Navigating Cloud Computing and AI with Orkes’ Jeu George

Hi, everyone. Welcome back here to Techstrong tv. Alan Shimel back in studio.

I've got a first time guest on here for me. His name is Drew George. Drew is the CEO and co-founder of a company called orus Think Orchestration, like the plural, if you will, of orchestration orus.

Drew, welcome to Text Drug tv. It's great to have you on here. Uh, thanks for having me, Alan.

Uh, nice to be here. Nice to have you. So, drew, let, before we talk about Orcus and orchestration and all that good stuff, let's talk a little bit about you.

You're the CEO co-founder, and you know, I, I think everyone's always interested in what, what made this man sign up to, you know, go through co-founding and, and running his own company. It's not something we do lightly. Yeah.

And, uh, a lot of, uh, it has to do with, uh, my time at Netflix. And, um, you know, before starting Orca, I, I did spend some time at Uber before the IPO, but spend a significant amount of time at Netflix, and that's where it all started. And, uh, if you remember back in the day, um, the cloud was still coming up, right?

You know, pre-cloud era, like everything was big data centers, one of big applications. Netflix was also the first company of its size to operate completely in the cloud, right? And, and so much so that every single cloud computing principle that you see on the internet today came out of Netflix.

And, and we used to have folks from AWS sit in, sit in the Netflix offices trying to take feature requests, and a year later they would make up, uh, into the AWS tool set, like, you know, things like auto scaling and how to do multi-region, how to do reliability and, you know, failure, uh, addressing it scale and all of that stuff, right? And, um, Um, the whole idea of chaos engineering. Absolutely.

And, and, uh, you know, that that's May, that's, that's also, you know, those principles also is what made Netflix really great and operate extremely efficiently and add a scale. And we talk about scale, like, you know, we talk Netflix scale because, uh, you know, it is hard to find companies that operate at that scale. Um, and, and especially in the early cloud days when, and these things were still being formed on how to do these things at, at like really large scales, right?

Um, conductor, the product that was built on Netflix, uh, also had a lot to do with one, how do we move a big business? Back then, you know, we were like under 20 million or so paid subscribers. Um, it was hard to find paid subscriber services that were that huge in number, right?

Uh, but that said, conductor had also a big role to play in, in that journey on Netflix, moving from completely on the data centers to operate completely in the cloud and making this transition while keeping the business operational, right? That was one aspect of it. The second one, um, was today, if you go to Netflix, most of the content that you see on the website is origin programming, right?

Content that you would only see on Netflix, not anywhere else. Back in the day, it was more licensed content. Um, and there was a big strategy shift back then saying, yeah, how do we, uh, create some differentiation in the business?

And, and the answer was, let's try to produce our own content. And that's where the scale comes in. Imagine that today we have Netflix has over 300 million subscribers doing several, I would say a hundred thousand pieces of content every year, 50, 60, 70 different languages, lot of different art book combinations.

And when you multiply them all together, and then how do you run something, uh, a complex process as this at Scale Conductor enabled a lot of that and continues to enable a lot of that in Netflix today, right? Um, we saw that, and you talked earlier about a little bit about microservices. The, the whole microservices architecture also started when, when cloud came about also led to an interesting problem where we had lot more, uh, microservices that people in the company, half the microservices were built to talk about the services.

And that's where the whole notion of orchestration came in. Conductor was born to kind of fill these gaps and put the need and solve this problem At Netflix, we just hit the problem first because we were one of the early adopters, right? So seeing this big traction in Netflix, we decided to open source that that was back in 20 16, 17.

Over the last few years, we have seen over two 3000 companies use the open source product at scale, uh, and some of their very, very core business units. So take a company for example, like Tesla, Tesla's entire stack is built on open source, right? Uh, the, the conductor.

So if you look at how, how people go and place a, uh, order for the car online, few months later, the car shows up home, right? Their payment systems, billing systems, CI ICD pipelines, right? And we saw that same journey in, in companies like Tesla, companies like Wiggy, which is the, you know, they both with their kind of the board edge of India saw that in the big banks like JP Morgan, Amex, you know, Morgan Stanley, healthcare companies like GE Healthcare, that's where we saw there was this big shift of, you know, how the cloud came about and changed everything, right?

No one builds data centers anymore. Similar thing on a layer above that, when people are thinking about building applications, um, you spend a lot of time getting the foundational systems right, on how do you run this at scale? How do you run this at extremely high li team?

And those are things where Conductor was super, super good at. And we are seeing a big shift on how developers think about building applications. And that's where conductor fits in.

That is a passion that basically came about and say, Hey, this is, there's this world that is completely set for kind of making this big transitional shift happening on building applications, right? And that's, that's how we go and then, and start over, right? Um, it's been, you know, three plus years right now.

We have an enterprise grade conductor on the top of open source that we built, um, Netflix and Orcas combined together, and then, you know, took the open source story for a couple of years since we started Orcas, and then we got worked with Netflix, called them to archive that project, and moved, moved conductor to an open source foundation. And we continuing that fabulous journey in, in the enterprise ORCUS world as well. I love it.

Thank you. That was a great, a great history and explanation. Drew.

You know what I always like, I co-founders are always pa not just co-founders, but founders, people who stock companies, entrepreneurs, their passion bleeds through when they talk about it almost like they're talking about their children. Absolutely. You know what I mean?

And, and so, and it comes through with you. So, and that, that's genuine and that's, that's a good thing. So, so congratulations on that.

Let's talk a little bit. So I, I mentioned in the opening, Orcus is kind of sh plural, shortened orchestration in some way. Expand on that a little bit.

Yeah. So if you look at orchestration, what Orchestrate was conducted as it enables developers to build applications, applications that are typically run in the backend, uh, they are asynchronous or synchronous in nature, right? You know, long running workflows to real time a p orchestration that is run at high scale, um, you know, high throughput, low latency, for example, and off late, uh, the last couple of years, building out agent applications, agentic workflows, and you know, how to also build I reliable AI agents, right?

And these are all forms of applications and, and that's, you know, changed over the years. But, uh, we are the platform that enables all of that, right? And, uh, how do you do this, especially when you want to run this at enterprises, how do you do this in, in a way which enterprises are, you know, the things that enterprise look for, right?

Outside of the, the regular stuff, which is security governance. Uh, but you know, we need to run this at extremely high liability, extremely high scale when needed. Not everyone needs scale, but extremely high liability.

And when we talk about liability, what that means is low error rates, which means that, you know, how do we detect error? How do we make that error rates really slow? How do we take the, you know, time to detect and time to fix and time to deploy when these things happen?

And this is happen all the time. Um, and, uh, that's really where kind of we fit in, right? And, uh, this is an area as as big as the cloud itself.

And, um, and then we have seen, you know, with, with also agent take stuff coming in, like, you know, the, the mode of how people kind of go and develop applications has already changed as well, right? So, um, and then if you look at it like, you know, everything that you build, you know, there are individual small pieces that you build, but you need to tie all these things together to make business sense out of it. Um, and whether it's at the high level business layer or platform layer, but um, and then things are talking to each other, that's where the orchestration comes in, right?

And then the common things that almost every single company on the planet needs, who's, who's building software, whatever size or shape it, it might, might end up, uh, being used in, right? And that's where like an orchestration's really come into play. And, uh, when we started, orchestration was still fairly new, right?

Like even the term was getting coined and, and, and, uh, fairly new things. But that has really accelerated in the last couple of years, right? And, uh, we have seen lot of companies also come out emerging this category, but I think we are really prime to go and lead the space as well, right?

And that's also very, very exciting. And we see this, this journey is also being great because the, the usage and how people are thinking about building applications also changed. Yeah.

All right. So Drew, as you can expect, I fair, I spend a fair amount of time talking to people about ai, right? Everybody wants to talk about AI today and what effect it's having on their job, on their industry, on society.

com, our newest site. So, you know, things like microservices and, and let, let's call them new architectures and, and new ways of looking at, you know, building out applications are what I spend a lot on, right? And sometimes I, I, so I think I live in a bubble where like, I think everyone knows this, right?

We're all living in this, well, maybe you are too, and you're in my bubble. But when you step back and look at it, Jew, we are in a is a very interesting times. It's kind of a revolutionary kind of timeframe where things like cloud and cloud native are being married to ai, agentic AI and, and things like this.

And the, I don't think as we sit here, we're not a hundred percent sure how this all shakes out, except we know it's gonna be big and it's gonna be very different. I'm wondering if where you are sitting, what you are seeing and, and do you feel that way? And if so, and what, how is it going to shake out?

Yeah. And I think this has been a fundamental shift, right? I think the last big shift that happened, uh, you know, combined with cloud coming out and, and how people can go and easily deploy microservices at scale.

And then obviously that with that comes orchestration. That was a big last shift that happened probably from, you know, 2007, eight, you know, up until now. Um, last couple of years is where kind of AI really, really took off, right?

Uh, with obviously things like Chad, GPD coming out and, um, and then people basically seeing all the magic that it can do, right? But that said, uh, there is the consumer aspect of it. Like when you go and use Chad GPD over the last few years, you've seen like, you know, how it's really taken off.

Um, it is, it is, you know, hallucinations were a problem early on, right? Like, you see less and less of that right now. But it is, it is magical to think about, like, you know, all the great stuff that it can do, but sometimes also trips up, some trips up on very, very simple things, right?

And then we, we chat with, uh, the application and try to get the right answer out of it. Right? Now, you take the same thing, go to an enterprise, the enterprise, you don't want to have any kind of those things.

You need to have you building these agencies. You see the efficiency benefits that comes out of it, but you also wanna make sure it works really, really well and it, it doesn't do things that it's not supposed to do, right? And when you, the two big areas that LLMs AI has been kind of really being used and found, its, I would say like, you know, product market fit is in, in chat, complete areas, chat areas, and also code, right?

But when you want to run these applications and build these things at scale, few things that come into play. One is you have existing application stacks that enterprises have right now. One is how do you go and use AI in those companies without the need of rearchitecting your entire stack?

'cause that takes a long time to do. People don't want to kind of break down an existing business and spend two years kind of, you know, you know, getting, getting that right. So that's one piece of it.

The second piece is how do you, for, for agents, when you think about agents, right? Like, you know, there's this whole thing about, you know, the agency aspect of it, but there's like a how can you build an agent in such a way that you can enable that to make autonomous decisions, right? And to make autonomous decisions happen, it needs a right kind of tooling in enterprise, which means that the ability to go and connect the agent and the LLM models to the internal tooling within enterprises, and it could be tool that could be APIs, whatever format, they kind of expose that.

And then also how do you connect that to internal knowledge bases, internal databases, right? That is where it makes these things agents really powerful, right? And then the principle on where to use ai, where to use LLMs and where not to use it, right?

And right now, if you look at it, the way, the feedback that we've been getting and how people have been using our, uh, you know, agent workflows and AI and products have been things which are very deterministic in agent. Like, you're gonna call an API, right? Like, uh, there's nothing an agent needs to do magical to call an API because an API is already built out, it's already existing enterprise, it's working calling that is not really where the power of an agent comes.

But when to call that and how to call that, that is something an agent can really do well, right? LLM models can really do well. Um, non-deterministic things, which if you were to go and build these things from scratch, it can be extremely much more efficient.

So the mixing and matching of these things, and also to build in the guardrails that you need to, right? Like if you want to, uh, make actions, uh, the autonomous of the agents, if you wanna make them happen really well, you need to also put the right kind of, when, when agents are gonna call into the tooling, you also wanna put in the right car and placing that, Hey, I build this agent, this agent has been built for doing a specific purpose. If a human, were going to do that, we are put in the right checks and balances in place, right?

Can you do the same guardrails on agents as well? And that's really what makes agents really, really powerful, right? And obviously, you know, this is also fast moving industry, right?

Um, things will change rapidly and you know, this is where things are right now, right? And we, we think this is where the industry is gonna be move in the next, you know, year or so. But, you know, we have to keep a very, very close tab of how, how things are also getting developed in the industry so that, you know, we can, we can adapt to what enterprises are looking for, address their problems, right?

But so far, this is the action that we have seen really, really good adoption coming. Absolutely. You know, chu we we're running low on time.

We mentioned the website was, or io That's right. Or io, OKS io. Well, someone out there who says, you know, this seems very interesting for me.

I'd like to find out more. I want maybe try it, whatever. What, what's like, what's the path they should take?

Yeah. So if you go to orchestra io, right? If you want to, as a developer, if you wanna try it out, uh, on the top of it, you see a developer tab, you can go or you can go directly to or io slash developers.

There is, um, developer edition that you can sign up for free, perpetually free. You can go sign up on that and you can, uh, you know, with your social accounts, with your company accounts, whatever you may choose. And then you can build applications, you can try out and get a sense of the enterprise product, uh, every single thing in, in, in what you find in enterprises enterprise product.

You can find it here as well, right? So that's one option specifically made for developers. Um, if you want to use as an enterprise, um, you can, there's a get a demo tab on the top, you can talk to someone there.

Um, you can, we can, uh, sit with you and figure out your needs and, you know, car bought Enterprise edition for your business as well. There's like two different ways to go, you know, get in touch with us, right? Uh, but if you're an existing open source user or, uh, if you have questions, there's a Slack channel out there, there's a discourse channel out there, but Slack is one of the easiest ways to connect with us.

But people, enterprises, or people who don't have access to Slack, there's a discourse format out there. Both the links can be found on the homepage, on the top right corner. Um, and you can, you can get in touch as, uh, touch with us in that manner as well.

Excellent. Drew, we're about outta time. I want to thank you for coming on, educating us a little bit about Orcas, giving us a little bit of your own journey.

And, and look, there's no doubt we're living in a time where we're seeing change, reshaping our, our daily workflows, our daily tasks, and how we're doing things. It's gonna be an interesting couple of years. Maybe you could come back on and tell us more sometime soon.

Absolutely. Thanks for having me. I'm love to come back again.

Love it. io, uh, here on Text Trunk tv. We're gonna take a break.

We'll be back with more in just a moment.