 Hello everyone, welcome to this CUBE conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. Got a great conversation with a startup of Chara, two founders, the brothers, co-founders, Ron Kana, CEO, co-founder of Chara, and Nikhil Kana, CTO, both co-founders and brothers of a four-year-old hot startup. Welcome to theCUBE. Thanks so much for having us, John. It's great to be here. So you guys have a company that's really hot right now in this market. It has tailwinds, our move to headwinds. People are optimized, that's the purpose of your company. Before we get into the conversation, and I want to ask, where did the idea come from? What's the origination story? Which role do you have to start this company? Yeah, well, you know, I like to say that both Nikhil and I were born in the cloud, we're brothers here from Seattle. We grew up around, you know, AWS and Azure. Our parents actually worked at Amazon and Microsoft, and my first job ever was actually at Windows Azure on the fabric team. And later on in my career, I moved over to AWS launching the SageMaker product and basically kind of saw the industry in my time in it really grow from renting infrastructure over the wire to renting much more robust managed services. And one of the key problems that even in 2017, 2018, when we were launching SageMaker, I saw my customer struggling with was cost, was optimizing, was figuring out, you know, how they're going to adopt and budget for these crazy expensive GPU machines and services. And, you know, seeing a lot of the gaps in the vendors that I have to deal with and send to my customers really lit a fire under my butt to start this company. And we have a fairly unique model where essentially we try and provide a free single replacement for all of these disparate tools from AWS and Azure like cost explorer and trusted advisor and simple pricing calculator, having one free, easy to use automation first tool that replaces that. And then layering on these additional financial options on top of that, which Nikhil can talk about because that's really where he came into the business and brought the other side of things I'll hand it over to you, Nikhil. Nikhil, hold on before you get there. Did you, was it your idea? Did you call him or he called you? You guys come together at the same time. We're both running into these same kind of issues. Yeah, like Ron was looting to my background sort of in finance and also in machine learning. In the past I've racked up some pretty large AWS bills unfortunately, and there was really a lack of ways to actually kind of get that under control and manage that. Particularly there are these things called commitments which you really only have a couple of options. You can either say, hey, I'm gonna use this machine for a year, I'm gonna use it for three years and there's really nothing in between that. So you're kind of getting, you're pretty much getting stuck putting like the full bill for the full price on demand instance otherwise. And I was comparing those options to the sorts of instruments I was seeing in sort of the financial world in the past I've interned by hedge fund called EESHA and sort of worked on the futures team there. And just seeing the sort of difference between the kinds of offerings that the nascent cloud space versus in a more sophisticated financial world really shows you just how much of a gap there is in terms of usability, in terms of just being able to actually have financial options that match the needs of customers. And that is just a pressing need and that's something, that's a gap that we saw in the market that we realized that we could help fill. Yeah, great, great, great intro. Thanks for that. And congratulations. Here's going all the startup action. The market right now is interesting. I want to get you guys take on this because we see the layoffs, the job cuts. And there are first generation SaaS comes like Dropbox. They're laying off people, which is a sign that, hey, that first gen cloud generation of SaaS is emerging out through the chasm here. And the new startups like the AI stuff's booming. You mentioned SageMaker. I remember when Swami launched that with Matt Wood, we covered it, grew really, really fast out of the gate. Now AI is booming, cloud native SaaS is kind of retooling, refactoring. Cost spend is where the knobs people are turning right now to generate profit. So a lot of focus on this cloud spend on AWS. What's the optimization reality going on? What are you guys seeing? Can you share the market dynamics right now? Yeah, absolutely. Well, the way that we see it, there's really only four ways to optimize in the cloud that kind of fall into two categories. There's usage optimization, trying to turn stuff off or use modern services like serverless to get more efficiency out of your application. That requires engineering effort. It requires changing your application. But there's really great benefits in terms of price performance and unit economics you get out of that. On the other side, you have rate optimization, which is really purchasing and managing commitments, which is a big part of what Archerra helps customers do, as well as negotiating things like an enterprise discount, which is another thing that Archerra really helps customers with. And those two things are really done at kind of the central top level and don't necessarily require engineers. That's really around financial commitments to the cloud over anywhere from one to five years. And so what we really saw early on with customers looking at what could we get in the longterm with usage optimization, but we know that's not going to happen overnight. So how do we plan for that? But right now lock in savings. How do we get immediate reductions in cost, quick wins, doing it in an automation first way, and then really set ourselves up correctly to plan for that longterm optimization. That's really kind of the tact that we've seen change in the market. Whereas before it was just grow, grow, grow, we'll think about efficiency and optimization later. Now that has really come home to roost and people are being very methodical about how they want to approach that. Yeah, I always say it's like leaving the lights on, turn them off, save some energy. This has been a big, big issue. What's the product you guys sell? Take us through what you guys do. What's the secret sauce? Yeah, absolutely. So as we were alluding to earlier, we provide a completely free platform that actually competes with the AWS native tools. If you've ever tried to use Amazon's native cost optimization tools or Azure's native cost optimization tools, there's like 20 different tools. None of them talk to each other. You need an engineer to go glue them together. And there's really no automation there. So our model is to actually fill those gaps with a unified free platform. Customers can sign up and save millions of dollars with us and never pay us a penny. It's a really unique model. And what we do on top of that is we provide net new ways to purchase. So for example, if you're a deep learning company and you have a AI workload that might take three months to train it, but you don't want to buy hour by hour, we offer this flexible commitment called a guaranteed reserved instance where you can commit for 30 days. And then at any point after those 30 days, you can click a button and send that commitment right back to us. So you can get that discount that's much higher, 30, 40% versus on demand, but have that flexibility to, when your deep learning workload fails, you can keep the thing around or if it actually converges and you get a great model out the other end, give the thing back. So that's really the core financial offering that we monetize through this free platform. Additionally, for enterprise customers, we do have things like unit economics tracking and customer reporting, but those are sort of flat SaaS upsell. I mean, the FinOps is a booming category. People are going to their bosses, getting yelled at, coming to the controller's office, executive management gets escalated. Clearly a great market. You mentioned deep learning though. I want to pivot to that because that's where there's a huge, I mean, it could be expensive. Nick, you could, hey, let's play with some generative AI and some of the foundational models. The next thing you know, boom, you're hit. Take us through how you guys help folks running workloads that are expensive like that in the cloud. Miquel, you want to take that? Yeah, so I guess on the outset, a lot of folks throw their engineers at these sorts of problems and then usually the engineers not really incentivized to care too much about the underlying cost of it in most cases. They just want to get a cool model out the other end, which I can totally empathize with as an engineer myself. I really care less about racking up the bill and more about getting something really cool. That is something awesome and useful at the end of the day. But the issue there is, there is that bill that's getting racked up. And a lot of the time, it's really not worth the engineer's sort of time or even if it's worth their time, it's not in their incentive to actually really care about that underlying cost versus the output at the other end. So what ends up being really helpful is a tool that can basically, without the engineer having to get involved without them having to disrupt their workload or take time away from where they're most valuable, which might just be for a deep learning workload, training that model, tuning the parameters and making sure that something useful is coming up the other end. The best solution is something where they're able to make those sorts of cost optimizations sort of on the rate you're paying for the actual machine level rather than saying, hey, this GPU's machine's too expensive, go use a cheaper one or go switch us to a different cloud or something like that where I'm getting a better deal maybe on a GPU. And that's really where we come in because we make it so someone who either that engineer or someone who's not even kind of in the weeds with that engineer working on that problem can come into our platform and actually get some of these short-term contracts that Iran was looking to earlier and then really be able to save on that workload month over month without having to make any sorts of changes. So we're sort of a low friction way that you can get savings without having to disrupt your engineers or otherwise take these valuable deep learning engineers away from the work that they shouldn't really be focusing most of their time on. Yeah, I'm glad you brought that up. That's why I was wanting to thread that reserve instant pricing value together. So essentially you can give people comfort and confidence that they're going to be within the parameters, right? Is that seems to be the value right there? Does that get that right? Yeah, it's very similar to a sort of an insurance offering where you have a certain amount of uncertainty in your future, right? In this case, the uncertainties around am I going to still be using this machine a year from now, three years from now? And ultimately a large part of what we're offering is not only that discount you're getting immediately but the peace of mind that you can actually turn that machine off without having to pay for this expensive GPU machine out into the indefinite future where you don't know what that's going to look like. And that's sort of how I view a lot of the value that we're bringing to customers. We're sort of giving them a peace of mind of their infrastructure in addition to those immediate savings. All right, so you got a free product, love that. How do you compete with Amazon and Microsoft's tools? They're free too. And what's the difference? Why are your tool, why is your product better than the free tools from Amazon and Microsoft? Yeah, absolutely. Well, I was actually at both AWS and at Azure and worked a lot trying to send customers to these tools and get their problem solved. And I'll tell you that each of these tools is essentially built by a different team that in many ways just aren't talking to each other. So to actually get the integrated value of say building a scenario trying to figure out, hey, what's my future spend going to be? Then tracking actuals against that and figuring out the best commitment strategy based on that forecast. That'll happen in five different places with spreadsheets tying them together and really no automation to speak of unless you build it yourself. And that's really something that from a workflow perspective we wanted to solve. But at the same value point and value is what you get over what you pay. So to compete with the value, you have to be free. But beyond that, we wanted to make it as low friction for customers as possible. So we do all of our sign up and billing through the cloud marketplaces. It almost looks like a native service. I think it's actually easier to turn our service on than to turn on trusted advisor just from a number of steps perspective in the AWS console. And I think another thing that is particularly true for larger customers is they often want a second set of eyes on these things. Like I wouldn't say they don't trust the data that Amazon and Microsoft are putting in front of them in terms of what they should commit to Amazon and Microsoft. But you can imagine someone who has some background in FPNA is not going to let the vendor tell them what to commit to the vendor. And so having a third party, that's really a fiduciary for the customer, which is how we kind of orchestrate and kind of stand up our offering here is really valuable in the sense that you know that the data is being presented in your best interests and all the alternatives are there for you to actually go and click through. That's awesome. Love the value purposes. I see some companies buying into the longterm contracts Amazon offers for known things, but then start saying, hey, I want to start doing innovative things. I need horsepower, like the large language models you mentioned earlier. I asked to ask you, what kind of customers that need you guys? If they're watching now, is there a certain size that have to be up and running? Is there a cloud spend threshold? How early in the cloud journey is a customer or prospect for you a customer? What's the makeup of a target audience? And when do they know they need you? Yeah, well, that's the beauty of our platform. We actually work with all sorts of customers from Fortune 500s with tens of millions of dollars of annual cloud spend, all the way to startup customers. We're just starting on the cloud and spending a few hundred bucks a month. So we really don't have any minimum or maximum. We generally find customers spending around a thousand bucks a month and up, we'll get enough savings for it to move the needle and for it to be kind of a meaningful win. That being said, we really do work with all customers. That's kind of the beauty of this free product-led model that we've created here. Beyond that, you know, I think from a vertical perspective, customers who have a large part of their cost of goods sold being on the cloud, I think we work with a number of CUBE alumni, including one of our reference customers, Valtix, you know, who obviously will use a lot of cloud services to construct their product that they're selling to end customers. That's a really big place where we tend to add value because not only are we reducing spend and OPEX, we're actually increasing margin and then providing additional tools like our premium SaaS offering to actually track that margin over time with custom dashboards. Yeah, and you're in the marketplace too, which I love the fact that marketplace, you get to selling opportunities from Amazon, pushing and the integrations right there. I got to ask you, what's the biggest thing people should be aware of right now as they look at this optimization? Everyone's talking about it. There is headwinds, Amazon's even admitting it now. We brought this up at re-invent last year. It's not so much that cloud's going away. It's just that people are rightsizing, right? And more services are coming out. So, you know, you leave a service open and it's just the meter's running, you know? We don't want that, right? So what's the state of the customer psychology right now if you had to kind of put a pin in it? What would it look like? Yeah, I think a lot of customers are just coming to this exercise of putting an optimization practice in place for the first time. And a lot of what we're seeing is kind of that classic shift left of the engineer, the DevOps person, the SRE getting cloud optimization tacked on as an additional task. And one of the key things that we're trying to do with customers is provide this tool with an automation-first approach to make sure that they don't have to go and spend so much engineering time to stand up sort of the easy win, low-hanging fruit kind of best practices, you know, like turning something off. I think the other thing that's important to note is that we see a lot of customers get stuck in analysis paralysis. Even when they decide, hey, we're going to go and optimize. And in the cloud, you're getting billed by the second. So every time you're not committed to a machine for a given hour, that's money that you're basically flushing down the toilet. So one of our key operational initiatives is to make sure as soon as the customer plugs in, we're able to find all that low-hanging fruit and within less than a week, actually automatically go and address it. So you're not burning money on demand, but you have flexibility to make those longer-term optimizations like replatforming or moving to managed services, et cetera. What are some of the customers you guys have? Give a taste of the kind of profile customers, what they're saying about you. What's the testimonials like? What are you hearing from them? What are some of the good things they're saying? Yeah, absolutely. So I can talk about one of our recent customers that we're just doing a case study with at Toonly, which is a machine learning company based out of Seattle here. And they've had incredibly variable spend, as you can imagine, with that machine learning workloads, being able to turn things on and off made a lot of sense for the cloud, but they were running it all on demand with some of the work that we were able to do around automating their purchasing strategy and then automatically buying back commitments as their workloads got turned off. We're able to basically get them from 0% coverage to 95% commitment coverage, even when their cloud spend was fluctuating from a few thousand dollars to tens of thousands of dollars and then back down in the span of a few months. So that's one of the big wins that we see with a lot of these customers in machine learning space. But even beyond that, we have large enterprise customers that will actually use us as part of their overall commitment strategy. When they're thinking about their three to five-year EDP plans, they're going to say, hey, how much do I need three-year all-up-front savings plans? And then how much do I need to cover with things like these archera GRIs? Where if I am missing my growth targets for the year, I'll at least be able to dial those down and not be over-committed. So we really see a range of use cases. And our goal is to really provide those tools and those new primitives like these GRIs to enable that entire range of customers. And that's the benefit of the cloud. That's the whole purpose of the cloud is to be elastic like that. That's absolutely the use case. Usage-based, dial it up when you need it, dial it down when you don't need it. Okay, talk about the impact of machine learning and AI to your business. I mean, you got to look at this as saying, this is a perfect fit for AI with all that data you're seeing out there from spend. Yeah, we're really seeing both sides of it right now. But first of all, just because of the kind of influx of AI in just general companies using it, training deep learning workloads, we're seeing a huge amount of demand for offerings like ours and the short-term commitments that we offer in order to kind of fuel these deep learning workloads and you really get cost reductions on them. And just more generally, in terms of our own use of AI, we've seen a lot of use in terms of, nothing crazy like large language models, but just simple forecasting techniques and stuff like that to be able to actually help customers plan for stuff like EDPs and try to do long-term projections based on our data that we actually have. Awesome. Final minute, we have left guys. Put a commercial in for what you're looking for. You guys hiring, what's the focus? What kind of customers you're looking for? Take a minute to put a plug in for what you guys are working on and what your goals are. Yeah, I would say anyone who is using AWS and in the next two weeks will be GA in Azure as well, should come and look at us as an alternative to the free tools that are probably very difficult to work with that you're getting from the Azure advisor or the cost explorer and trusted advisor from AWS. So there's literally zero risk. It's a free tool and you can plug it in on the marketplace just like any other app. So I would say if you're interested, if you want to stand up and get some insight into your spend, this is definitely a better option than trying to wrestle your way through cost explorer. On the other side, we're definitely hiring and we're hiring on the go-to-market side, primarily right now with folks who have experience in AWS and Azure sales, please reach out because we're definitely hiring across North America. All right, Ron Nakeel, thanks for coming on. Co-founders here of a chair of brothers, congratulations and love the story. Again, and when you have the headwinds, the optimization's got to be there. When there's a tailwind, you're going to have expansion. Either way, you're in a good spot. Cloud growth, thanks for coming on. Thanks, John. It's theCUBE conversation here at Palo Alto. I'm John Furrier, your host. Thanks for watching.