 From San Mateo, it's theCUBE, covering Scalar Innovation Day, brought to you by Scalar. Hi everyone, I'm John Furrier with theCUBE. We are here for an innovation day at Scalar's headquarters in San Mateo, California. Profiling the hot startups, technology leaders, and also value propositions. Our next guest is Casey Clark, who's the Chief Customer Officer of Scalar. Great to see you. Great to see you as well. Thanks for having us. Thanks for coming in. So what is it? Talk about the customer value proposition. Let's get right to it. Who are your customers? Who are you guys targeting? Give some examples of what they're doing with you guys. We sell primarily to engineering-driven companies. So the top dog is the CTO. They're probably born in the cloud or moving heavily towards the cloud. They're using things like microservices, Kubernetes, maybe starting to look at serverless. So really kind of forward-thinking engineering-driven businesses or where we start with some of the companies that we work with, career builder, scripts networks, discovery networks, a lot of kind of modern e-commerce media, B2B, B2C types of SaaS businesses as well. I want to drill down on that a little bit later, but basically born in the cloud, it seems to be that's a big cloud native. Yep, absolutely. All right, so you guys are a startup, series A funded, which is in Silicon Valley terms, you guys are right out of the gate. Talk about the status of the product evolution of the value proposition stages. You guys are in market, selling to customers actively. What's the status of the product? Where is it from a customer standpoint? Sure, yeah, we've got over 300 customers and so fairly mature in terms of product and market status. We were very fortunate to land some very large customers that pushed us when we were seven so on employees maybe three or four years ago. And so that forced us to mature very quickly. Large enterprises that had, you know, we have this one customer is Alondo in Germany, they're one of the largest e-commerce businesses in Europe. And they have two, 3000 engineers using the product on a weekly basis and we landed them when it was seven employees, you know, three or four years ago. And so that forced us to mature. And so it was very easy for us to go to other enterprises and say, yeah, we can work with you and here's the proof points on how we've helped this business mature, how they've improved kind of their speed to truth, their time to answer whenever they have issues. And so the, just to kind of back up, the playbook was early on, we had seven folks and growing beta status. Was that kind of commercially available? When did it, when was the tipping point for commercially available? When did that click in? That probably tipped when I joined about a little under four years ago. I had to convince Steve that he was ready to sell this product, right, as you would expect with a kind of technical founder. He never thought the product was ready to go, but they already had maybe a dozen or so kind of friends and family customers. And so I kind of came in and went to my network and started trying to figure out who were the right fits for this. And we immediately found, you know, traction, the product just stood up and we started pushing in. So. And you guys are attracting some good talent. I know some Silicon Valley tech leaders are joining you guys. Which is a great sign when you've got talent coming in. On the customer side, a lot's changed in four years. Obviously the edge of the network. Digital transformation has been a punchline, been kind of a cliche, but now I think it's more real as people see the power of scale with the cloud. On-premise is seeing hybrid, multi-cloud is being validated. What is the current customer profile when you look at pure cloud versus on-premise? Are you guys seeing different traction points? Can you share a little bit of color on that? Yeah. So I talked a little bit about our ideal customer profile being, you know, of these kind of four categories, e-commerce, media, B2B SaaS, B2C SaaS. You know, most of these companies are running some production workloads in the cloud and probably the majority are in the cloud. When we started this thing, you know, it was only AWS and GCP and Azure were never talked about. We're seeing significant traction with Azure and in specific regions, Southeast Asia, GCP is very hot. So we're seeing a high demand there. And then with the proliferation of microservices, Kubernetes has absolutely taken off. I mean, I'll raise my hand and say, I wasn't sure if it was going to be Kubernetes and Mesos two years ago. I was saying, oh, I think Mesos is going to be the one to bet the company on. Thank God we didn't do that when we went with Kubernetes. And, you know, so we're seeing a lot more of kind of these distributed workloads, distributed team development. Yeah, and that's got a lot of headroom now. The, you know, KubeCon was just last week, so it was interesting kind of the growth of that whole, yeah, service mesh is right around the corner. Microservice is going to have- Yeah, service mesh is out there. It's going to throw out more data. Yeah, for sure. And that's one of the big problems that we run in with logs is that people just say, they're too voluminous. It's either too hard to search through it. It's too expensive. We don't know what to deal with it. And so they're trying to find other ways to kind of get observability. And so you see kind of the growth of some of the metrics companies like data dog infrastructure monitoring, phenomenal infrastructure monitoring company. You've got lots of tracing companies come out. And really they're coming out because there's just so many logs. It's either too expensive, too hard, too slow to search through all that data. That's where your answers live. And they're just extracting summarizing value to try to kind of minimize the amount of search that you have to do. Talk about the competition. I mean, because as you mentioned, a few of them splunks out there as well. And they were public a couple of years ago. And there's different price points. I get that. But why can't they scale to the level that you guys have? And how do you compare to them? Because I mean, I know data's getting larger, but what's different about you guys vis-a-vis the competition? Yeah, yeah, absolutely. This is one of the reasons why I joined the company what excites me the most is I get to go talk to engineers and I can just talk shop. I don't really talk about the business value quite as much. We get there at some point, obviously. But we made some very key decisions early on with the company's history. I mean, really before the company started. Two kind of main backend architectural decisions. One, we don't use Elasticsearch, Lucene, any sort of keyword indexing, which is what almost every single logging tool uses on the backend. Keyword indexes, Elasticsearch, are great for human legible words, relatively stale lists, where you're not looking through infinite numbers of high cardinality, kind of machine data. So we made an optimized decision to use a no-sequel database, as proprietary columnar database. So that's one aspect of things, how we process and store the data is highly efficient. The other piece is we're a SaaS business, but we're true SaaS, we're true multi-tenant. And so when you put a query into a scaler, every CPU core and every server is executing on just that query. It's very similar to the way Google search works. So not only do we get better performance, we get better cost and better scalability across all of our customers. And you guys do sell to an engineering led buyer and you mentioned that. A lot of SaaS companies that are, a lot of companies that are trying to come in and sell that market, bump into people who want to build their own. Like I don't need your help because I might get fired or it might make me look good. That seems to be a go to market dynamic and or consumption piece. What's your response to that? How does that, how's that fared for you guys? Engineers want to engineer, whether it's the right thing or not, right? And so that is always hard. And I can't come in and tell you, your baby's ugly, right? Cause your baby is beautiful in your eyes. And so that is a hard conversation to have. But that's why I kind of go back to what I was saying is we just talk shop. We talk about the engineering decisions around, is that the right database? Is this the right architecture? And they start nodding and nodding and nodding. And then we say, and the values are gonna be X, Y, and Z, cost performance, scalability. And so when you kind of get them to understand that like elastic search is great for a lot of things, product search, web search, phenomenal. But log management, high cardinality, machine data, it's not what it's designed for. And okay, okay, okay. And then we start to get them to come around and say, not only can you reallocate. I mean, we talked about how getting talent is hard. Well, let's put them back on, you know, mission critical business, you know, engineering objectives. And we get, you know, a service that this is all we do. Like you're going to have a couple people in their part-time managing and logging service. This is all we do. And so you get things like tracing that we're rolling out this quarter, you know, better cost optimization, better scalability, things that you would never get with an open source. So the initial reaction might be to go in and sell on, hey, it's cheaper solution and there's an economic buyer. Not really for these kinds of products because you're dealing with engineers. They want to talk shop first. That seems to be the playbook. Yeah, our artist is getting that first meeting. Getting the first meeting is hard because they're busy. Everybody's busy, they just wave you off, they ignore the email, the calls, and we get that. But once we get in and we have kind of this consultative conversation around why we made these technology decisions, they get it. So let's do a first meeting right now. People watching this video. What's the architectural advantages? Let's talk shop. Why you guys? Yeah, absolutely. So kind of two technical differentiators and then three sort of benefits that come from those two technical choices. One is what I mentioned, this proprietary columnar NoSQL database specifically designed for kind of high cardinality machine data, right? There's no indexes that need to be backed up or tuned. It's massively parallel to grep, it's simplest form. So one piece is that database. The other piece is that architecture where we get one performance benefits of throwing every CP corn, every server on just your query. Very simple way Google search works. If I go say, how do I make a pizza in Google? It's not like it goes to like a KC server in a data center in Alaska and runs for a bit. They're throwing a ton of compute power at every query. So there's the performance piece. There's the scalability piece. We have one huge massive pool of shared compute resources. And so your log volume can spike but relative to the capacity we have, it means nothing, right? But all these other services that are single tenant hosted services, there's a capacity limit and you, a single customer, if your log volume doubles, well, it wasn't designed to handle that log volume doubling. And then the last piece is the cost. There's a huge economies of scale. Shared services, we run the system at a significantly lower cost than what anybody else can. And so you get cost benefits, performance benefits, and scalability benefits. And the life of the engineer that you're a buyer here, what is some of the day in the life use case pain in the butts that they have? I mean, it's the challenges. There's a lot of DevOps is basically, usually the people who do DevOps are pretty hardcore and they love it and they tend to love the engineering side of it. But what are the hassles for them? Yeah, yeah, yeah. That you saw. So, you know, kind of going back to what we're all about, speed to truth, right? And in kind of a modern environment where you're deploying every day, multiple times per day, a lot of times there's no QA, you're deploying directly to production, right? And your kind of butt is on the line when that code goes live. You need to be able to kind of get speed to truth as quickly as possible, right? You need to be able to identify when a problem went wrong or when something went wrong immediately and then you need to be able to come up with a resolution, right? There's always two things that we always talk about. Meantime to restore and meantime to resolution, right? There is, you know, maybe the SREs are responsible for meantime to restore. So they're in scaler, they get an alert, they're immediately diving through the logs to figure out, okay, it's this service that we need to restart it or how do we kind of just put a bandaid on top of it to make sure our customers don't see it, right? And then it gets kicked over to the developer who wrote the code and say, okay, now meantime to resolution. How long until we figure out what went wrong and how do we fix it to make sure it doesn't happen again? And that's where we help. You know, it's interesting case you mentioned the resolution piece. A lot of engineers that become operationalized through your service, not operations people just being called DevOps, is that they have to actually do this as an SLA basis when they do a lot of API to API. It only gets more complicated with service meshes, right? Oh, these microservices frameworks because now you have services being stood up and torn down literally without human intervention. So this notion of having a path of validation working with other services could be a pain in the butt big time. Yeah, I mean, it's very difficult. We've, you know, with some of the large organizations we work with, they've tried to build their own service meshes and they've, you know, got into a massive conference room and try to write out a little different services that are out there. And the reality is they can't figure out there's no good way for them to map out, like who talks to what, when, and you know, each little service knows like, okay, well here's the downstream effects and they kind of know what's next to them. They know they're adjacencies but they don't really know much further than that. And the nice thing about, you know, logs and all kind of the voluminous data that is in there which makes it very difficult to manage but the answers are in there, right? And so we provide a lot of value by giving you one place to look through all of the data. At KubeCon, this has been a big topic because a lot of times just to be more hardcore there could be downtime on the services they don't even know about it. Yeah, yeah, that's exactly right. So discovering and visualizing that or surfacing is huge. Okay, what's the one thing that people should know about Scaler that haven't talked to you guys or know about you guys, should know about you guys and consider? Yeah, I mean, I think the reality is everybody's trying to move as quickly as possible and there is a better way. You know, observability, telemetry, monitoring, whatever you call your team is core to the business, right? It's core to moving faster. It's core to providing a better user experience. And we have spent a significant amount of time building unique technology to support your business's growth. And I think you can look at the benefits. I've talked about them cost performance, scalability, right? But these align well with whatever you're looking at. If it's P&L, if it's service up time, that's exactly what we provide is a tool to help you give a better experience to your own customers. Casey, thanks for spending the time and sharing that insight. Of course, we love speed to truth. It's our motto at the Kube. We go to the events and try to get the data out there. We're here at the Innovation Day at Scaleless Headquarters. I'm John Furrier. Thanks for watching.