Thanks for coming to my talk. I'm Shitej. I'm one of the co-founders at ORB, and I'm going to be talking about how to think about pricing. Maybe top-level takeaway from this talk is that pricing is a deep, complicated topic. We're going to cover some examples. We're going to cover some tactical advice.

But in general, the way you should think about pricing is pricing is a form of friction for your product. And sometimes that friction can be applied for a very good reason. Sometimes that friction can just prevent people from using your product. And so you really have to think about the value your product is delivering, as well as the audience you're delivering it to, and the way that you're building and innovating in your product to start really thinking about pricing.

Just a quick intro to ORB. ORB is a usage-based billing infrastructure company, so we help companies at lots of layers of the stack think about pricing and billing. Nowadays, a lot of our conversations first center around monetization and pricing as a practice, and then we get into the weeds of how do we actually bill for this stuff.

So maybe an intro tweet to start. This is Amjad. He's the CEO and founder of Replit. Replit is one of ORB's customers. And this is a tweet actually pretty recent, starting to think about how Replit agent should price itself. And so a pretty short tweet, but a lot of complexity actually packed in here.

Really starting to think about, as you have Replit, which is a very popular agent that does programming, is starting to build full-stack web apps. How should a company like Replit price their agent? Should it be like a super simple, every time the agent checkpoints, it charges you a fixed amount?

Or should it start aligning with the complexity of the change it made? And as you can start thinking through this question, you have to think through considerations like who is Replit built for, right? What are the costs that Replit incurs? But also just the user experience of if I get charged some amount at the end of something that this agent has done without me really having control over what the agent did other than the initial prompt, is that going to be surprising?

Is that not going to be surprising, right? So these considerations apply particularly to agents because the work that the agent is doing is fairly opaque or can be opaque, but they actually apply at every layer of the stack, including the kind of infrastructure layer of the AI stack. So monetization as a whole is changing.

Maybe let's start with some kind of traditional principles in pricing and how pricing has always been thought about, right? So we're maybe coming from way back in the day, you know, you're selling a fixed license, you go to like Fry's Electronics and buy your TurboTax license. And then of course, now you have seat-based pricing subscription, maybe you're paying $30 a seat, all the way to you have this usage-based pricing, which is very dynamic.

And then maybe you can consider outcome-based pricing as a further evolution of that. So in traditional pricing, you have a few key principles. You want to make sure that the pricing is simple so that users can actually understand it. They can grok your pricing. They know how they're going to pay for your tool.

You want to produce some friction because it determines the value of your product. Like willingness to pay is a very real signal. And then you want to protect your margins, right? Traditionally, software margins have been very healthy. You know, a good software margin looks like 80%. Obviously, as you start getting closer to the infrastructure layer and as you start moving into the world of AI, these margins are much, much more variable, not necessarily unhealthy, but there's a lot more degenerate workloads that you can incur with an agent.

So now starting to think about AI native pricing, predictability matters a lot, right? I think especially when you're selling to more mature companies that need to budget and think about their, you know, cost profile using your tool, they need to be able to predict how that cost is going to scale, not just on an individual developer, how that cost might scale, but just generally throughout the company's buying process.

AI is very early. So speed and just showing the value of your product and people being able to play around and experiment matters a lot. Oftentimes, at least in this space, it's not like people are coming to you buying a very established product. It's kind of your burden of proof to say, you know, here's how our agent works.

Just go try it and then start thinking about pricing. So maybe the friction point and where it is has changed. And then I was just saying, um, cogs are variable, but, but not only are they higher, they're also changing very, very quickly. I'm sure everyone in this room seen, okay, great.

Um, you know, open AI, uh, cut, cut model costs by a third overnight. Uh, that has a pretty material impact on like potentially the underlying cogs of many of these agentic tools. So, um, the three key principles that, that I think matter here are like really thinking about the audience, who your product is optimized for, what the value delivery mechanism looks like your margin structure.

Um, and, and I say margin structure and not actually margins, because again, the underlying costs are changing very, very rapidly. So you want to think about what are the axes of scaling rather than what is the literal margin on day one, especially if you're an earlier product. And then you want to give yourself the flexibility to experiment over time.

This comes with a lot of complexity, but I think it's very important because you're just not going to get it right the first time. Um, so, so these are kind of the principles to keep in mind, maybe going through one by one. Um, the, the way to kind of think about pricing for your audiences, um, you know, what is their buying journey and what are they coming to you to, to buy and what value are they looking to get out of it?

Right. Uh, if you're like, I, I, we, we talked to lots of people who are like, Oh, you know, service now is pricing is so silly. You go to their pricing page and it says like contact sales. It's like, well, that's, that's a very, very different audience that they're selling to where they're probably cross selling some, some existing contracts.

That is a big enterprise commit. They want you to talk to sales because they really want to tailor the value to their sales motion. Right. And then on the other hand, companies that are selling to individual developers, there's not a procurement team. There's no purchasing process. They want you to click, try it and start using the product immediately.

So, so, um, don't just think about the kind of point of entry, but think about who's behind that at the company. Uh, is it going to be a cross functional decision? Is it something that someone can just insert into, you know, as, as a vendor on day one, or is there going to be more of a process around it?

And then also, uh, start to kind of think about, uh, what users are seeking as quickly as possible. If they're seeking a proof point, then perhaps you want to be able to give them that proof point before you insert pricing into the equation. Here's, here's an example, again, using replet, um, couple of things to note here.

Uh, you'll see that there's one, a lot of tiers and pricing is actually quite transparent. Obviously it's a free tier, right? Indicating that they want you to see the value without having to do a ton of work. Think about the monetization. Um, and, uh, one thing that's kind subtle is oftentimes pricing and packaging really starts to, uh, dictate what sort of use case they are imagining you to use, right?

Like, um, you know, what models you have access to, how many agent checkpoints you have access to included, how many seats you might have included. Uh, it kind of starts positioning your product. Is it a multiplayer product? Is it something that people are going to be using daily or is it something that you're going to be using, you know, maybe like five times a month, uh, the way you package it really determines the incentives that you're pushing onto your users and obviously controlling for the costs that you might pay on the backend.

Here's another example from Unify, which is, I think, uh, uh, agent agent to company, but it's like targeted at revenue and go to market teams. One, you'll see the, like the price point is a lot higher. Um, I want you to like forget whether it's monthly or annually or any of that, just like you come on this page, the price point is higher that communicates something psychologically.

And then they're like second duty years are custom, right. Um, again, I, and, and maybe the third thing to point out here is that 50,000 credits, 200,000 credits, 600,000 credits. I actually don't know what that means, but that seems like a large number that communicates something about the workflows they expect to participate in and like what that might mean for how you use Unify.

Right. Um, of course, like it maybe goes instead, but the set of logos you put on your pricing page is also very important. Um, are you tailoring it towards, okay, we, we service the best developer brands or we service the like fortune 100 enterprises. Um, and so pricing is, is not just the price point, but, but of course also the, the whole experience of what someone sees when they land on this page.

Um, let's, let's talk a little bit about margins. Um, you know, as, as, uh, the, the underlying architecture of your product changes, of course, your, your margins are going to change. These inputs are changing very quickly. Um, one, one, one interesting example here is, uh, you, you, as much as possible, you want to think about, uh, the differentiation of your product also in R and D innovation and try to pass that on to your users as basically pricing leverage.

Uh, a good example of this is like Cloudflare has built their infrastructure on and their workers infrastructure on these like isolates, right? So, so they're kind of lightweight compute machines. And so they can charge for CPU milliseconds. And so if someone is building like an agentic app on Cloudflare and it makes like a, you know, call out to open AI or Anthropic, you're not getting charged for that because Cloudflare is just charging you for CPU milliseconds instead of wall time.

I think that's a really good example of them, you know, taking an architectural bet, um, which, which might be good for many, many reasons, but it's a really good fit for building AI agents or generally AI software on top of Cloudflare. So that's, I see that as an example of like passing down technical innovation to the end user.

Um, and then the other thing to consider about your margins is you don't have to protect them at all costs. You just have to think about what are the extreme edge cases and what are you doing to like prevent those outcomes, right? So if you have these potentially really degenerate workloads, uh, how do you incentivize reasonable usage?

Do you put in rate limits or guardrails? Um, you don't have to kind of linearly necessarily scale costs with usage. You, you can protect your margins potentially in, in different ways. Um, this is an example from Jasper. I think this, this blog post and pricing has changed since, since I pulled this example, but you know, Jasper realized like, Hey, we're, we're targeted toward marketing teams.

Marketing teams don't want to count credits. Like when they're, you know, basically writing marketing copy. And so, uh, they, they talk a little bit in this blog post about how they've architected around being able to like switch between models under the hood seamlessly. You don't have to choose the right model for the job.

And as a result of that, they went unlimited on, uh, each of the three tiers. So you're, you're no longer counting credits as you're using Jasper. Now, uh, this kind of highlights the core value prop of the product, which is they want to be a really critical part of that workflow.

And they don't want you to have to like pick and choose when you use Jasper because you might have credits or might run out of credits. So again, kind of trying to take, is there like an architectural, uh, advantage you have and how can you pass that on to your pricing model?

Finally, I want to talk about flexibility. Um, you know, you, you all, as, as you're working in your teams, uh, you're constantly improving your product. You're putting a lot of R and D into it. Um, one thing that is used to be true, especially in the seat based world is like, you'd have R and D over here shipping a ton, um, increasing the product value.

And then you'd have like a once a year, okay, we're changing our seat price from $20 to $25. Um, one advantage you have in the AI world is like, uh, the value is much closer to the end users. It's not necessarily like a $20 a seat thing. It's like someone is literally seeing, okay, I'm, I'm taking this action.

This agent is giving me this output. Okay. It's getting better. It's getting, it's better, it's getting better. So I think not only do you as a business have a responsibility to incrementally evolve your price as, uh, you know, you invest R and D, but I think it's, it's honestly a lot more understandable for customers why that's happening because the, the monetization is much closer to the value of the product.

Um, so, so, uh, you know, obviously in orbs customer base, but in general, we are seeing people, uh, make many, many more price changes, not just once a year or twice a year, but in fact, some customers making price changes, uh, two, three times a month. Um, and again, there's, there's caveats that come with that.

Uh, you have to think about, you know, what, what is that complexity that introduces in the, in the mental model of your end users, how are you going to manage change? Um, and a kind of good internal dynamic example of this is, uh, if your go to market team is, you know, commissioned and, and they're selling these big enterprise contracts and your pricing is changing, or you're going to like a pay as you go model, you need to think about like, what does that mean for your sales team?

What does that mean for the incentives that they have in selling your product? classic example of this is a lot of usage based companies now commission their sales reps, not on the initial contract land, but on the expansion of that account over time. And then, okay, second order effect.

How do you think about customer success versus sales as an organization? And how do you organize that internally? Right? So, so repricing can have lots of impacts, not just on your end user, but also in the internal dynamics of your org. Um, finally, the thing I'll say is, uh, as you're iterating, you, you really want to, as much as possible, be able to simulate the impact that this pricing change is going to have on your users.

In particular, I think, uh, you want to be able to simulate, uh, different usage patterns. So if you think about your users and cohorts, um, some might be using it for very different use cases. What is that revenue mix going to look like? What is your top, uh, revenue generating customer going to, how are they going to change with a new pricing model?

You want to be able to do that in a, in a data informed way, especially if you're running at a, at a reasonable scale. Here's some predictions I have as, as we're wrapping up for AI agent pricing. I think, um, you know, the, the price wars will continue. We're seeing agents get cheaper and cheaper.

Um, and, and, you know, I think you're, you, we should expect people to try to give effectively unlimited plans. I think there'll still be caps. I think there'll still be guardrails, but I think, um, we, we will move to a world where, uh, you know, people just want these agents embedded in every workflow.

So we'll try to, try to converge to effectively unlimited. Um, I think outcome-based pricing is going to get more real. We are seeing some companies, uh, do that already, but the, the, the hard part about outcome based pricing is defining what the actual SLA is like, what, what is the outcome and how do you measure it?

And does it make sense to the end user? Um, I think like, we're going to see a lot of clear definition of success language and contracts and like people really trying to get, uh, precise about what the outcome is that they're charging for. And then, um, I think there's going to be a lot more real-time visibility, spend management, uh, balance alerts.

I think the kind of workflow around pricing and monetization is going to get, uh, more and more sophisticated. You think about like, you know, obviously in, in database queries, you have like query planners. I think it's quite possible that, you know, you, you prompt an agent, it tells you like, here's my estimate for how much, uh, how many credits I'm going to use, or, Hey, here's three options for, um, how I might execute, how I might plan this workload.

One's going to take 10,000 credits. The other is going to take, you know, 4,000 credits. I think that customer experience of like being able to control the workflow around the spend is likely to get much, much more sophisticated, um, this year. So, uh, just, just final note, um, we have a whole ebook on how to price your AI agent that goes into much more tactical advice, talks about specific, you know, threshold billing, fraud, prepaid pricing models.

Um, if you want to check that out, that is at this link. Thank you. Thank you. Thank you. Thank you. Thank you. I'll see you next time.