Xo7I0Nx15vdr8FdpZLs1
I talked about this before: https://rpgbx.substack.com/p/why-personalized-ai-companies-win
In the lens of productivity cost and job-to-be-done:
SaaS is just cheaper expression of productivity compares to your own resources. Naturally you would opt to use them to get the job done.
LLM reduces the cost of productivity by 2-3 orders of magnitude. Startups should focus on finding the job that require high productivity cost and address those with LLM.
How do firms differentiate themselves though if everyone uses this? If I’m a lawyer and, after the gains from a first mover advantage, how do you market yourself? If you’re selling the work then it feels like every law firm becomes a glorified marketing/brand play. Some might argue that’s true today, but I still feel like reputation, experience, etc were the defining characteristics of that specific profession.
it's not that different from how it is now
Would this not be the same as making the case to build your own CRM nowadays as a way to get competitive advantage? So why use Salesforce if all other firms use it as their CRM... It will get commoditized and companies/firms will continue to double on what makes them different.
I work for a Workers Compensation law firm for 13 years and currently work on a saas startup app for Workers Compensation lawyers. My co-founder and I are interested in presenting our platform to you. I think you are the perfect partner for us. Please let me know if you are interested. Thx and I look forward to hearing back.
Loved this.
Would love to connect with you and the founder of Automate Ventures to discuss further in the New Year. We're launching professional services companies that follow the model you describe.
Will DM you!
Great Post / Tech Forecasting - So much going on here that can be extrapolated even further. It will be interesting to see which SaaS apps adopt new tech and evolve and which get passed by new AI based services. Also interested to see how the sales model evovles - will it too use AI? AI selling AI?Traditional SaaS (great productivity enhancer that I use in my company) seems a bit tired - there is less and less differentiation as all claim to do everything.
Love the concept! Wondering which party should be held accountable if errors occur - the startup that created the "work," the end user using it, the AI model itself, someone else? There can be insurance opportunity created around that too.
Great read. Law is also a great domain for this, because costs like this are so often passed onto the end client. The end client is happy because it probably cost less than what a lawyer would have cost. The law firm is happy because they can increase matter flow and focus on adding high-leverage strategic value, which is worth a much higher hourly rate.
very very true. bunch of companies taking advantage of this!!
There’s a new SaaS pricing fad making the rounds: outcome-based pricing. And in AI, particularly contact center AI and multi-use agent platforms, I don’t see a strong case for it.
Here’s why:
1. AI Is Replacing Labor, But Labor Was Never Priced on Outcomes
If AI agents are replacing contact center workers, then we should look at how BPOs (Business Process Outsourcing providers) price today. Spoiler: they don’t use outcome-based pricing. Despite decades of calls being handled, few BPOs operate on a “you only pay us if the issue is resolved” model. Why?
Attribution is messy. Is a ticket resolved if the customer stops responding?
Rework is common. What if they call back two days later for the same issue?
Resolution is subjective. In sales, does a transfer count? A nudge? Only a closed deal?
AI doesn’t change this — it just scales it. The fuzziness remains.
2. Outcome-Based Pricing Doesn’t Scale Across Use Cases
AI agents aren’t single-function tools. They're multi-use platforms. One customer uses it for L1 support. Another for routing. Another for sales qualification.
But “outcomes” mean vastly different things across these verticals:
In support, is it resolution? CSAT?
In sales, is it a closed deal? A qualified lead?
In back office ops, what even counts as a win?
Trying to design pricing logic that accounts for every permutation becomes a pricing ops nightmare. Worse, it slows down adoption because procurement teams don’t know how to forecast spend.
3. AI Infra Costs Are Falling – Buyers Want to Capture the Delta
Everyone knows where foundational model costs are headed: down. And fast.
If your AI agent becomes cheaper to run each quarter, do you pass those savings to customers? Or lock them into rigid outcome-based contracts where they can't realize efficiency gains?
In reality, outcome-based pricing often hides high costs behind complex contractual terms. It's less about customer alignment and more about risk-shifting — and in many cases, about masking an overpriced product with an attractive narrative.
4. Value-Based ≠ Outcome-Based — Don’t Confuse the Two
Here’s the critical distinction:
Value-based pricing means your price reflects the value you create.
Outcome-based pricing means you only get paid if a specific result happens.
You can be deeply aligned with customer value — and even justify premium pricing — without tying revenue to uncertain or subjective outcomes.
This is where many people get confused.
Reconciling with the Vertical SaaS Playbook
Outcome-based pricing can work—but only in very specific contexts. In vertical SaaS, where the solution is purpose-built to solve high-value problems within a tightly defined industry (think healthcare claims, supply chain optimization, or fraud detection), the logic holds.
Why?
Because in these verticals:
You own the entire problem space, not just one sliver like support or a sales nudge.
You can track concrete, repeatable results—claims processed, fraudulent transactions prevented, cost per shipment reduced.
The buyer isn’t experimenting with a toolkit—they’re solving a mission-critical problem with clear ROI.
✅ In these cases, outcomes can justify premium pricing—not as a conditional revenue share, but as a value-based premium for delivering predictable impact.
Now try applying that same logic to a general-purpose AI platform that spans dozens of use cases across dozens of industries. It unravels quickly. What counts as a resolved support case? What if the user ghosts? What if a sale is "nudged" but not closed?
Outcomes become fuzzy, inconsistent, and highly contingent on downstream events that the platform neither controls nor can fairly be judged by.
That’s why outcome-based pricing sounds attractive in theory—but breaks in practice when you try to scale it horizontally.
Final Word
AI vendors don’t need to bet on gimmicky pricing to prove value.
If you’re truly building transformational software, show me:
Clear tiers aligned with business use
Transparent usage models customers can predict and plan for
Optional add-ons for performance enhancement — not buried fees tied to arbitrary KPIs
Pricing should create trust, not confusion. Clarity scales. Contracts don’t.
There’s a new SaaS pricing fad making the rounds: outcome-based pricing. And in AI, particularly contact center AI and multi-use agent platforms, I don’t see a strong case for it.
Here’s why:
1. AI Is Replacing Labor, But Labor Was Never Priced on Outcomes
If AI agents are replacing contact center workers, then we should look at how BPOs (Business Process Outsourcing providers) price today. Spoiler: they don’t use outcome-based pricing. Despite decades of calls being handled, few BPOs operate on a “you only pay us if the issue is resolved” model. Why?
Attribution is messy. Is a ticket resolved if the customer stops responding?
Rework is common. What if they call back two days later for the same issue?
Resolution is subjective. In sales, does a transfer count? A nudge? Only a closed deal?
AI doesn’t change this — it just scales it. The fuzziness remains.
2. Outcome-Based Pricing Doesn’t Scale Across Use Cases
AI agents aren’t single-function tools. They're multi-use platforms. One customer uses it for L1 support. Another for routing. Another for sales qualification.
But “outcomes” mean vastly different things across these verticals:
In support, is it resolution? CSAT?
In sales, is it a closed deal? A qualified lead?
In back office ops, what even counts as a win?
Trying to design pricing logic that accounts for every permutation becomes a pricing ops nightmare. Worse, it slows down adoption because procurement teams don’t know how to forecast spend.
3. AI Infra Costs Are Falling – Buyers Want to Capture the Delta
Everyone knows where foundational model costs are headed: down. And fast.
If your AI agent becomes cheaper to run each quarter, do you pass those savings to customers? Or lock them into rigid outcome-based contracts where they can't realize efficiency gains?
In reality, outcome-based pricing often hides high costs behind complex contractual terms. It's less about customer alignment and more about risk-shifting — and in many cases, about masking an overpriced product with an attractive narrative.
4. Value-Based ≠ Outcome-Based — Don’t Confuse the Two
Here’s the critical distinction:
Value-based pricing means your price reflects the value you create.
Outcome-based pricing means you only get paid if a specific result happens.
You can be deeply aligned with customer value — and even justify premium pricing — without tying revenue to uncertain or subjective outcomes.
This is where many people get confused.
Reconciling with the Vertical SaaS Playbook
Outcome-based pricing can work—but only in very specific contexts. In vertical SaaS, where the solution is purpose-built to solve high-value problems within a tightly defined industry (think healthcare claims, supply chain optimization, or fraud detection), the logic holds.
Why?
Because in these verticals:
You own the entire problem space, not just one sliver like support or a sales nudge.
You can track concrete, repeatable results—claims processed, fraudulent transactions prevented, cost per shipment reduced.
The buyer isn’t experimenting with a toolkit—they’re solving a mission-critical problem with clear ROI.
✅ In these cases, outcomes can justify premium pricing—not as a conditional revenue share, but as a value-based premium for delivering predictable impact.
Now try applying that same logic to a general-purpose AI platform that spans dozens of use cases across dozens of industries. It unravels quickly. What counts as a resolved support case? What if the user ghosts? What if a sale is "nudged" but not closed?
Outcomes become fuzzy, inconsistent, and highly contingent on downstream events that the platform neither controls nor can fairly be judged by.
That’s why outcome-based pricing sounds attractive in theory—but breaks in practice when you try to scale it horizontally.
Final Word
AI vendors don’t need to bet on gimmicky pricing to prove value.
If you’re truly building transformational software, show me:
Clear tiers aligned with business use
Transparent usage models customers can predict and plan for
Optional add-ons for performance enhancement — not buried fees tied to arbitrary KPIs
Pricing should create trust, not confusion. Clarity scales. Contracts don’t.