 I'm John Furrier here in Palo Alto Studios for CUBE Conversation. I'm here with David Linkus, the CEO of ScienceLogic. David, thanks for coming in, good to see you. Great to be here, John. So thanks for coming in. You came in from DC, that's really your headquarters in ScienceLogic. You guys are having a good business run right now. You're self-funded early on, now you get a venture back. Take a minute to explain how you guys got started. What does the company do? So this is the classic story of entrepreneurship. We started in the garage. Myself and a couple of co-founders believed that IT operations management was broken. And it was broken because a lot of the industry had really focused on having silos of data. The silos of data, the network, the application, the security, the storage, now cloud, containers, and every technology had its own data silo of manageability. We believed that was intrinsically wrong to understand how the service that combined all these different applications and technologies was behaving. We wanted a service view. So we brought it all together, kicked off, really the first seven years we bootstrapped the company. The first year and a half we coded, got the product to market, it grew very quickly, got to the Inc. 500 a couple of times. And then we attracted a lot of financing options. We had about 250 companies approach us. We never made one outbound call. And fortunately, we had some really great and strong investors, NEA, then Intel Capital. And three and a half years ago, our last round of financing was with Goldman Sachs. And they've really been a great catalyst to help us continue our growth over the last five years. I think we've grown about 540% on the revenue side. So it's been an exciting time. Well, congratulations. Always a good successor to be a hot deal when you don't have to make any calls, they come to you. And that's good, that's part of growth. But I got to ask you, what year did you start the company? What 2003? So it's not obvious then, I'll just ask you as a visionary, but I mean, it's now people now know IT operations is broken and cloud highlights in a big way. It's not like the lights get turned on and the cockroaches are running around. But web services were still booming at that time. You start to see the beginning of the whole web services movement. You guys saw this early. Now it's well recognized that IT operations can be automated away. And cloud certainly has an automation vibe to it. AI has been a big part of the AI operations. Is this kind of where you guys started with that vision? How was it? Was the original vision kind of where it is today? Take us through kind of like what you saw and what's happening today. So thematically we have this next wave of the compute architecture, cloud compute architecture, edge computing, where the way you manage that kind of infrastructure is different than the classic client server. There are different needs, different requirements. And that thematically has led with the change of infrastructure. Applications are changing and applications now are more infrastructure aware. When we started the company, usually applications sat on one system or a cluster of systems and they weren't widely distributed. So now that the application profile is changing, the architects are changing the microservices, that really puts huge strain on our industry. The industry, the total addressable market is about $25 billion a year annual spend on tools. John, if you can imagine that. So 25 billion a year spent, it's going through an amazing, I would say tectonic shift because why? Infrastructure's shifting. And as more people move workloads to the cloud into what I would call ephemeral workloads where they're moving around, that causes all kinds of pressure on the systems of record to manage that so that you understand what is happening at this moment in time. Where is it? What cloud is it running on? How's the application performing? And you really need to tie the application to the infrastructure real time. I want to get your thoughts on this. And I interviewed a CIO this past week for a big company. I won't say the names that we haven't published a video yet, but he told me candidly. He said, look at, we outsourced everything and we outsourced our way into oblivion. And what he meant by that was is that the core competency of IT and he referenced the book Nick Carr, IT doesn't matter which kind of was true, but wasn't true. Now, IT is a competitive advantage and essentially they had this anemic IT department that was outsourced and they lost their competitive advantage. So he's like, the reinvestment in IT is more than ever now because of cloud, because of these new environments. So I kind of believe that to be true. I'm sure you'd do too. But the reaction really is, is he got a lot of legacy vendors that were dictating how to do things. Yes. I'm IBM, I'm Oracle. You got to do it this way and your kind of constrained, IT was constrained by that. Now you got to be much more agile. You have workloads that are dynamic, provisioning, orchestration, this is a whole new dynamic. What's the impact to the IT buyer, the IT environment with this new model, this new modern dynamic, new modern era? All I, when you think about CIOs and CEOs, the pressure that they have to be cloud first. Cloud first is such a strong, at the board level, there's pressure. The adoption of cloud now is happening faster and more rapidly than the adoption of virtualization. Maybe it's doubling in the speed and the time warp. But what that means is that most CIOs are dealing with as many as nine to 11 clouds, not one. You have a federation of clouds, private clouds, public clouds, software as a service clouds. And that's your IT landscape. So it's changing so quickly that you have to think of it in a more federated approach. That means that the way you used to manage your private systems and now your public systems are really different. And you've got to look at them more holistically because often they're communicating with one another in hybrid architectures. So that's really the heart at our mission to provide the context of how all the services you're trying to deliver as a CIO are behaving. What's their availability? What's the risk of the service having a problem? And knowing that real time is ultimately what you want to do with your cloud first strategy but you need the right tooling operationally to affect that kind of outcome for your team. So what's the core problem that you guys are solving? Because obviously there's a lot of complexity now it's new environments. So I still got the baggages and legacy environments. Is it monitoring that you're solving? I mean, I guess what's the core problem is my question that you guys are solving. If you have the kind of finish that, the core problem is blank. The core problem is visibility. The holy grail is application to infrastructure. And the problem is that's becoming so complicated because everything's moving around. The more abstraction layers where it's a container which is abstracted on top of a virtual machine which is on top of a bare metal server, SD-WAN is an abstraction on top of an MPLS network. So you have all of these layers that get from a software defined perspective that get abstracted away from the actual equipment that it's running on. Well when that happens, where is the problem? Because it's moving around. The problem isn't in one place. So that application to infrastructure awareness, it's almost like one of the things that we've looked at in the world of Facebook. You've got a lot of relationships. You've got videos, you've got friends. You've got all these different connections that are constantly moving around with data streams. What we do as a company is pull all these different data streams from the technologies themselves, from the cloud providers, from the application layer. Pull it together in a data hub that we can then understand how they all relate to one another so that you can really, truly understand service impact. And that is the crux of the problem that most companies are dealing with now. You've got to fight with your legacy because you still have that and it's not going away tomorrow. So you've got to make sure you're good at that. You've also got cloud, the cloud first initiative. And then you've got in between systems that are using both. That's really where we play. We're really good at the legacy. We're good at cloud and connecting the two together. And that is a really tough space because most legacy providers really didn't get good with managing hyperactive ephemeral cloud estates. The guys who started over the last five years building tools to manage the cloud are really good at cloud, but they don't cover legacy. They're not going to cover a NetApp or hyperconverge typically. So we combine the both legacy and cloud together in one management system, monitoring management, Paranym, and then there's an automation engine where we actually proactively remediate problems, real time. So the three together is where algorithmic operations, AIOps comes together. Dave, I want to dig into the offering, but before we get there, I want to get your thoughts on two trends. One is multi-cloud. We certainly, we've seen a lot of hybrid cloud discussion, but now the big hub hub is multi-cloud. And the other one is AI operations. So, you know, I've been saying on theCUBE, everyone who's in IT operations is screwed, going to get automated away by AI. I'm kind of, it's kind of tongue in cheek, but it's kind of a reality is that those old business models that were based upon certain service levels are going to be done in software. Now you've got multi-cloud. So first question is, what does multi-cloud define the definition that you have for that? I mean, what does it mean? What is multi-cloud? In our world, multi-cloud is most large organizations use more than one cloud. And half of that is driven by what cloud is best to operate a particular application profile. Amazon's really good at a lot of application profiles, but Azure might be better at certain Microsoft profiles. And then Google has profiles and IBM Watson has profiles, depending upon what you're trying to do with the application, where it was born, how it's living, how it's been refactored. You're going to use one cloud or the other, but most customers that we see have many clouds. There really isn't one cloud management escape when you're using, vendors are still reasonably proprietary in the public hyperscalers. And some are better than others. And some are better, it depends on the use case. So we try to bring all that together so that you're not looking at four panels, you're looking at one. Actually you've made it easy that one dashboard. Okay, AI operations, this is a hot trend. A lot of venture caps are funding companies that have AI ops in it. Machine learning obviously is booming. There's no doubt software automation is coming, it's seeing it everywhere. What does that mean? What is the definition of AI operations? I mean, I'm bombastic in saying it's going to industry sector is going to crumble. I'm kind of thinking it will, but it'll shift. But what is the impact to IT operations with AI? And what is AI ops? We like to think of it as a life cycle. So when you look at the life cycle of operations you have at the beginning of the life cycle provisioning. So when we think about algorithmic, there's many different layers of automation, machine learning, cognitive learning, and you're going to use different parts of algorithmic operations for different parts of the life cycle. So at the very beginning, you're going to connect generally to a provisioning system so you know what's been provision or deprovision so we can automatically align a manageability template because nobody can be on a keyboard anymore, John. This has to be all machine to machine. So once then it gets provisioned, then there's the run-operate part. And how do you learn from the normal operating conditions that you're looking for, the anomalies that you would look for to detect things aren't behaving appropriately? And then once you understand those anomalies and the patterns, you can remediate them proactively adding resources, decreasing resources, changing configurations, those are the things that kind of that last tier. And then that final tier, when there is a problem, if there is a problem, you've got to then raise a ticket, you've got to then work through the incident management of that ticket. So there's another multi-step layer of automation to the incident management orchestration layer of solving problems, closing out a ticket. So we have so many different layers across that life cycle that we plug into, most of which are native to our core platform. And your secret sauce is managing all the workloads that are moving around really fast. So to complicate that even further, you got a lot of stuff moving around, the track at all. So I want to get, I love what you said about, you don't have to type in a keyboard anymore, but essentially I'll translate that from what I heard was command line interface of CLI has been the primary mechanism for dealing with either network and or storage, which is moving packets from here to there and moving storage from now to then, storing stuff. So CLI is moving to a programmable model. This is the big takeaway. So I totally think this is the mega trend. The command line interface mode of operation is moving to programmable, which hits your run and operate. Correct. This is the mega trend, your thoughts. It is, and that's one of the layers of complication because instead of a CLI, it's an API. And it's usually a RESTful API or a Graph API. Those APIs are very different in construct. And instead of talking to one device, that one device is virtualized into a hundred or a thousand. And so with one API call, you actually create a thousand devices versus one device and understanding how one system is behaving like a CLI would be to one system, right? So that is the layer of complication where when we make an API call, we break it up into hundreds of things that then we track and understand the tendency of what is the multi-tenant nature of that, what is the organization, what is the service view for all these little components that are part of one API call. And that abstraction layer makes it really difficult for the enterprise because the one thing about our API economy right now, there's no standard. Every vendor chooses their own formats for their products. And in some cases, many formats for products in a product family. So that layer of complexity, John, is what we're really solving for. The customer doesn't have to worry about that. We take care of that for them, but you're right. The API has become the CLI and it's just a level of complexity beyond what most enterprises are wanting to deal with themselves. That's why they bring us in. And it's so important too because the data's in the API. That's right. That's key and Cloud's got orchestration challenges, state and stateless applications. All right, let's get into science logic's offering. So what do you guys provide to customers? Talk about the product. How do you guys deliver it? Is it software? Is it cloud? Is it services and appliance? Take us through the offering. What's the key secret sauce? How do people buy and use your product? So our products delivered as a service. You can use it in the cloud. We deliver it as a service in our cloud, but we also provided if customers are using Amazon or IBM or Google or Microsoft, they can put our product, same code base, same product. They subscribe to it. It's a subscription license model. So it's a pay as you go and you pay for the number of devices that are under management. Typically, there are some customers, whether it's in the government, financial services or international locations where they might want to deploy our product on-premise. So we offer the same mode either in the cloud or on-premise, but most customers now are choosing to deploy the product in the cloud. And that is a really easy. It's easy to see. And that's good for you guys. It's great for us because there's consistency of operations we can keep everything up to date. And most customers want technology delivered as a service. They just want it to work. They want it to solve the business problem and do it easily, efficiently, even better, solve really complex problems in an easy form. Give some customer examples or benefits or anecdotal stories around customers that have used your service, that extracted benefits and value out of it. And second part of that question is when does someone know they need your product? What are the smoke signals? When is something breaking or is it just pain? When do they know to call you guys? So first one is customer examples or stories and then how does someone know who's watching us? Hey, I might need these guys. There are four segments that we cover. We have customers all over the world. There's enterprise customers. This is really a product for large enterprise, Fortune 1000 companies. So Clorox would be a customer. Qsatellite would be a customer. Cisco Systems out here in the Valley is a customer. Dell EMC, so it depends on what problem we're trying to solve for customer. So large IT deployments, basically. Very large, multinational, big networks, hundreds of thousands of devices, tens of thousands of devices is where those companies have immense complexity. Lots of heterogeneous technology that comes together to deliver a service. They need a really robust solution to manage that proactively. So enterprise customers, service providers. So a lot of managed service providers, infrastructure as a service providers, telcos, they all use this. I think we have about 60% of the infrastructure as a service provider to use our product to deliver managed services to their customers. And then the federal government all over the world, we have governments, customers around the world. I think right now about 70,000 organizations use our product every day and it's fairly evenly split, Amia and Isha Pak and then the US is our biggest market. You know, it's interesting you mentioned heterogeneous and I just kind of like smile because you mentioned client server earlier. You know, every wave has their inflection point and I think what's going on with cloud and I'd love to get your reaction is that, you know, cloud where it's winning is it's a scale out large scale pool of resources. We've got what's going on with Amazon others is that you don't need to know what servers they have, just get more servers so you're scaling out. Yes. But now you need to have heterogeneous components, it's not just X86, you could have a GPU, you have other stuff, AI going on. So heterogeneous is different now but it's still the same game and still complex that needs to be abstracted away. Is this kind of the key area that you're writing on? Is that right? What's your thoughts about that concept? Well, to a large degree, John, the cloud providers have really provided a layer for you to not have to worry about that. But we've seen customers actually with hyper converged environments that they build in house and or systems that they build because of geofencing in different countries that need the data kept in the country. There are requirements that drive people to build their own system. So the real thing that we're seeing a tremendous struggle with right now is that context, understanding what connects to what. All the different technologies that come together, all the heterogeneity that comes together to deliver a service. And whether you buy best in class technologies to solve one part of the stack, the landscape of whether it's your load balancer or a caching server or the database or the server, the network, all those different components, the security layer, those components that come together often people have chosen specific technologies to solve those problems. The cloud kind of abstracts that away with the hyperscalers. But often you're putting infrastructure that you have on-prem combined with infrastructure in the cloud to deliver an aggregate solution so that multi-tiered architecture, just like back in the day, a three-tiered architecture, we're seeing those emerge again with public cloud because you might want the data that actually generates the information on the web client side to be in your data center. But you still have to understand how the service is behaving. So we really look at all layers of the stack to solve the problem and that's really hard to do. Well, David, great to have this conversation. Before we end, let's get a quick plug-in for the company. How many employees, offices? What's the revenue like? What's your goals? You have to share the revenue if you don't have to. But if you want to, you can. Let's give a plug for the company. What's happening? Well, I'm really proud of what the team's done. We've got a great team of employees, about 370 employees today full-time. They're spread all over the world, probably 80% are here in the Americas. And the vision for the company, we think that this is a big opportunity. We are far from done. We really started the company to disrupt the industry. Because the industry, as I said, was a silo industry and it really is 20 years later. It's still that way. It's not really converged into a unified solution. We have great aspirations. Every year we've been growing the business 40, 50% a year for the last several years. And this year, we'll round over 100 million within the next 12 months of our run rate. So it's an exciting time for the company. Well, you got a great model, SaaS, in a growing, massively growing market, and changing market, complex market, heterogeneous, networks and apps are all being abstracted away and automation is driving this. So I think it's a perfect storm of innovation. Congratulations and thanks for chatting on theCUBE here in Palo Alto. Love to be here, John. Thanks for having me. We're here, CUBE Conversation. We're here with David Link, CEO of Science, Logic and also the founder. Self-funded, big venture rounds, growing like a weed based in DC. It's theCUBE Conversation. I'm John Furrier. Thanks for watching.