Maximizing AI Workloads with Berops’ Gidon Shuman

Hey everyone. Welcome back here to Techstrong tv. I've got a first time guest here to introduce you to, I'm very excited to have him on.

I'm gonna try to do his name justice. If I get it wrong, I apologize, but his name is Gidan Schumann. Gidan.

Schumann. I think Gidan. Did I get that right?

You absolutely did that one. Thank you. So Gidan is CGO at a company called Bops, and I'm gonna let him explain what CGO means.

We'll talk about bops and maybe let's hear a little bit of Gaan kind of journey, how he got to be here today. Gaan, I, no pressure, but go ahead. Tell them, tell them about you, the company and your role.

So thanks, first of all, thank you for having me. I'm really excited to be here. io.

I work today. I work closely today with companies, CTOs and AI startups to understand their infrastructure challenges and help them use clai to reduce cloud costs, scale reliably and simplify multi-cloud Kubernetes. And really my goal is to make sure that our clients get real practical value from day one.

Love it. Love it. Um, so cgo o's Chief Growth Officer.

Correct. Um, so you mentioned two things here. The company's name is Bops, correct.

The kind of product service, whatever you wanna project. io Correct. io.

What does Bops do besides cloudy? Do io Is is cloudy or should we start with cloudy and then go to Bops? What makes more sense to you?

Well, it's all started of course, from a bops, uh, it's a consultation company, uh, in the Kubernetes field, a majority. We work with enterprises to the startups level on the Kubernetes field. And about four and a half, five years ago, uh, based, we started to develop a clothing.

And clothing, uh, is basically a platform for managing multi-cloud and hybrid cloud Kubernetes clusters. Um, so we developed along the way based on our clients and based on use cases out there. And today our main focus is the AI workloads as the trend and demand keep rising in this area.

Absolutely. Talk about right place at the right time, right? Absolutely.

Uh, it it's blowing up it, I just wanna make sure. So cloudy open source, not open source. Claud is an open source.

Absolutely. Okay. You can find it on GitHub.

You can, uh, pull requests and we're happy to support you, But it's, it's, it's operated by bops. Correct. It's not like a Linux Foundation or CNCF or something like that.

Solely us. Solely us. Got it.

Um, now I would imagine you probably started working on when you, when the company first started working on cloudy, you, you really weren't thinking necessary about AI workloads, but probably other multi-cloud hybrid cloud sort of environments. Right? Right.

'cause you know that that's been something that we've seen coming now for seven, eight years, maybe more. You know, I use AWS for this, but I use Azure for that and, and I use Google for my cobe or you know, or what have you. And then maybe I still have some stuff back at the private data center too.

Absolutely. What we see right now is a market that is moving strongly forward tools that help companies get control of their cloud costs to scale quickly and easily manage their cloud providers and businesses want. Today we see that businesses want more flexibility, especially with AI workloads.

And this is exactly where cloud fits. It's basically lets teams seamlessly use resources from different cloud providers, as you said, or even their own servers. It's really depends on cost or performances needs.

For an example, I can tell you, um, there's, uh, companies can shift workloads from, as you said, AWS to more affordable clouds like hener and dramatically cutting costs. And that's because many organization now face stricter better residency rules and cloudy can make it easy to keep data exactly where it needs to be. So really the combination, uh, of flexibility, saving and compliance is exactly what we see the market is asking for right now.

Agreed. Agreed. You know, you mentioned moving into, we call data sovereignty by data sovereignty.

It's, it's it sovereignty, right? That's a huge driver. We're seeing it around the world.

Everybody wants to keep their stuff in their own jurisdiction, right? Right. Whether it be for security or tariffs, financial, et cetera.

But Giden, let me ask you a question, and I I don't mean to insult you. There's a lot of multi-cloud Kubernetes deployment tools out here. What makes cloudy better different than these others?

Yeah, It's a great question actually, Alan, and we in Encounted this que this question, uh, actually several times. And cloudy is, uh, distinguished itself by building a single Kubernetes cluster that spans multi-cloud providers rather than managing separated clusters connected via cluster mesh. So this a architecture eliminates the complexity and overhead of managing inter cluster communication.

It's really enabling seamless workload mobility across providers without reconfiguration. It's also also cloudy provides customization through infrastructure as code scale up, scale down complete capabilities and multi-cloud load balancing and persistent storage solutions. So making it, it's really making it a platform for managing a multi-cloud and a hybrid cloud, uh, Kubernetes environments.

So with clothing, you also get support for compliance requirements like data locality and features as such as cloud bursting that provides flexibility and cost efficiency. Love it. We spoke about AI a few times already in the couple minutes you are on, right?

Absolutely. It, there's so much. It is, I mean, it just takes the oxygen out of every conversation almost, right?

But it's, it's, it's, it's, it's moving on in so many different directions. It's disrupting in so many different ways. Uh, you can't have a solution today without having some sort of AI strategy, but in order to have an AI strategy, you gotta understand what, what's going on in the market.

Right. What, what about AI workloads? What about how are companies using ai?

So what is, you know, the folks at BOPS and Cloudy know about the AI market that maybe some of these other multi-cloud deployment tools don't right. Or are missing? We see a lot of AI startups coming up on a consistent pace, and the demand is absolutely there.

And we understand that many AI workloads don't require the most expensive data center grade GPU to run efficiently. So with cloud, you can leverage customer grade GPUs, which are more affordable and more available, making AI workloads more accessible to startups and smaller teams for 30% of the cost. They provide 90% of the performance.

So with CLA, you also get built in auto-scaling. So GPU resources that are part of your Kubernetes cluster can automatically scale up or scale down based on demand. So given the fact that model trainings comes burst auto scaling automatically frees up the GPUs after their demand drops and has a tremendous impact on these startups or small companies bills.

And this approach reduce overall infrastructure cost while still supporting real world AI workloads. So it's really helping users optimize their GPU usage and avoid paying for ILE resources. So we see a lot of new AI startups and small companies that have high bills and we would love to show them how they can utilize quality for better flexibility and cost efficiency.

Love it. Gi let me ask you another question. Well, before I ask you the question, I have to make a confession.

Sure. Multi-cloud caught me by surprise. I've been following the cloud since the cloud first came out.

I, and I always was a big believer in hybrid cloud, right? That people wouldn't move everything to the cloud. Some was gonna stay in the day, you know, the private data center, the server closet, whatever you wanna call it.

And then some percentage would go up to the public cloud, right? Right. So to me it was always a public private mix of cloud.

I didn't think, but I, I did think that if you standardized on AWS you'd be all in on AWS if you were on Google, you're all in on Google. If Oracle all in, I never saw it coming that people would, would have some stuff here, some stuff there, some stuff over here. The whole idea of a multi-cloud, I miss, I just didn't see it coming.

And then it kind of hit me in the face, Right? We do see a lot of companies, uh, changing strategies from when they started back in the days and their migration to cloud. And we see it actually on a consistent pace as part of ops, um, uh, work and what we do on a day-to-day basis as we do have some enterprises that we are helping them migrating to cloud as their data center costs are just out of the roof.

Sure. So if you can give us sort of a profile or an idea who's actually asking for multi-cloud today? Who's, who is the multi?

Is it everyone or is there a, a special profile? Yeah, I get that cloudy value isn't immediately obvious at first glance. Right?

So we believe that cloudy value becomes clearer, especially for startup focused on AI workloads and smaller companies where GPU costs can account for 30 to 60% of their monthly budget. And this is exactly where cloudy fits. It helps startup and small companies spend less and less money on their infrastructure and offering better flexibility so they can focus really on innovation and growth.

And to truly understand the benefit that cloudi offer, you need to build a cluster yourself and see how it simplifies managing multi-cloud and hybrid cloud Kubernetes environments. So because cloudi allows you to combine different, uh, supported cloud providers and also an on-premises infrastructure, which may also include GPU, uh, needed for AR workloads, you simply choose the most cost efficient infrastructure that fits your needs handed over to Claud to spin it up the cluster and you're ready to deploy your workload. The setup is in most cases, more cost efficient than relying solely on a hyperscaler.

Excellent. Excellent. Good.

I just, we're almost outta time. io. io, correct?

Is that the website? That's correct. For for the project's, Yeah, for the project itself and our support as well.

Um, basically we are right now offering, um, an infrastructure engineer to engineer session to see first of all, if cloudy fits the, our client infrastructure. If not everything is okay, we will always be happy to get the feedback. Um, and we are able to help basically our clients to deploy clouding.

And of course what we want to see is the dramatic and drastic cost reduction. And this is really one of our main goals here. Fine.

And then the company Bops, what's the website for that one? com. com.

Absolutely. You got it right. Fantastic.

Gidon, thank you so much for coming on today. Continu you much continued success with Bar Bops and Cloudy. Thank you.

We're looking forward to seeing more. Absolutely. We're gonna take a break on text Drunk tv.

We'll be right back.