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and more growth for your business. Save dev time, win over your customers, and propel your business forward with PropelAuth. Check them out at PropelOff.com. That's P-R-O-P-E-L-A-U-T-H dot com. Open telemetry emerged in the last couple of years. And it represents a very important paradigm shift that I actually believed had to happen years ago, but finally we are getting there. This huge positive paradigm shift comes with a huge cost.
And that's why we are so excited about it, because Cosley fits in exactly into that paradigm. This data is important, but more important is how do I extract from this data what's important? Because we consume the raw data and immediately make sense out of this data. My name is Samuel Krieger. I'm the founder of COSM.
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Shmuel Klieger is a self-proclaimed old man. He was born in Israel and served in the army for six years. It was in the army where he obtained his passion for the space, from which he got his bachelor's, master's, and eventually his PhD.
Outside of tech, he's married with four kids and two grandchildren. He met his wife during grad school, and they now live in New York. When asked what he does for fun, he laughed and said that he enjoyed living in the best city in the world, with everything at his fingertips. Schmuel found himself at the center of an idea that was the culmination of all his years in technology, to build something that could not only process large amounts of observability data,
but could make sense of it and take appropriate action. This is the creation story of Cosley. What we built in Cozly is actually a very natural continuation and evolution of my entire career. Before Cozly, I was the founder and president of Cozly. Turbonomic. I founded Turbonomic in 2009. We became the leading providers for application resource management in virtualization and cloud environment. We were acquired by IBM in 2021.
I was the CTO of the Resource Management Software Group. I arrived to EMC through an acquisition of a company called Smarts. I was CTO and co-founder of Smarts. We started smarts in 1993 and became the leading providers of root cause analysis for networks, and we were acquired by EMC in 2005 for $300 million. I came to the U.S.
to do a postdoc, but I did it for a very short time before starting smarts. But if you go back before my PhD, I started my career, as I said, in the Israeli army, being a system programmer, dealing with system management. system operation and so on. If you look at my entire career, with the exception for the six years that I did my PhD, it was all around system management, system operation, and what I was focusing from. in my two start-up smarts and Turbonomic 9 Cosly.
is really, if you look at IT operation, It's a very labor-intensive industry. Humans are involved in every step, the daily operation of the IT. I believe that's all that has to be disrupted and it needs to be changed. If you look at smarts, we were focusing on one aspect of that, which is automating the root cause analysis or the troubleshooting. We ended up focusing only on the network.
And then if you look at Turbonomic, my focus was, okay, how do I automate resource allocation and resource management in IT, focusing in virtualized and cloud environment? And if you think about... Both of them, they are tackling one part of the puzzle. There's no one magic bullet to what I'm trying to accomplish, which is eliminate or reducing the labor associated with the ongoing operation of IT.
You have to do what we did at SMART, what we did in Turbonomic, but you need, the way I look at it, you need to build a system that understands an environment, understand what is a good state of the environment. good state is basically a state in which all the applications are performing and delivering on the service and be able
The environment is in a good state. If it is in a good state, what action it can take in order to stay in a good state? If it's not in a good state, what is the root cause, why it's not in a good state, and what action to take to get it back to a good state? So it's not like one thing that will get you a system that reduce label or take label out of the IT operation. and it's building a system that combines multiple analytics
that are working together to do what I just described. Our initial focus is on troubleshooting and root cause analysis. specifically in cloud-native environment, relying very heavily on open telemetry as the sources of data that we are consuming for the analytics. But to build a system that automatically can troubleshoot and pinpoint what problems you have in your environment, think about a typical environment.
where you care about the performance of the application, the performance of services. You're monitoring those services using, for example, OpenTelemetry. You're monitoring the latency, the error rates of those services. And then when something goes wrong somewhere in the environment, you are not getting a single, oh, here is a service that has high latency. You get a flood of services that are...
having degradation, and then you have to troubleshoot, you have to go into a war room, you have to figure out what's actually going wrong, what is the root cause. So instead of human doing it, the idea of it costly is that software, the system will tell you. Here is the root cause, and this would cause all of the service degradation that you observe in your environment.
So let's dive into the MVP for Causey, that first version of the product you built. How long did it take to build and what sort of tools were you using to bring it to life? The MVP that we build is basically what I just described to you.
product that is focusing on Pinpointing root causes that cause service degradation, i.e. services to suffer high latency or high error rates the product is focusing on discovering the dependencies between services discovering the data flow between services discovering the underlying infrastructure that support those services. and use all of those information to be able to pinpoint when those, let's say, when I have a Kafka messaging that is a bottleneck.
pinpoint that or if the API is not performing, if an API at some endpoint is not performing well. pinpoint that and say this API is now causing all of those services to be degraded. And that's basically the MVP. It's out of the box, deployed, leveraging, open telemetry, and if you're running Kubernetes, the native instrumentation of Kubernetes, but mainly open telemetry. the OpenTelemetry traces and logs to both to discover the environment, to discover the dependencies.
to monitor the latency and error rates of services and from there infer the root causes.
that I described. The underlying, what we bring to the table is in building analytics that is based on patents that I developed years ago. At the core, it's the ability to represent causality knowledge between what may go on and how it will manifest, how it will propagate and manifest itself in an environment and then automatically instantiate that with topological information of the environment to create automatically.
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I'm curious where you'll take this one. I'm curious about how you progressed and matured it from that MVP point that you just described. And I'm curious about, you know, to wrap that in a box, I'm curious about roadmap. How do you go about building your roadmap? How do you go about deciding what is the next most important thing to build or to address with Cosley? So there's two elements to that question.
One is, okay, my goal is to build a system that continuously keeps the environment automatically, with minimum labor, keeps the environment in what I call a good state or a desired state. And in order to do that, automatically pinpoint would cause it. In order to actually have a system that continuously delivering, making sure that the applications are performing, that's not enough.
You need to figure out what actions do you need to do in order to prevent it from getting into a bad state. Figure out what are the actions. to remediate in case you are not in a good state. And if you really want to be better, you want to predict and say, okay, given the current trends that I see in the environment, you are running the risk.
getting out of a good state in a week from now, here are the set of actions that you better do now to prevent you from a good state in a week from now. So there is a whole breadth of analytics that you have to bring to the table that collaborate and work together. to accomplish the system that I actually want to accomplish. So that's one dimension that it's critical to say, look, today I'm giving you automatically troubleshooting and pinpoint root causes.
But I need to do more. I need to be able to automate the remediation. I need to be able to figure out how does it turn? Is it going to stay in a good state? And if not, what action to take to prevent it from going to a good state? All of that requires to bring different analytics. we are building that will work together with the good cause analysis system that we have today. The other dimension is what I call the adjacency dimension.
At any product, you get into the market focusing on some part of the landscapes of the technology to be managed. We're starting with cloud native application, running on Kubernetes, Kafka messaging, database servers, and so on and so forth, relying on open telemetry as the instrumentation. But that doesn't necessarily provide the entire landscape. If you go to any enterprise, they may say, wait, I have these.
But I have some other cloud service or I have another instrument, different instrumentation that it's adjacent to what we do today, but we need to expand and cover. that adjacencies. So the other dimension of the roadmap is how do I expand my coverage to adjacent domains to where I'm covering, of what I'm covering today. I know Cosley is doing some big things with OpenTelemetry, the protocol there. Tell me about that. Tell me about what things look like with Cosley and OpenTelemetry.
Open telemetry emerged in the last couple of years, and it represents a very important paradigm shift that I actually believed had to happen years ago, but finally we are getting there. And the paradigm shift is, if you think about how we used to manage IT in general and an application most importantly, we had management software. that needed to manage the application in the IT environment. The paradigm is that management software is reaching out and discover what's out there.
reaching out and monitor what's the characteristics, the metrics of what's going out there. If you think about it, it's a fundamentally wrong paradigm. If you think about it, the government, every time a baby is born, they would send the police to the... to find out, oh there is a baby out there, here are the parents of the baby, and here's all the information about that baby.
That's not how the world operates. What happens is when a baby is born, the parents tell the government, here is a baby, this is the name of the baby, and here is all the information that you need to have about this baby. Parents have obviously incentive because the government will know about that baby. If you think about that analogy, it's the same thing of what happened in management. It doesn't make sense for the management to go.
and figure out periodically or whatever, is the application there, what the application is doing. It's much more natural and scalable if the application will tell the management, here I am, here I'll give you some information about me. and then do a proper management of me in the context of the entire environment. And that's what Open telemetry represents that paradigm shift of moving and putting some accountability and burden on the application.
that if you are the application developer, you want your application to be properly managed and properly operated, you please tell me something about the application. So this is a huge paradigm shift and a positive paradigm shift. But it comes with a cost. It's not free because once every application will keep sending the management every few minutes, every few seconds. all kind of information about itself, what we call traces or logs, you are overwhelming.
the management with a lot of data, huge amount of data. and you can to some degree bring down the management. So this huge positive paradigm shift comes with a huge cost. And that's why we are so excited about it because Cosley fits in exactly into that paradigm. and saying, okay, this data is important, but more important is how do I extract from this data what's important. consume the raw data and immediately make sense out of this data.
create the service dependency map, create the data flow maps and understand and out of that build the causality mapping to a point that the information that the management gets. and the management maintain and the user see is the information that matters. is being created, Cosly is able to extract what's matter to the user.
To do all this, you've got to have the right people at the helm. How did you go about building your team? What do you look for in those people to indicate that they're the winning horses to join you? That was actually very easy. In the case of Cosley, if you look at all the engineers with the exception of one, they're all people that came from Turbonomics in my previous company, so hand-picked if you want.
my top engineers that I had in Turbonomics and they happily joined me as founding engineers here. I have a couple of engineers that I actually started with me at Smarts in my first company. So the entire engineering team is basically, with the exception of one person, it's people that work with me in Turbonomic and some of them are actually smart.
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Let's talk about scalability. Was this built with scale in mind from day one? And I assume there's things in mind, abstractions and things. Or have there been interesting areas where you've had to fight it as you've grown? In this space for 30 years, all my customers are big enterprises, big banks, big financials. So from day one, we built it for scale enterprises. The same way Turbonomics served 8 out of the 10 top banks.
in the US and Fortune 1000 companies. Smarts was the same. So I always built enterprise scale software. So from day one, that's what we built here. The customers that we have today are managing tens of thousands of microservices. using our software from day one. It's not like that we started small and we go from there. From day one, it's built and designed.
to manage enterprise scale environments. So as you step out on the balcony, look across all that you've built with Cosley, what are you most proud of? Look, Cosley is still in a very early stage. There's a lot of unknown, but at this stage is putting a team together, a team that when I, after Turbonomic was acquired, I was waited to start this company closely.
till those people were available for me. So just putting the team together, enjoying working with them, proud that they wanted to continue to work with me. Together, putting a product that's starting to get traction in the market. I'm doing it third time around. And obviously, as I mentioned, I had bought companies that I had previously.
had a very good success. I guess I do the third one because I believe that I have still something to prove. I would be proud if I'm able to prove my thesis that we can actually. make a dent and move the needle in terms of reducing labor associated with IT operation. To be honest, we are very early for me to declare victory here. Still early, but let's flip the script a little bit. Tell me about a mistake you've made and how you and your team responded to it.
I think that if you look at building a company, any company, especially a company that tried to disrupt something. You have to have conviction around what you're building. You have to have a strong belief and a strong intuition behind it, even beyond what the market or the customers are telling you. In all cases, both Cosley and the two companies before, me personally start with this strong conviction and strong intuition about what the market needs.
I am trying to surround myself with people that has that belief and has that conviction. In the early stage of a company, it's relatively easy because I'm handpicking those people.
Those people, I know them, they know me, and that's an easy step. As it goes, you start hiring other people, and with some of them, you don't necessarily... making sure as the company grows you still have a critical mass of people that has the conviction that you have and internalizing the belief and the insights that you have.
is very difficult i found when we go to but we ended up to bonomi being company when we were acquired we were 700 people the number of people that actually had the conviction and actually understood and internalized it depth of what we did in Turbonomic was a handful and they get lost. They are not a big enough critical mass. So you cannot expect that 700 people will understand it. You want to have, let's say, 10% of the company on the same page with that conviction. My mistake.
If there was a mistake, it is that as we grow, I wasn't able. to make sure that there is enough critical mass of people that has the conviction and the belief that I had in the power of the technology that we built. So let's talk about the future. What is the future for Cosley? It's early days, but what's next? What are you going to go do? What is your big plans for the product and for your team?
I think we have to get to a place that this product is being adopted by the big enterprises that they are. see the value, that the value is jump out to them from the product, that we are able to take the richness and depth of capabilities that we have. and put the spotlight on the core value proposition in such a way that Every user immediately understands the value proposition of what they are looking at.
And that takes time. That's not something that you nail down in day one. So that's our first milestone is to get to there where the value proposition jumps out to every user. Again, I'll go back to my passion, if you want, about why I'm doing that again and again. I want to reduce labor. So I want to see people using Cozly to automate things that today they spend a lot of time and a lot of human labor around.
My hope and my criteria for success is how much automation I'll be able to drive with Cozli. to reduce the labor associated with IT. It's very similar to the journey that I had in Turbonomics.
If you look at the core of Turbonomic, in Turbonomic we focused on automating certain aspects of resource management in virtualized and cloud environment. And it was a journey. And I remember when I started, They said, well, nobody will trust the software to do and automate an app because people will want to be in this loop of this automation. And it took us a few years to get to a place where we gave the key to the software to automate.
We'll have to go through the same journey here because even now, I said, trust my software that we know how to pinpoint. Where is the root cause? But even more important, trust my software that it can remediate whatever it needs to remediate. Or trust my software to act on the environment. to keep it in a state that we believe the environment needs to be. This trust is a big ask. And the success of Cosly is a function of how successful we'll be in gaining and earn this trust from the users.
Let's switch to you. Who influences the way that you work? Name a person or many persons or something you look up to and why. There is one person that influenced dramatically my entire career, which is Professor Yimini, he was a professor in Colombia. He was the co-founder of Smarts. He partnered with me in Turbonomics. A lot of my philosophy and my ideas is what I learned from him. He was a well-known professor in network management specifically.
He wrote papers in the 80s about network management. Twenty years later, people just started to think and adapt. My entire life career with him was he always came with ideas that were 25 years ahead of where the market is and my goal wasn't. to challenge the idea but rather to figure out how to take what he's telling me and figure out how can I apply and leverage it now or in the next year.
So he is a huge influence. The way I look at the space, my entire evolution after my PhD. And I can say that everything I do and everything I preach, if you want, philosophically about the space is something that I learned from him. He wrote a paper about network management and how to model and abstract and represent networks.
But my takeaway from that paper, and that paper was written in the 80s, was the starting point to manage complex environments, whatever the environment is, a proper abstraction or a proper model. And that's maybe very simple and sounds very intuitive to everyone, but if you look at the space that we are playing, no vendor is actually doing it. And that's a very important concept to start with.
So that's something that I learned from him when I started to work with him. And that's a paper that he wrote in the 80s. Okay, last question, Schmilt. So you're getting on a plane and you're sitting next to a young entrepreneur who's built the next big thing. They're jazzed about it. They can't wait to show it off to the world and can't wait to show it off to you right there on the plane. What advice do you give that person having gone down this road a bit several times?
Have the right convictions. And if you have the right convictions, don't let others telling you that you're wrong. But you have to start with a strong conviction. I'll say something that might be a little bit controversial because people always say, oh, listen to customers. They will tell you what to build. But you all know the famous Henry Ford. If I would have listened to my customer, I would build a faster horse.
I'm a technologist, and I'm coming to everything from the point of view of a technology. Even yesterday, I had a conversation internally with someone. I said, oh, we have to define a problem and then solve the problem. No, I'm building a technology out of a certain conviction, and then I find a problem to solve with the technology I built.
And that's controversial because a lot of, especially marketing, go-to-market people will say, start with the problem and start with the user and start with the customer. And I'm saying, you know, I'll build something and then I'll find the right problem to solve with that thing that I'm building. I like that advice. I think that's fantastic. Well, Smil, thank you for being on the show today. And thank you for telling the creation story of Cosley.
Thank you for having me. And this concludes another chapter of Coach Story. Code Story is hosted and produced by Noah Laphart. Be sure to subscribe on Apple Podcasts, Spotify, or the podcasting app of your choice. And when you get a chance, leave us a review. Both things help us out tremendously. And thanks again for listening. If you're looking for legit ways to make extra money, you've got to check out the side hustle show. with hundreds of actionable episodes to choose from.
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