Conference talk by Mayank Pant (Stripe) on pricing AI products. It covers why traditional SaaS pricing struggles with unpredictable GPU and inference costs, and presents a framework for aligning charge metrics (credits, tokens, seats) with customer value while balancing predictable revenue with adoption. Includes real-world examples and margin-protecting guardrails, and a Stripe usage-based billing segment. Aimed at those launching or reworking an AI product's pricing model. # Video Identity Observations: "Mastering AI Pricing — Mayank Pant, Stripe" ## Speaker & Affiliation - Speaker introduces himself as **Mayank**, a **billing solution architect at Stripe**: > "I'm from Stripe and I'm the billing solution architect working in Stripe." ## Explicit Self-Description - **Creator claims (channel identity claims):** The talk is framed from Stripe's perspective, emphasizing Stripe's role as a billing infrastructure provider for AI companies: > "The most flexible and complete billing solution in market is we've got Stripe... 78% of AI companies are building on Stripe." - **Claimed scope of expertise:** Based on internal Stripe research and customer work: > "Based on the research that our Stripe team has done... over the last 2 years and based on our experience, we want to share some of the learnings." ## Intended Audience - **AI product/founding and billing teams** — people responsible for pricing AI products: - References customer pain points like "how many slides or how many decks did I get" for product users (Gamma example). - Audience questions indicate practitioners: questions on churn risk from pricing changes, ACV thresholds for discounts, how big AI labs price, and transaction-level recording. - The setting appears to be a **conference/event with a Stripe booth**: > "Please feel free to come to our booth, floor three." ## Claimed Scope of the Channel/Content - Educational content on **AI pricing strategy**, delivered via a five-step framework (define value, choose charge metric, pick pricing model, build guardrails, iterate). - Includes **Stripe product positioning** (Stripe Billing, Metronome, payments/tax/invoicing) — the talk doubles as product education and marketing. ## Demonstrated Presentation Patterns (Observable) - **Data-heavy, claim-cited style:** cites statistics throughout (e.g., "top 100 AI companies took 20 months to get to 20 million ARR versus... 65 months"; "hybrid pricing which was only 6% in 2024 is 41%"). - **Named-customer references:** repeatedly cites Anthropic, OpenAI, Lovable, Eleven Labs, Intercom as Stripe billing customers. - **Framework teaching:** structured step-by-step frameworks (four value types: automation, augmentation, enhanced service, improved results; three charge metrics: consumption-, workflow-, outcome-based). - **Interactive Q&A format:** closes with audience questions and direct referral to sales staff, indicating live event delivery rather than studio content. ## Claims vs. Demonstrated — Distinction - **Creator claims about the channel/brand:** Stripe as "most flexible and complete billing solution in market"; investment in AI billing "in the last two, three years." - **What the material demonstrates:** practitioner-level depth in AI monetization, comfort citing third-party-style stats, and a consultative-to-sales content arc (framework first, product pitch last). # Delivery Observation: "Mastering AI Pricing — Mayank Pant, Stripe" ## Tone & Register - **Conversational and advisory**: The speaker uses a coaching cadence — "So we've got a billing engine… we want to share some of the learnings with you guys." - **Rhetorical framing device**: Frequently poses questions and immediately answers them — "So what is the hybrid model?" … "A hybrid model will have a base fee and it will have a scaling fee." - **Vendor perspective stated openly**: Ties advice back to Stripe — "from our side, the most flexible and complete billing solution in market is we've got Stripe." ## Recurring Phrases & Verbal Habits - **"Right?" as a tag**: Used constantly as a listener-agreement marker. - "…but with AI it's completely a different thing, right?" - "…frequent pricing change is a signal of growth, right?" - **"So" as a pivot word**: Nearly every new section begins with "So" — "So now where are these companies moving, right?" - **"Under the hood"**: A repeated metaphor for backend abstraction, appearing at least five times: - "you can have your own models how you get to that" - "Under the hood, you are changing these these calls or permutations" - **"As I said" callbacks**: Re-anchors earlier points — "As I said, your first model is a hypothesis." - **Fillers**: Noticeable "uh" and repetitions ("the the", "we we") throughout, indicating a spoken, unscripted style. ## Teaching Habits & Structure - **Explicit numbered framework**: Announces "we've got a five-step framework" and walks through it sequentially (define value → charge metric → pricing model → guardrails → iterate). - **Personal anecdote as illustration**: Reuses the Gamma example repeatedly — "if I go into Gamma, I don't really mind how many API calls am I using. I want to see how many slides or how many decks." - **Customer-perspective roleplay**: Speaks from the buyer's voice to make points — "I do not know what those 1,000 API calls mean." - **Stat-heavy evidence**: Leans on percentages ("33%", "41%", "84%", "78% of AI companies are building on Stripe"). - **Pro tips**: Flags advice explicitly — "One of the pro tip is to translate value with credit." - **Repetition for emphasis**: States the core thesis multiple ways — "The first price that you put in is a hypothesis. It is not a commitment." ## Interactive Q&A Behavior - **Confirms before answering**: "That is true." / "No, no, they they use it under the hood." - **Honest about limits of knowledge**: On pricing discounts — "I'm not really sure on what is the threshold… that's more on the sales side, but do come over to our booth." - **Deflects to sales channel** for commercial specifics, twice referring questioners to the booth. ## Uncertainty Notes - The five-step structure, "right?" tag, and Gamma example are **stable, repeated patterns**. The "good, better, best, and then enterprise" tiering language appears once in Q&A; it illustrates the same credits argument but is not frequent enough to establish a broader habit on its own. # Video Position Record — "Mastering AI Pricing" (Mayank Pant, Stripe) ## Speaker - **Mayank Pant**, Billing Solution Architect at Stripe, presents the entire session; audience members ask questions (unidentified). ## Core Claims ### AI Economy Growth - AI companies grow **3x faster than traditional SaaS** (per Stripe data). - Top 100 AI companies reached **$20M ARR in 20 months** vs. **65 months** for top 100 SaaS companies. - Rapid growth drives global expansion, faster scaling, and pricing challenges. ### Why AI Pricing Is Hard - Traditional SaaS had stable **80–85% gross margins**; AI margins are lower and fluctuate with user load. - **Neither pure subscription nor pure usage-based models suffice** because: - **Margin risk from power users** — 5–10% of users can consume 80% of compute. - **Unpredictable external infrastructure costs**. - **Technical pricing overwhelms users** (e.g., users of Gamma care about decks produced, not API calls). - **Pricing can't keep up with product velocity** — premium features become standard within ~6 months. - Cited Stripe survey stats: - **33%** of AI-powered businesses cite unpredictable compute costs. - **41%** cite difficulty defining delivered value. - **84%** say product rollout outpaces pricing. ### Iteration as Competitive Advantage - "The first price that you put in is a hypothesis. It is not a commitment." - Hypergrowth companies (100%+ YoY) changed pricing 3+ times in 2 years — **49%** of hypergrowth vs. **22%** of low-growth companies; he frames low growth as correlated with static product. - **Hybrid pricing** rose from **6% (2024) to 41%**; **56%** of AI company leaders use hybrid. Seat/subscription-based pricing declining. - Companies cited as using hybrid pricing on Stripe: **Intercom, Lovable, Eleven Labs, OpenAI, Anthropic**. - SaaS companies adding LLM features plan to move to hybrid because SaaS pricing "erodes margins." ## Five-Step Pricing Framework ### 1. Define Your Value - Base it on **customer perception**, not product mechanics (Gamma decks example). - **53%** of hypergrowth companies offer clear value-based pricing vs. **26%** of low-growth peers. - Four value types: - **Automation** — time saved → cost saved. - **Augmentation** — same headcount, higher quality/output. - **Enhanced service** — access to proprietary data/software (e.g., Stripe's fraud detection from payment volume). - **Improved results** — direct bottom-line impact (e.g., Intercom pricing per ticket solved without human involvement). ### 2. Define the Charge Metric - **Consumption-based** (API calls) — easy to implement, aligns to provider cost, hard to align to value. - **Workflow-based** (images generated, documents summarized) — aligns to the product. - **Outcome-based** (candidates hired, qualified leads) — aligns to customer ROI; harder to attribute value. - **Pro tip:** Translate value via **credits** — bundle features behind a credit currency so customers see ROI, not technical units. ### 3. Pick the Pricing Model - Subscription → predictable revenue, committed relationship, but power users burn margins. - Usage → scales with customer, protects margins, but customers hesitate to experiment. - **Hybrid** = base fee (relationship) + scaling fee (experimentation); "not alienating any category of users." ### 4. Build Guardrails - "Build fair pricing, but then do not surprise." - **Usage caps**, **automated notifications** at 50/70/90% thresholds, **top-ups** (manual/auto), **pause options**, **rate limiting** against runaway code. ### 5. Iterate - Prioritize speed over waiting for the "right" price. - Talk to churned and upgrading customers; run **A/B tests on pricing**. - **Realign credits under the hood** as features evolve while keeping customer-facing structure stable. ## Infrastructure / Stripe Positioning - Infrastructure choice determines iteration speed; multi-month pricing changes "not worth it." - Claims Stripe is "the most flexible and complete billing solution in market," backing it with **78% of AI companies building on Stripe**. - Stripe Billing (subscription/usage/hybrid) + **Metronome** for enterprise contracts (minimum commitments, pre-commitments, overage pricing) + payments, tax, invoicing, revenue recognition. ## Q&A Positions ### Q1: Frequent pricing changes → customer frustration/churn? - Response: Use **credits as abstraction** — customer sees a stable 100-credit structure while underlying feature-to-credit mappings change as features commoditize. Also **grandfathering** for existing users when new versions launch. ### Q2: At what ACV/volume do Stripe discounts kick in? - Answer deferred: "more on the sales side"; mechanism explained (use as much/little of the platform as wanted; volume-based sales engagement), but **threshold not specified** — directed to booth sales staff. ### Q3: Big AI labs seem to have constant pricing, not iterative — how does that fit? - Pant: "They use it under the hood." Labs keep 4-style plan tiers (good/better/best/enterprise) with features moving between plans; customer-facing plan structure stays constant, features change, abstracted by credits. He qualifies: "Price might change again." ### Q4: Does the platform record every billable transaction? - Yes — usage events are sent to Stripe with the applicable pricing model (tiered, dynamic, dimension-based); Stripe rates and prices them and provides full invoice-level reporting. ## Unresolved Tensions (as expressed) - Pant simultaneously claims AI labs "use iterative pricing under the hood" while acknowledging customer-facing prices remain largely constant — the disagreement with the questioner is reconciled via the credits abstraction rather than conceded. - The discount-threshold question remains unanswered in the session. # Speaker Attribution Record — Mastering AI Pricing **Video ID:** `01a15ca9-8019-7f0d-9e50-ac571d52a99b` **Video Title:** Mastering AI Pricing — Mayank Pant, Stripe ## Speakers & Roles | Speaker | Role | Evidence | |---|---|---| | **Mayank Pant** | **Creator / Presenter** — Billing Solution Architect at Stripe | Self-introduction: "I'm Mayank. I'm from Stripe and I'm the billing solution architect working in Stripe." | | **Unnamed audience members** | **Audience / Q&A participants** (≥4 distinct questioners) | Ask questions on pricing-change churn risk, ACV/discount thresholds, big-lab pricing models, and transaction recording. No names given in transcript. | | **Applause** | Audience (non-verbal) | Transcript end: "[applause]" | ## Cited Third-Party Companies / Claims - Companies said to build/bill on Stripe: **Intercom, Lovable, Eleven Labs, OpenAI, Anthropic (referred to as "Tropic" once), Gamma** (Gamma cited as a customer-use example only). - **Stripe data claims** (attributed to speaker, citing Stripe research over "the last 2 years"): - Top 100 AI companies reached $20M ARR in 20 months vs. 65 months for top SaaS companies (3x faster). - 33% cite unpredictable compute costs; 41% cite difficulty defining value; 84% say pricing lags product rollout. - Hypergrowth companies changed pricing 3+ times in 2 years (49%) vs. low growth (22%). - Hybrid pricing grew from 6% (2024) to 41%; 56% of AI company leaders use hybrid; 78% of AI companies build on Stripe. - 53% of hypergrowth companies offer clear value-based pricing vs. 26% of low-growth peers. ## Promotional Segments ⚠️ - **Closing segment** promotes Stripe's own products: **Stripe Billing** (subscription/usage/hybrid), **Metronome** (enterprise contracts), and the broader Stripe platform (payments, tax, invoicing, revenue recognition). Speaker claims Stripe is "the most flexible and complete billing solution in market." - Repeated referrals to the **Stripe booth ("floor three")** for sales follow-up. ## Provenance & Availability - Statistics presented are **vendor-claimed Stripe research data**; no external published sources are cited in the transcript. - No curated metadata was supplied (no channel, upload date, or description). - **Comment availability:** not stated in source — unknown. - Q&A discount-threshold question was explicitly **not answered** ("more on the sales side"); deferred to booth staff. \>\> Hello. Hi. I'm Mayank. I'm from Stripe and I'm the billing solution architect working in Stripe. And today we want to talk about AI pricing. So we've got a billing engine and a lot of nowadays a lot of AI companies come to us talk about how they want to do billing, how we can help them with their AI pricing. So based on the research that our Stripe team has done on in the over the last 2 years and based on our experience, we want to share some of the learnings with you guys. So as we all know, AI economy is growing at a record pace. They they are growing three x faster than traditional SaaS and based on uh Stripe data, this is what we see, right? The top 100 AI companies took 20 months to get to 20 million ARR versus the top 100 SaaS companies which took 65 months. So basically we are growing at three x faster rate than what we've seen before. And though this is great, this is exciting, but this is bringing new challenges. The speed of movement is so great that the companies are now going global faster. They are scaling up faster and pricing that pricing AI is becoming incredibly challenging because of the speed that we see. So we know that earlier it was traditional SaaS pricing. Basically we had 80 85% gross margins. They were not changing, right? But with AI it's completely a different thing, right? The margins are low and the margins get impacted based on how many users are using your system. So neither pure subscription nor pure usage-based model offer us a complete answer, right? And the simple reason is we have margin risk from power users. 5 to 10% of your users can use 80% of your compute. Uh external costs are unpredictable, that is your infrastructure pricing, right? Which can really hurt your margins. Technical pricing may overwhelm users. What we mean by that is for a product company talking in terms of tokens, API calls might seem very uh normal, but people on the other side do not understand that technical pricing. For me, for example, if I go into Gamma, I don't really mind how many API calls am I using. I want to see how many slides or how many decks did I uh was I able to get out of the price that I'm paying, right? And pricing is not able to keep up with the product velocity. What might be a premium feature today in 6 months maybe a standard feature across. So are you able to keep up with that pricing? Uh so what we see at 33% of our AI-powered business site unpredictable compute costs as their concern. 41% say that defining the value that is being delivered is is a concern, right? And 84% of them talk about that we are rolling out products faster, but we are pricing is not keeping up with it. So that is why what we say in iteration is a competitive advantage. The first price that you put in is is a hypothesis. It is not a commitment because you are bringing in new features every week, every month and with those new features and maybe the old premium features are becoming standard. So the pricing has to evolve with the feature that you put in, right? So frequent pricing change is a signal of growth, right? You build the infrast- infrastructure, you put your pricing in the infrastructure that can iterate that allows you to iterate rapidly, right? So this chart is one of my favorite. It shows that hypergrowth companies which are giving 100% plus year on year growth, they are changing their pricing three plus times in the last 2 years. They are not standing with their pricing. 49% is, you know, high growth. They are they've changed three plus and the low growth companies are only 22% who have changed it. And that means that those low growth companies have a static product, right? And that is not where you want to be in today's age. So now where are these companies moving, right? If you look at it, hybrid pricing which was only 6% in 2024 is 41%. Outcome-based pricing is 5%, right? And where it has it picked up from? Se- seat-based pricing, SaaS pricing, subscription pricing is all declining because the models are changing. So as I said, hybrid pricing seven x increase and now 56% of AI company leaders are using hybrid pricing. Like we talked to all the all the companies such as Intercom, Lovable, Eleven Labs, OpenAI and Tropic, they are all building on Stripe, billing on Stripe and all of them are using hybrid pricing and I've also talked with companies who were SaaS companies so far which used to uh help customer build workflows and they were in SaaS pricing, but now as soon as they bring uh LLM AI into their product, then they are also planning to move on to hybrid pricing because SaaS pricing then starts eroding their margins, right? So to if if this is where we are going, then how do we start thinking about pricing? How do we come to a right price? How do we define our price? And how then do we iterate it? So the we've got a five-step uh a five-step framework for this and the step one of that framework is you define your value, right? So what type of value can my product provide? But not what your product is doing, but what the what the customer perceives your product to do. Like again, like I took an example of Gamma. For me as a customer, I need that product to give me my presentation, my decks. I don't care underneath how many API calls is it making, where is it going. My uh for the for the customer it is the quality of the deck and the relevancy of that deck deck. So 53% of hypergrowth companies have offered clear value-based pricing that the customer understands versus just 26% of low growth peers. And the way we look at it is uh there are four broad frameworks on which the first kind of these companies are the company that are providing automation. Like like I uh as a company I'm helping them to save time and as a customer what they are looking at it is, okay, with the time saved I'm saving on the cost, right? Second is augmentation. The number of people remain the same, but the quality that they come out with is much better. Like they can produce images better. Probably for a campaign provider they can build in better campaigns more quicker. So they uh those kind of augmentation the company looks at it is, okay, I have the same number of people, but they are more efficient, they can deliver more value, right? Third is the enhanced service. Maybe it gives you an access to a proprietary software or some new uh data set that you wouldn't have access otherwise, right? For example, if you look at it look at Stripe and our payment infrastructure, we can uh do fraud recognition much better because of the volume that flow through us, right? And finally, the fourth kind of one is the which gives you an improved results. Like for example, company like Intercom who says, I will price you based on the number of tickets that I solved without human need. So that is impacting the direct bottom line. So once we realize how we are helping the customers, how our customer perceive it, that is when we know the value and we know the price that we can command in the market. Once we've understood this is the value that we are providing, then you define your charge metric. Like what is the billable unit that is best representing my value, right? And then you build features into a currency that customers understand. For example, is it consumption-based? Like for an infrastructure company, consumption-based might be the right one. Like how many API calls did I help you to make, right? And this aligns to the cost of the company that is providing that service. Second could be the workflow-based. Like how many images that was I able to generate? How many documents was I able to summarize? And this aligns to the product, and the third is the outcome-based as I said. How many business results did I generate? So if if I am helping the company to hire people, how many uh candidate that I have put forward? How many candidates were hired out of those candidates resulting in saving time? Or how many qualified leads did I generate? Right? So, this aligns to your customer ROI. So, now that you've understood the value that you're providing and you've decided your charge metric against it, we will we will see how they change it, right? Like if you move from consumption-based to outcome-based, it is definitely easier to implement consumption-based is much easier to implement. But, it is harder to align to value. Like if I if some company tells me and says, "I allowed you 1,000 API calls. I do not know what those 1,000 API calls mean. I want to see how many decks I generated as in my previous example." But, if you go from outcome-based to consumption-based, it is easier to sell. Like I can definitely go to a customer and say, "I will increase your outcome. I'll increase your uh candidate hired, but to attribute value is difficult." So, that is where the balance has to be made and data is required to satisfy and to make your case. One of the pro tip is to translate value with credit. Like bundle the features into credits. Say that "I am giving you 100 credits for the month. And then beneath under the hood of the credits, you can have your own models how you get to that." But, that helps the customer to understand, "Okay, 100 credits and it will translate to uh this particular ROI." Once you've done that, then that is where you pick your pricing model. Now, is it a subscription fee? The usefulness of subscription fee or SaaS fee is predictable revenue. It gives you a committed customer relationship. Right? How And then the second could be the usage fee, which scales with customer with protects margins. But, the downside as I said is for the SaaS fee, uh your power users might burn your margins. And for a usage fee, uh the customer might be hesitant in experimenting with your product. Like going full deep because they do not know what kind of a invoice will they uh expend on this. So, pure subscription, pure usage-based was a thing of the past. Now, as we saw in the previous slides as well, everybody is moving into the hybrid model. So, what is the hybrid model? A hybrid model will have a base fee and it will have a scaling fee. The base fee basically helps you to establish a relationship with your customer. And the scaling fee or the usage fee on top of it will allow them to experiment as much as they want and then to pay for the value that they derive out of your platform. So, you are not alienating any of the user any category of the users. Once you've established your uh pricing, then you build in the guardrails because a wrong bill can erode a lot of customer trust. You might be doing great for the three, four, five months, but if the on the sixth month your bill goes wrong, bill goes very high, then that those customers go and then you work so hard to retain those customers, but then they leave you. So, what safety feature do you consider? Like you are giving them flexible pricing. Now, you're building guardrails around it, right? As the design principle is fair it's simple here. Build fair pricing, but then do not surprise. So, what what we what we advise people to do is put in usage caps. Like I paid $20 and then I've been given 100 credits. I say that after 100 credits, I've built in a usage cap. You either pay more to go ahead or we will stop and you wait for the next month. So, that the it is the customer that has remained in control of their uh usages. Right? You build an automated notification. You tell them when they've used 50%, 70%, 90% of uh of their allocated limits because this is building trust with the customer. We are not trying to uh cheat them. We are there trying to inform them that this is what you've used. These are the uh thing that you can go ahead with now. You can top up. You can do a manual top up. You can do an auto top up. Or you can pause and then start the next month when fresh credits gets allocated to you. And then you set rate limiting so that no wrong code is burning through the limits, right? So, this will protect you and your customers both. Once you've done these step one to four, then the step five is you iterate, right? You keep iterating. 84% agree that fast pricing adaptation is a key competitive advantage. As I said, your first model is a hypothesis and you keep building on that pricing as your product keeps evolving. You prioritize speed. You do not wait for the right price right price point and wait for a year before you put it. You put a price point there which you think is the right price point and then you iterate. You talk to your customers. Those guys who churn, you talk to them and ask them why they are churning. Is it a product-market fit? Which is then you work on your product. But, is it high price? Then you work on your pricing. Right? If they upgrade, you ask the same questions. You run AB tests on pricing to find the optimum point, but you prioritize speed and you keep iterating. And then you continuously realign to your value. Now, realigning to the value means as you are rolling out new features, you've already given them hybrid pricing. You've already put in number of credits. Under the hood, you can keep changing what those credits means. Do do those 100 credit that you've given them means five API calls of a certain category, 10 image generation of a certain category? Whatever. So, under the hood you can keep changing them. But, you constantly realign the value. So, this is how all the companies have been thinking about pricing. This is how we've been uh trying to help them. But, what is most important in all this is what kind of an infrastructure do you have for your billing, for your pricing that is helping you to do this? If every change costs you three months, four months, and a lot of engineering effort, then it is not worth it. So, that is why it the infrastructure you choose determine how fast you can iterate. And uh from our side, the most flexible and complete billing solution in market is we've got Stripe and it is not us that is saying it. 78% of AI companies are building on Stripe, which is a testimony in itself. Most of the AI companies that you see here are building on Stripe like I said, Anthropic, OpenAI, Lovable, Eleven Labs, Intercom. They're all launching their billing and pricing. You might have associated Stripe with payments only, but in the last two, three years we've uh we've invested a lot into AI billing. So, we've got Stripe Stripe billing which allows you to go with subscription pricing, usage pricing, hybrid pricing. And as these companies are so quickly within 10 to 15 months now starting with the retail PLG motion going to enterprises, we've got Metronome that allows you to build all the all the dif- difficult and complicated contracts with the enterprises having minimum commitments, uh pre-commitments, uh overage prices. So, we've got that and we've got the whole platform that allows you to do uh payments, tax, invoicing, revenue recognition on this AI pricing. So, yeah, this is where we are right now helping all our AI companies in the market. So, yeah, this this is me happy for any questions that you have. Yes, please. This is super interesting. With the advice around changing your pricing model fairly often, one of the risks is that that can cause frustration with customers and then they might churn because of the sort of the instability and it's less predictable for them what their cost structure is going to be. So, have you got any advice around like how to Yeah, so so what what people are doing and what we also enable them to do is they sell credits, right? But, what do those credits mean? Like you got in let's say today in January you had one feature that was your premium feature. It was not replicated anywhere in the market and you had assigned five credits for that feature under the hood, right? In six months, that feature becomes standard feature. The pricing had dropped and in the meanwhile you brought in new features because you are also competing in the market, right? So, for the customer, he just see 100 credits. Under the hood, you are changing these these calls or permutations like what feature means how many limits. So, the it is remain transparent for the customer, it remains fair for the customer, but based on your product feature, you can keep changing pricing. Plus, we can we also have features where it allows you to grandfather pricing like you bring in a new version. I who have been using it still keeps getting it on the same price, but the new users have to pay more. Yes, please. At what ACV do you start to get better rates and more of an enterprise management structure? Like, how how much payment volume do I have to do before I can start to get better discounts? So, again, more on the sales side, but it depends on the volume like your payment volume and your billing volume. Basically, we like we we have a platform. We allow you to use as much of a platform as you want or as little of it as you want, right? And then depending on the volume that is coming and we we bring in all together. Let's say you are using uh payment and billing together but not using tax, that is fine. You bring your payment and billing and then our sales people start getting into it. The sticker price, of course, is there for everybody to see. I'm not really sure on what is the threshold that you know, start bringing the price down because that's more on the sales side, but do come over to our uh booth. We've got some sales people there. They'll be able to give you a better answer to this. I've explained you the mechanism, but the threshold they'll be able to answer. Yes, please. Your thoughts on the presentation was really interesting. Thank you. And one thing I'm wondering, how does for example the pricing model of like big AI labs relate to this iterative pricing? I feel like AI labs often have like multiple plans which have like very constant pricing. And then like the roll out of features always happen first on the expensive plans and then they trickle down to like That is true. the lower pricing. And I don't really see like they don't really use like iterative pricing in their pricing. No, no, they they use it under the hood. So, for them, for you, like even if you go to 11 Labs, you'll see four kind of plans there, right? Uh let me call it good, better, best, and then enterprise, right? You will just go for you go for the best, right? They will keep adding features or removing features from one plan to the other. The pricing will They will try to remain constant with the pricing for you, but inside it the features will keep moving and that is why they want to give it give you credits or we advise that you give credit to the customer so that the all these features doesn't start interacting with pricing. Okay, like the customer facing prices stay stay constant but linked to which plan? Yes, they they let me say not the pricing the customer facing plan remains constant, right? Price might change again, but the features will keep changing because it has been abstracted by credits on the top. Okay. Right, that's it. Yeah, you had a question, sorry. Um Yes, sir. My question is does your platform provide like a way to record every transaction in your system like \>\> Every transaction. Uh that costs something and the user we can track it. Yeah, so what what we do is like if the prompted like you make a prompt, uh the customer is ingesting some uh calls, you will send those calls to us. You will tell us like we have all kind of pricing in there. Tiered pricing, dynamic pricing, uh dimension-based pricing. You tell us what kind of pricing does it go and then it it comes in, we are able to rate it and price it for you. And then you can get the whole report on exactly why why the invoice is what it is so we can give you all the detailed pricing. Right? Thank you. All right, if you have any other question, then please feel free to come to our booth, floor three, right? Thank you so much. \>\> \[applause\]