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Cloudcast media presents from the

0:41

massive studios in Raleigh, North Carolina.

0:43

This is the cloud cast with our undeveloped

0:46

and Brian Graceland, bringing user

0:48

of cloud computing from around the

0:50

world.

0:52

Good

0:52

morning, good evening, wave y'all, and welcome back

0:54

to the cloud cast. We're coming to you live from our

0:56

massive podcast studios here in Raleigh,

0:58

North Carolina. And

1:00

as I hit record on this, it is

1:02

the middle of September. And

1:05

probably from a news standpoint, there's

1:08

two big news articles we need

1:10

to talk about, and we're gonna go ahead and jump

1:12

right in. First of all, VMware

1:15

Explorer, formerly known

1:18

as VMware World, happened recently. And

1:20

Alex William, over at the new stack,

1:22

put together a really good piece.

1:25

Kinda highlighting some maybe

1:28

some of the less covered aspects of

1:30

it, but of a particular interest to

1:32

our listeners. And so this is less

1:34

about virtualization and less about

1:37

infrastructure, but more about

1:39

cloud native, about containers,

1:42

tanzoo, and also the

1:44

rise of what they're referring

1:46

to as a platform engineer. So a platform

1:49

engineer, of course, is somebody

1:51

that is building the platform for

1:53

the developers because you see a lot of companies

1:55

these days. How do we talk to developers? I wanna

1:57

talk to developers. At the same

1:59

time, they may not be the ones

2:03

doing the purchasing at times, or

2:05

they just don't care because

2:07

they don't care about the platforms underneath it. It's

2:09

not something they think about on a daily basis.

2:12

So interesting article

2:14

that also goes in to mention just

2:16

the dev experience in general

2:18

and VMware's history there

2:21

and They also mentioned Backstage,

2:24

which is an open source project VMware

2:26

uses to support its developer experience.

2:29

They mention Pivotal and

2:31

the history with Pivotal and the spin

2:33

in and spin out and they mentioned Heptio

2:36

and a good friend of the

2:38

show, chip Childers who

2:40

is the chief open source officer

2:42

at VMware has

2:44

some things to say in the article as

2:46

well. So definitely worth

2:48

checking out if that is of interest to you.

2:51

And for our second article, the other big thing

2:53

going on in the industry right now, if

2:56

you follow crypto, is Ethereum,

2:58

and the Ethereum blockchain merges

3:00

and changes coming up. I'm

3:02

not gonna go into this too

3:05

in-depth, but I did want to mention it because

3:07

it is a pretty big news piece, because it

3:09

has been attempting

3:11

to happen for a long time now. I wanna

3:14

say, gosh, a good six months

3:16

or so now they've been trying to do this merge.

3:18

And if you're not familiar

3:20

with it, what is it? Well, you have really

3:23

two ways to really do blockchain.

3:25

One is called proof of stake. The

3:27

other one is called proof of work.

3:30

Proof of work is the older way. It's what

3:32

the original Bitcoin was built

3:35

around in Ethereum as well. Most

3:37

new ones are what we call proof of stake.

3:39

Why is proof of stake necessarily better?

3:42

Each one has their benefits and downsides,

3:44

but the big benefit for proof of stake

3:47

much, much lower energy

3:50

consumption. Proof of work requires

3:53

a lot of computation. When you think

3:55

about a Bitcoin miner, or the,

3:57

you know, the big crypto miners and the big

3:59

rigs and with big GPUs and lots

4:01

of fans and taking all the energy, they're

4:03

typically proof of work. So

4:05

by moving over to proof of stake,

4:07

hopefully that will alleviate some

4:10

of the concerns around that in Ethereum.

4:12

I mean, the two big downsides

4:15

to Ethereum have always been. It's

4:17

it tends to be a little slower than a lot

4:19

of the others, and it tends to be a power hog.

4:21

So this will at least

4:24

start to fix some of that, and we'll

4:26

lay the foundation for more improvements

4:28

over time as well. With

4:31

that, I'm going to wrap up cloud news of the

4:34

week. Coming up right after the break,

4:36

we are gonna be talking about Kubernetes

4:38

cost optimization.

4:40

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5:35

in

5:38

And we're back. And folks, as

5:40

we get closer and closer to Quban,

5:43

North America, and Detroit, and if

5:45

October of this year. We've been spending a little more

5:47

time this summer throughout the last

5:49

few weeks and so forth. Really kind of digging

5:51

into the things that are kind

5:53

of adjacent to the core Kubernetes

5:56

project. We've obviously talked about that quite a bit

5:58

and we've been looking at more and

6:00

more areas in which as more people

6:02

deploy Kubernetes, as more people optimize

6:04

their Kubernetes as they look to

6:06

kind of expand where it's being used and so

6:08

forth. We continue to look areas in

6:10

which, you know, there's technologies, there

6:12

are new ways of thinking about how to make those

6:14

environments better, how to make them more efficient, how

6:16

to make them more responsive

6:19

to your business in order for you to be able

6:21

to to go solve business problems and not have to think

6:23

about Kubernetes so much. So You know, we

6:25

continue to kind of dig into that and, you know, one of the

6:27

things that we love to do on the show is not only

6:29

look at the new technologies that are making that

6:31

happen, but also you know, new companies

6:33

that are, you know, at the forefront

6:35

of making that happen. And so today, we're really excited to

6:37

kinda do both of those things. So so really

6:39

excited to have Shahar, Yakhov who is

6:41

product manager at Granite, who is an

6:43

intel company. Shahira, welcome to

6:45

show. Great to have you on. Hey, Brian.

6:47

Happy to be here. Yeah.

6:49

So excited. You know, we we love having

6:52

new companies. You know, we love having

6:54

companies focusing on new areas. Tell

6:56

us you know, before we dive into into granulate

6:58

and all the things you guys are doing, tell us a

7:00

little bit about your background and, you know,

7:02

ultimately, what what, you know, what you

7:04

guys focus on at granulate? Sure.

7:07

Yeah. So first of all, my name

7:09

is Shahriar Khong. It's really hard to

7:11

pronounce it in Hebrew. In the English because

7:13

it's an Hebrew name. And so

7:15

Sharia Cobb. Currently working as a

7:17

product manager at Granite, and

7:20

I started my journey around there.

7:22

decided to go as a software engineer in the

7:25

Israel Defense Force. And since

7:27

then, I had the opportunity to work with

7:29

different companies, and specialized

7:31

mostly on the dev side

7:33

and CSD aspect and

7:35

some things about monitoring and observability.

7:38

And I did a transition from a software

7:40

engineer into project manager somewhere

7:42

in the middle. And right now,

7:44

as I said, I'm working on twilight, really

7:46

excited about that because I have the opportunity

7:48

to both use my technical skill

7:51

that I got from my my technical

7:53

background and my product run with

7:55

skills. Yeah.

7:56

No. That's that's great. We've had a number of

7:58

of alumni of of of Israeli defense

8:01

really really smart people. They trade you

8:03

very well. You you work on very challenging

8:05

problems. So, yeah, when we see

8:07

when we see those folks move into

8:09

into the technology product space, we

8:11

always know that there's really good foundation

8:13

there. Let's let's start by

8:15

talking, you know, I highlighted at the beginning, we're

8:17

gonna talk a little about cost optimization.

8:21

As you think about it as a as product

8:23

manager as to, you know, how you

8:25

help companies that are running Kubernetes

8:28

or any large compute environment,

8:30

like, how do you think about, you know, what are the

8:32

things that we can do to help people around

8:34

cost optimization? What's kinda what's the

8:36

bigger picture the way you think about it?

8:39

Yeah. So I I

8:41

think you highlighted it's pretty good. Right

8:43

now, it's pretty pretty

8:46

important to understand where your

8:48

money goes to? What what is the cost that you are

8:50

paying for your environment? Whether it is,

8:52

like, on premise environment or

8:54

cloud environment? Yep. It doesn't matter. Because

8:56

right now, the burn rate, it's really

8:59

important thing to understand

9:01

where the burn market and the burn market

9:03

there, everything to change, the economics

9:05

So understanding how to improve

9:07

your margins, I think it's pretty pretty

9:09

important right now. Right. And

9:12

and and as an optimization

9:15

company, and we have multiple solution.

9:17

Yeah. We have a good solution on portfolio,

9:19

mostly focusing on cost

9:21

optimization. and performance optimization.

9:24

And also, when we are focusing on

9:26

performance optimization, most of the time we

9:28

translate those kinds of optimization, those

9:30

kinds of performance optimization, into

9:32

cost optimization. There are

9:34

sometimes when we translate them into

9:37

improvement of the SLA and

9:39

competitive SLA and that stuff, but

9:41

mostly what care of the company that

9:43

we are working on, what really care about them and

9:45

what the biggest pain pain point of

9:47

that is the cost. how much they

9:49

pay for the cloud provider, how much they pay

9:51

for their infrastructure, and they want to

9:53

eliminate it. And I think

9:55

that that the few things on their

9:57

side, like, is it boarding to other

9:59

step? And the the the step

10:01

where the company gets to the point

10:03

where they need to understand, like, how

10:05

much they paying for stuff? Sometimes

10:07

it get, like, too far from the from

10:09

the point where it important on the

10:11

standard. And so we think the first and

10:14

foremost things to understand that cost

10:16

optimization is too important, then

10:18

that need to be handled. And

10:21

I think this is the the the most important

10:23

point

10:23

to to understand right now. Yeah.

10:26

Yeah. Well, and and I think and you highlight

10:28

something that's really important and this isn't

10:30

always obvious. You know,

10:32

what I've found having worked around

10:34

Kubernetes for a long time is, you know,

10:36

there's there's a period of time when when people are

10:38

just getting started. They're trying to figure out

10:40

the technology. And and oftentimes,

10:42

you know, they'll they'll look at other things.

10:44

They look at cost optimization

10:46

or or even security or stuff, and they

10:48

and they say, well, we only have one

10:50

cluster right now. I don't I don't really need to

10:52

worry about those things. And

10:54

what inevitably happens, and

10:56

we we see this time and time again

10:58

at different companies. Once they

11:01

figure out the basics of making things work

11:03

and they realize, okay, I can I can

11:05

help deploy applications faster. We can

11:07

scale them automatically. Like,

11:09

their ramp their ramp up is really

11:11

fast. And I and I think what you're really

11:13

highlighting is, while the

11:15

ramp up in terms of getting applications can

11:17

go fast, like, the cost can can

11:19

grow really quickly too. And you you you

11:21

need to be thinking about that early

11:23

in the process rather than waiting till you

11:25

get those first big bills and then not

11:27

knowing what to do. Yeah.

11:28

Yeah. Exactly. And as

11:30

I said, there there are several ways to to look

11:33

at that. there is a like, you you

11:35

can look at that in in many points of

11:37

view. The first one, you can look at that on

11:39

a cost visibility view. Like, where the money

11:41

goes to, which services are cost the

11:43

most. And what the resources that

11:45

derive the most cost and the the

11:47

like, wasting the the most of money

11:49

or investing the the most of money. The

11:51

second way to look at that is, like,

11:53

how do I manage my infrastructure better?

11:56

how do we manage that that the different

11:58

resource that that loads pools from

11:59

Kubernetes or autoscaling groups from

12:02

a a VM based application,

12:04

compute based application. And

12:05

our approach at graphite is looking

12:08

looking of that on a really unique

12:10

and other way from a point of

12:12

view of the application says As

12:14

the application is really, really dynamic.

12:16

And as the company, the the

12:18

massive growth, the rapid growth, as

12:20

the company grows, and

12:22

you it's becoming the

12:24

hardest place, the the the lost

12:26

densify area for the

12:28

company because microservices, age,

12:31

microservices, like, top every every

12:33

deal too in the environment. And

12:36

from a big program company, the trial

12:38

is, like, Right? Two to three deployments, it

12:40

become really very

12:42

fast, like hundreds of deployments and

12:44

multiple clusters and multiple staging.

12:47

and most of them in the production. So

12:49

and our approach is to look in that in the

12:51

application level layer, really, like, from the

12:53

application level perspective. Okay.

12:54

Yeah. And I was I was gonna ask you you sort

12:56

of led me into my next question. It's,

12:59

you know, there's there's always ways you can

13:01

focus. Do you focus on infrastructure? Do you

13:03

focus on on application layer.

13:05

You know, kind of walk me

13:07

through a little bit as your as

13:09

your, you know, as your products

13:11

and platform are looking at the application

13:13

layer. what are some of the common things

13:15

that that you see? And

13:17

and, you know, some of the areas that people often

13:19

make mistakes or or have, you know,

13:21

problems that lead them to higher bills?

13:23

Yeah.

13:23

Sure. So as I said,

13:25

our first solution were to

13:27

to to check there that to to

13:29

get cost reductions through performance

13:31

improvement So we gather first solution

13:33

in the portfolio, the the continuous

13:35

runtime optimization, which trying

13:37

to identify utilization pattern

13:39

in your application, and then by

13:42

identifying this button, we know how to leverage

13:45

all this button and open a button

13:47

next some bottlenecks in

13:49

your application. And that's most of the

13:51

time leads to performance improvement, which

13:53

we translated in the cost optimization.

13:54

But as we look in that

13:57

from the predictive point of view, And I

13:59

want that a little bit to that.

14:01

It's that looking on the competitive point

14:03

of view, it's like become

14:05

pretty different. It's like behaved really

14:07

different. because as

14:09

a as a competitive container's

14:11

work, if you'll improve the

14:13

performance utilization part of each

14:15

application utilization part, did

14:17

did did did not mean necessarily that

14:20

anyhow

14:21

your your computer, your cost

14:23

did improve. because Kubernetes, the the

14:25

way it works, the the scheduler part of

14:27

Kubernetes is just taking

14:29

those those atomic box

14:31

and which we call deployments

14:33

workloads, products, whatever, and

14:35

taking those atomic boxes

14:37

and trying to locate them into

14:39

the different nodes. And by

14:41

locating the those those

14:43

boxes into the the the node is now

14:46

that the orchestration, the command is

14:48

orchestrate the rock. Yeah. Schedule

14:50

the rock. And but yeah.

14:52

Yeah. So what we actually did, you know, for

14:54

our Kubernetes users, we

14:56

did the first phase of the performance improvement.

14:59

reduce the utilization part,

15:01

but has has the

15:03

like, we we show them the the performance

15:05

improvement They didn't achieve any

15:07

significant cost reduction immediately.

15:09

So

15:11

we we put our in investigator

15:13

and the research ahead and trying to

15:15

understand why why the immediate cost reduction reduction

15:18

haven't achieved. So

15:20

after investigating that and beat,

15:22

we identified the hover provisioning

15:24

gap. And by what I mean

15:26

by say over provisioning is that

15:28

there is the requested resource set from

15:30

Kubernetes, the the requested value. that

15:32

each deployment of prod like

15:34

set, and there is the utilization part of

15:36

that. And there is a huge

15:38

gap. Something's a huge gap. Something's a

15:40

little less, but there is a gap between

15:42

the actual reserve resource to

15:44

the actual utilized resource.

15:46

And without changing the the

15:48

requested resource, cost

15:50

reduction cost reduction can

15:52

be achieved because as I

15:54

said, credit is works on the atomic box

15:57

determined by the the reserve spot.

15:59

the And that's what you actually do. We're

16:01

looking at the application ever from the

16:03

requested resources at point of

16:05

view.

16:05

Yeah. It's it's a little bit, you

16:08

know, So sometimes in in technology, we

16:10

see the same problems come up again

16:12

from from one generation or the other. So

16:14

we we used to see back in the

16:16

days of virtualization people were

16:18

buying really big boxes

16:20

to run an application, and

16:22

they vastly underutilized them.

16:24

But because they just

16:26

knew they had overhead. They they

16:28

worried more about, okay, I don't want the box to be

16:30

overrun. And Kubernetes

16:32

came along and and sort of told

16:34

everybody hey, we're gonna we're gonna schedule

16:36

your stuff. We're gonna automate it. And everybody

16:38

went, oh, that that's great. That's gonna be really

16:40

good. But it's like you said, Kubernetes

16:42

does exactly what you tell it to do. So if

16:44

you tell it to take a bunch of resources to

16:47

deploy things, it's gonna do

16:49

that perfectly well, and it's gonna make sure you

16:51

always have those resources And I think

16:53

what you're really highlighting is some of

16:55

the same problems we used to have with

16:57

virtualization, which was people

16:59

tend to over provision things

17:01

just because they're not sure how the application's gonna

17:03

run, or they're worried about memory leak or

17:06

whatever. And and so we are sort of coming back

17:08

to the same same challenge again

17:10

just in a new, you know, in a new

17:12

environment. And and given

17:14

how fast Kubernetes works versus, you

17:16

know, the old days of virtual machines,

17:19

you know, you you need systems more paying attention

17:21

to this than just people with a spreadsheet that look

17:23

at it once a month? Yeah.

17:26

Exactly.

17:26

Like, when you start

17:28

using Kubernetes, you're assuming that everything

17:30

will work out of the box. Everything will

17:32

work perfectly. Right. And nothing's

17:34

special with Appen. Nothing nothing, you know, intervention

17:37

from your side should be appen. But

17:39

as

17:39

we, like, change that around, like, the

17:41

the the usage of

17:42

relatives, where they standard like,

17:44

there are two two main challenges for

17:46

us to related users. The first challenge

17:48

is to set those requested

17:50

resource sets for each container.

17:53

because if not if not like

17:55

something that you can understand prior

17:57

to the the setting of this value,

17:59

you can't really know what should be the

18:01

value. So it's most of the and I'm

18:03

setting the copy paste from another containers

18:06

since sometimes they tried an error. Right. But

18:08

they tried an error. There is

18:10

some risk try to get there. I was remembering

18:12

you get throttling. So,

18:14

like, this approach wasn't really hard

18:17

because like, most of competitive

18:19

users want to utilize both

18:21

their vertical scaling concept and their result

18:23

of scaling concept. But I can

18:25

use anything in, like, like, you're leveraging

18:27

both of them to improve. So to

18:29

get, like, maximum cost reduction, it become

18:31

really complex. So this was, like, the

18:33

first challenge that we faced. And the second

18:35

challenge that that we saw that happening

18:38

is like there are different

18:40

persons in each company that

18:42

in charge of different areas. So

18:44

if you know, competitors allow allow us to, like,

18:46

move some of their of their ability

18:48

to their engineers so they

18:51

can and beat it to fast

18:53

microservices. They can deploy it really fast.

18:55

There are CICD tools that do it for

18:57

you. And the but

18:59

have the the like, this

19:01

whole area of move to to the

19:03

engineering, and they need to to set

19:05

the value the values of Like,

19:08

the CPU memory presses for each

19:10

container, there are other departments,

19:12

which is the DevOps department that's in charge

19:14

of the budget. So, you know, as

19:16

they engineer infecting the values, they

19:18

had their hopes engine the dev of engineer, dev

19:20

of department need to,

19:22

like, give the explanation. Why is the budget? Why is

19:24

the billings that high? Like, the

19:26

the this bill side of

19:28

become, like, rechallenge in

19:31

many company.

19:32

Yeah. Absolutely. Absolutely. Granite

19:36

just recently launched a

19:38

a product service SaaS service

19:40

called gmeistro, your product. Tell

19:43

us a little bit about about how it works. You

19:45

know, what are the what are the

19:47

the most common ways that people start using it

19:49

and then some of the benefits they see?

19:51

Yeah. Sure. So first and

19:54

foremost, like, the the

19:56

the as I said, the motivation guys in France was to to

19:58

solve those two big challenges

19:59

that the the company that the community

20:02

has. And

20:04

What is

20:04

it? Are are

20:05

a solution? The GMS

20:08

solution? What actually, that is,

20:10

like, continuous right sizing. continuous

20:12

rightsizing your calls and your workloads.

20:14

And we achieved really good results

20:17

around sixty percent cost reduction from

20:19

a rightsizing operation. And,

20:21

actually, it's a pretty straightforward

20:24

concept. We just remove the gap and

20:26

reduce the gap and get the gap

20:28

between the utilize resource set

20:30

to their action to the reserved resource

20:32

set. And the way we do it in

20:34

technical matters, we just deploy a

20:36

single pod into a cluster First,

20:38

we provide a visibility, a full visibility

20:40

into the Kubernetes cluster.

20:42

I'm showing all the deployment

20:45

information, the deployment digitization, whether we

20:47

need deployment, roll out schedule

20:49

set, all the queries object.

20:51

And second, a few minutes after, we

20:53

provide the first recommendation, state

20:55

of recommendation on outright side

20:57

of the workload in your capabilities.

21:00

And we provide you and we generate

21:02

those kind of recommendation. by

21:04

checking out all the peak usage

21:06

on your workload, taking

21:09

into consideration out of

21:11

memory and throttling effect that

21:13

already happened. and checking,

21:15

like, we want to make sure that your

21:17

that you don't need to compromise on your

21:20

competitive SLA. And that's actually how we

21:22

build the recommendation. We build the

21:24

recommendation for you. We show it to you.

21:26

And the continuous part of that, that

21:28

we allow you to apply this recommendation with

21:30

the click of a button, and we make sure

21:32

that every time your workload

21:34

change, evicted, pretty dynamic.

21:36

We right sized your workload again

21:38

or over again. So you

21:40

always, like, use the the the

21:42

list resources needed. to

21:44

end up the load that the workload need to be ended.

21:46

Okay.

21:46

Good. So it's it's not a not sort of a one

21:49

time scan and and, you know, it's a it's

21:51

continuous sort of it's

21:53

watching what's going on, making recommendations, and

21:56

and are the recommendations done

21:58

on a on a dynamic

22:00

basis? Does it sort of see what's there

22:02

and and fixes it for you

22:04

or or gives recommendations back to

22:06

the to the user and and they, you

22:08

know, they they can decide what changes

22:10

to make or what you know, what areas to

22:12

evolve? Yeah. So

22:13

it really depends on the user. It

22:15

can take those recommendation. We

22:17

provide the the required yummin

22:20

changes to provide the patch command to

22:22

to update the workload

22:24

deployment that the recommendations

22:26

will generate their own. But If

22:28

you don't want, we can apply them

22:30

for you for them, sorry, and

22:32

ensure that and I said it is continued

22:35

solution. We ensure that every time that the the vocalist

22:37

changes at the time passes and the

22:39

utilization part of change as well,

22:41

and there are, like, different peaks and

22:43

different spikes, We can take that into

22:45

account and make sure that every time and every

22:47

change that the world changes, we are

22:49

making sure they said that

22:52

the the workload remained

22:54

the right side.

22:55

Now do you find do

22:58

you find some optimizations or

23:00

are sort of more immediately impactful

23:02

or, you know, some some are more

23:04

focused long term. Like, how do

23:06

once once people sort of see what's

23:09

available to help them, you know,

23:11

how do they tend to to think through it? Is

23:13

it, okay, these are these are immediate

23:15

because they're just you know, they're they're too big

23:17

and bulky and then others are

23:20

you just you're making little tiny

23:22

incremental things over time. What what

23:24

tends to happen as they as they see the optimizations

23:26

presented to them. Yeah.

23:27

So most of the

23:29

time, people, when they feel recommendation, they want

23:31

to figure out how we generate those

23:33

kinds of recommendation. after checking your

23:36

recommendation of tool and making sure

23:38

that this is a quality recommendation, they

23:40

can apply. And most

23:42

of the time after applying those recommendations, they cluster when

23:45

they just cluster autoscaling, autoscaling,

23:47

direct magic, and reducing

23:50

the loads the loads amount.

23:53

Sometimes when the cluster is like

23:55

a static cluster, we need to change it

23:57

manually and we provide a recommendation to change

23:59

that manually for you as well. But more

24:01

than the time competitors does it magic by

24:03

itself. But I think the the

24:05

concept of rightsizing is,

24:07

like, after you right side your

24:10

workload one time, you need to make

24:12

sure to right side your workload every

24:14

time and continuously. Since if

24:16

your workloads wanted to try size in the

24:18

first place, and it can gather out

24:20

of your room, it can end up throttling, And

24:23

there is also a lot of waste in terms of resource.

24:26

But once you write in the

24:28

workload, we need to make sure that it remains

24:30

right side over time. So,

24:32

like, the first impact should be immediate. And

24:34

as the time goes, you just make sure that

24:36

you don't raise much more resources,

24:39

and you're running in the most optimized

24:41

way. Gotcha. Gotcha. And and

24:44

Jim

24:44

Maestro is is deployed as a as

24:46

a SaaS service. Correct? Yeah. Yeah.

24:48

It's a SaaS service. You

24:51

can you know, open our platform,

24:53

you know, from the environment

24:55

side, open the GMX platform, sign up

24:57

with the screen offering. You can use it

24:59

for free, check out your recommendation, and

25:02

start using it right now with multiple

25:04

cluster on multiple application as well.

25:06

Gotcha. And and will it work for obviously, it'll

25:09

work for workloads

25:11

Kubernetes workloads in the cloud? Will it also

25:13

work if a if a cluster is is

25:15

on premises? Yeah.

25:17

So if you have,

25:20

like, an Internet connection from your

25:22

over VPC from your own premises and you

25:24

can allow an outbound

25:27

networking. It will be no problem to to

25:29

connect our view management to your

25:31

platform. Okay.

25:31

That's excellent. That's excellent.

25:33

So we've talked a little bit. Obviously, we could

25:35

we could spend a lot a lot of time going

25:38

in-depth. For people that are are looking at this and they're

25:40

going, yeah, we're we're beginning to see this

25:42

this sprawl of clusters in our kubernetes

25:44

environment. We're starting to, you know, get some

25:46

bills that we don't love what are

25:48

some of the best ways for,

25:50

you know, companies to engage with not

25:52

only the the GeoMistro platform, but

25:54

also potentially engage with

25:56

your team. Sure.

25:57

So they can open the open

25:59

our website, but I dot I'll check

26:01

the different solution we have. They

26:03

said their runtime optimization solution,

26:06

and Paul, the ride saving solution,

26:08

Jim Mastro. And Jim Mastro,

26:10

it's pretty you you definitely need

26:12

to sign up and deploy our our

26:14

Jim Mastro pod and then

26:16

you already get any sort of a combination. You can use

26:19

it on multiple cluster. You can use it

26:21

on single cluster, whatever

26:23

they they desire. And they

26:25

are the solution that you just need to schedule a

26:27

demo with one of our experts.

26:29

Excellent.

26:29

Excellent. Yeah. It's the other thing

26:31

I think it's it's really interesting, you

26:33

know, you you granulate is an

26:36

intel company. Back in the

26:38

day, Intel used to make a lot of

26:40

investments to drive

26:42

more and more CPU usage, you know, video

26:44

and and other things. It's it's really interesting

26:46

to see them evolve to now looking

26:48

at, you know, how do we be really efficient

26:51

with the the CPUs that you're using, the compute that you're

26:53

using because, you know, it's

26:55

it's so easy now for people to

26:57

to drive more usage. There's lots of business projects

27:00

that drive more compute usage and, you

27:02

know, AI and and data science and stuff. So

27:04

it's it's really good to see them

27:06

being on the other side and giving people

27:08

access to tools that that make them efficient

27:10

as well. So that's that's exciting to

27:12

see. You you know, last

27:14

last question, you know,

27:17

what's the best way you know, give us an example, what are the

27:19

best ways for folks to to engage with your

27:21

team? What are some of the, you know, kind of earliest

27:24

ways to beyond just

27:26

signing up for the account? Do you see

27:28

what are maybe some of the immediate types

27:30

of things that you hear feedback from from

27:32

people like, hey, we just save a

27:34

bunch of money or, you know, hey, you know, we we were

27:36

surprised we we could optimize some things. What are some of

27:38

the early, you know, stories

27:41

you hear back from people using the

27:43

platform? Yeah.

27:43

So we launched our

27:46

platform around there months ago. We got some pretty

27:48

amazing feedback from the community.

27:50

And we have a Slack channel. like

27:52

a like a platform for the

27:54

community, which we ask the

27:56

questions from the community, we have we

27:58

answer for any support provided

27:59

over there, but if

28:01

someone need to contact us, he's a

28:03

great child to do so.

28:06

But we got great feedback.

28:08

We we we are already checked that platform

28:10

before releasing it to the to global

28:12

available. We checked that in our

28:14

internally customer current customer, and

28:16

we achieved amazing

28:19

amazing cost reduction around an average

28:21

of sixty percent It

28:23

depends on how much the

28:25

environment was over provisions before our

28:27

first solution. but most of the time it

28:29

was over provisioned, as I said.

28:31

And I think the

28:33

best way to contact us just

28:35

join the community, join the Slack community,

28:37

talk with us. You can send me a

28:39

private message. You can send me a link

28:41

in link in message. And

28:44

But this will be the best way to listen. Excellent.

28:46

Excellent. Good stuff. I appreciate the time

28:48

today. It was really, really good not only to speak

28:50

to you again, but also to learn more

28:53

about what granulates and what the

28:55

GeoMistro platform is doing. So folks, as

28:57

always, we've been highlighting

28:59

more and more around Kubernetes, not

29:01

only how to run run the

29:03

platform, keep you up to date with the technology. But

29:05

with that, we