>> from San Matteo. It's the Cube covering scaler. Innovation Day. Brought to You by Scaler >> Ron Jon Furry with the Cube. We're here for an innovation day at Scale ER's headquarters in San Mateo, California Profile in the hot startups, technology leaders and also value problems. Our next guest is Casey Clark, whose chief customer officer for scale of great to See You See >> you as well. >> Thanks for having us. >> Thanks for coming in. >> So what does it talk about the customer value proposition? Let's get right to it. Who are your customers? Who you guys targeting give some examples of what they're what they're doing with >> you. We sell primarily to engineering driven companies. So you know, the top dog is that the CTO you know, their pride born in the cloud or moving heavily towards the cloud they're using, you know, things like micro services communities may be starting to look at that server list. So really kind of forward thinking, engineering driven businesses or where we start with, you know, some of the companies that we work with, you know, CareerBuilder, scripts, networks, Discovery networks, a lot of kind of modern e commerce media B to B B to C types of sass businesses as well. >> I want it. I want to drill down that little bit later. But, you know, basically born the cloud that seems to be That's a big cloud. Native. Absolutely. All right, So you guys are startup. Siri's a funded, which is, you know, Silicon Valley terms. You guys were right out of the gate. Talk about the status of the product. Evolution of the value proposition stages. You guys are in market selling two customers actively. What's the status of the products? Where Where is it from a customer's standpoint? >> Sure, Yeah, we've got, you know, over 300 customers and so fairly mature in terms of, you know, product market status. We were very fortunate to land some very large customers that pushed us when we were, you know, seven. So on employees, maybe three or four years ago, and so that that four system mature very quickly. Large enterprises that had anyway, this one customers alando in Germany. They're one of the largest commerce businesses in Europe and they have 23 1,000 engineers. He's in the product on the way basis, and we landed them when it was seven employees, you know, three or four years ago. And so that four system insurance 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 their speed to truth there. Time to answer whenever they have issues. >> And so the so. The kind of back up the playbook was early on, when had seven folks and growing beta status was that kind of commercially available? When did it? When was the tipping point for commercially available wanted that >> 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'd expect with a kind of technical founder. He never thought the product was ready to go, but already had maybe a dozen or so kind of friends and family customers on DH. So I kind of came in and went on my network and started trying to figure out who are the right fit for this. Andi, we immediately found Eun attraction, the product just stood up and we started pushing. And so >> and you guys were tracking some good talent. Just looking. Valley Tech leaders are joining you guys, which is great sign when you got talent coming in on the customer side. Lots changed in four years. I'll see the edge of the network on 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 to cloud on premises. Seeing hybrid multi cloud is being validated. What is the current customer profile when you look at pure cloud versus on premise, 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, if he's kind of four categories e commerce, media BTB, sas B to see sass. You know, most of these companies are running. Some production were close in the cloud and probably majority or in the cloud. When we started this thing and it was only eight of us and Jesus has your were never talked about. We're seeing significant traction with azure and then specific regions. Southeast Asia G C. P. Is very hot. Sourcing a high demand there and then with the proliferation of micro services communities has absolutely taken off. I mean, I'll raise my hand and say I wasn't sure if it was going to communities and bases two years ago. I was say, I think Mason's going to want to bet the company on. Thank God we didn't do that. We want with communities on DH, you know? So we're seeing a lot more of kind of these distributed workloads. Distributed team development. >> Yeah, that's got a lot of head room now. The Cube Khan was just last week, so it's interesting kind of growth of that whole. Yet service measures right around the corner. Yeah, Micro Service is going to >> be a >> serviceman or data. >> Yeah, for sure it's been, and that's one of the big problems that we run in with logs that people just say that 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 observe ability and so you see, kind of a growth of some of the metrics companies like data dog infrastructure monitoring, phenomenal infrastructure, modern company. You've got lots of tracing companies come out and and really, they're coming out because there's just so many logs that's either too expensive, too hard, too slow to search through all that data. That's where your answers live on DH there, just extracting, summarizing value to try to kind of minimize the amount of search. You have to >> talk about the competition because you mentioned a few of them splunk ce out there as well, and there public a couple years ago and this different price point they get that. But what's why can't they scale to the level of you guys have because and how do you compare to them? Because, I mean, I know that is getting larger, but what's different about you guys visited the competition? >> Absolutely. This is one of the reasons why I joined the company. What excites me the most is I got to go talk to engineers and I could 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 in the company's history. I mean, really, before the company started to kind of main back and architectural decisions. One we don't use elastics search losing any sort of Cuban indexing, which is what you know. Almost every single logging tool use is on the back end. Keyword indexes. Elastic search are great for human legible words. Relatively stale lists where you're not looking through, you know, infinite numbers of high carnality kind of machine data. So we made an optimized decision to use no sequel databases Proprietary column in our database. So that's one aspect of things. How we process in store. The data is highly efficient. The other pieces is worse, asked business, But we're true. SAS were true multi tenant. And so when you put a query into the scaler, every CP corn every server is executing on just that quarry is very similar way. Google Search works. So not only do we get better performance, we get better costume better scalability across all of our customers, >> and you guys do sail to engineering led buyer, and you mentioned that a lot of sass companies that are a lot of time trying to come in and sell that market bump into people who want to build their own. Yeah, I don't need your help. I think I might get fired or it might make me look good. That seems to be a go to market dynamic or and or consumption peace. What's your response to that? How does that does 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 your baby's ugly right because your baby is beautiful in your eyes and so that is a hard conversation have. But that's why I kind of go back to what I was saying. If we just talk shop, we talk about, you know, the the engineering decisions around, you know, is that the right database? Is this the right architecture? And they think that they started nodding and nodding, nodding, And then we say, And the values are going to be X y and Z cost performance scale ability on dso when you kind of get them to understand that like Elastics, which is great for a lot of things. Product search Web search. Phenomenal, but log management, high card. Now that machine did. It's not what it's designed for. 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. It's hard. Well, let's put them back on mission critical business, You know, ensuring objectives. And we get, you know, service that this is all we do. Like you gonna have a couple people in there part time managing a long service. This is all we do. And so you get things like like tracing that were rolling out this quarter, you know, better cost optimization, better scalability. Things you would never get with an >> open. So the initial reaction might be to go in and sell on hey, cheaper solution. And is an economic buyer. Not really for these kinds of products, because you're dealing with engineers. Yeah. They want to talk shop first. That seems to be the playbook. >> Are artists is getting that first meeting and the 1st 1 is hard because that, you know, they're busy. Everybody's busy, They just wave you off. They ignore the email, the calls in and we get that. But once we get in, we have kind of this consultation, you know, conversation around. Why, why we made these technology decisions. They get it. >> Let's do a first meeting right now. People watching this video, What's the architectural advantages? Let's talk shop. Yeah, why, you guys? >> Yeah, absolutely so kind of too technical differentiators. And then three sort of benefits that come from those two technical choices. One is what I mentioned this proprietary, you know, columnar. No sequel database specifically designed for kind of high card in ality machine, right? There is no indexes that need to be backed up or tuned. You know, it's it's It's a massively parallel grab t its simplest form. So one pieces that database. The other piece is that architecture where we get, you know, one performance benefits of throwing every CP corn every several unjust trickery. Very someone way. Google Search works If I go say, How do I make a pizza and Google? It's not like it goes like Casey server in a data center in Alaska and runs for a bit. They're throwing a tonic and pure power every query. So there's the performance piece. There is the scale, ability piece. We have one huge massive pool of shared compute resource is And so you're logged, William. Khun, Spike. But relative to the capacity we have, it means nothing. Right? But all these other services, they're single tenant, you know, hosted services. You know, there's a capacity limit. And you a single customer. If you're going, you know, doubles. Well, it wasn't designed to handle that log falling, doubling. And then, you know, the last piece is the cost. There is a huge economies of scale shared services. We we run the system at a significantly lower cost than what anybody else can. And so you get, you know, cost, benefits, performance by defense and scale, ability >> and the life of the engineer. The buyer here. What if some of the day in the life use case pain in the butt so they have a mean its challenges. There's a dead Bob's is basically usually the people who do Dev ups are pretty hard core, and they they love it and they tend to love the engineering side of it. But what of the hassles with them? >> Yeah, Yeah, >> but you saw >> So you know, kind of going back to what we're all about were all about speed to truth, right? In kind of a modern environment where you're deploying everyday multiple times per day. Ah, lot of times there's no que es your point directly to the production, right? And you're kind of but 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 one of problem went wrong when something went wrong immediately, and they needed to be able to come up with a resolution. Right? There's always two things that we always talk about. Meantime, to restore it meantime, to resolution right there is. You know, maybe the saris are responsible for me. Time to restore. So they're in scaler. They get alert there, immediately diving through the logs to regret. Okay, it's this service. Either we need to restart it. Or how do we kind of just put a Band Aid on top? It's to make sure customers don't see it right. And then it gets kicked over to developer who wrote the code and say, Okay, now. Meantime, the 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 he mentioned the resolution piece. A lot of engineers that become operationalized prove your service, not operations. People just being called Deb ops is that they have to actually do this as an SL a basis when they do a lot of AP AP and only gets more complicated with service meshes right now with these micro services framework, because now you have service is being stood up and torn down and literally, without it, human intervention. So this notion of having a path of validation working with other services could be a pain in the butt time. >> Yeah, I mean, it's very difficult. We've, you know, with some of the large organizations we work with you worked with. They've tried to build their own service, mashes and they, you know, got into a massive conference room and try to write out a letter from services that are out there in the realities they can't figure out. There's no good way for them to map out like, who talks toe what? When and 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 their Jason sees, but they don't really know much further than that on 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 are in there, right? And so we provide a lot of value by giving you one place to look through all of >> that cube con. This has been a big topic because a lot of times just to be more hard core is that there could be downtime on the services They don't even know about >> it. Yeah. Yeah, That's exactly >> what discovering and visualizing that are surfacing is huge. Okay, what's the one thing that people should know about scaler that haven't talked you guys or know about? You guys should know about you guys 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, observe, ability, telemetry, monitoring, whatever you call your team Is court of the business right? Its core to moving faster, its core to providing a better user experience. And we have, you know, spent a significant amount of time building. You need technology to support your business is growth. Andi, I think you know you can look at the benefits I've talked about them cost performance, scalability. Right? But these airline well, with whatever you're looking at it, it's PML. If it's, you know, service up time. That's exactly what we provide. Is is a tool to help you give a better experience to your own customers. >> Casey. Thanks for spend the time. Is sharing that insight? Of course. We'd love speed the truth. It's our model to Cuba. Go to the events and try to get the data out there. We're here. The innovation dates scales Headquarters. I'm John for you. Thanks for watching