Episode Transcript
[00:00:02] Speaker A: So welcome everyone to a new episode of the FinOps weekly podcast. Today we have a very interesting interview with Ido Kohler, the CPO of Pelanor. How are you, Ido?
[00:00:16] Speaker B: I'm good, how are you?
[00:00:19] Speaker A: Doing great, doing great.
Today it's going to be super interesting podcast about AI and phenoms for AI, AI for Phenoms and all this stuff that you should stay tuned to, know all the insight that we have prepared for you together with me, Damien Monafo. How are you, Damien?
[00:00:38] Speaker C: Doing great. It's always a pleasure to be here and talk about interesting things.
[00:00:47] Speaker A: Yeah, it's been probably the word of the year and it's probably going to be one of the more difficult words in the events that we have in finops now.
So, you know, from someone like you that are in the core of the FinOps ecosystem, what do you think is the distinction or the difference between FinOps for AI and AI for FinOps and which of these two excites you the most? Like, what's your preferred part from this?
[00:01:21] Speaker B: Yeah, that's a great question. And I think AI is probably going to be like the word of the decade, not only of the year, with the pace that things are going right now.
And I think both FinOps for AI and AI for FinOps are super interesting topics.
So FinOps for AI, I think it's really in the early days of that. So that means companies using AI, both for example, when developing and then creating new code, using AI, but also using AI services. So for example, using OpenAI or anthropic or AWS Bedrock and all of those services, and also training their own models. And that's becoming more and more of a big issue for companies and a big spend. And if you're looking at, let's say, the forecasts of companies like AWS or GCP or Azure, then you definitely see that people are predicted to spend a lot more on that. So it makes a lot of sense that finops will take care of that. And on the other hand, AI for FinOps is completely different. So it's not about doing FinOps on more things, but it's about completely changing the way we're doing finops. Because like, in the same way AI has been a revolution, for example, writing code or generally across many of the SaaS platform industries that we're in. I think in Finops it's going to be a drastic change as well. We're starting to see it happening even today. So that means completely changing and augmenting the workflows that we're currently doing to automate them a lot more using AI. But it also means understanding what we can do more what used to be impossible but now becomes possible through the usage of AI and how that can enable finopstream to be even more central within companies and push for much more effective resources.
So between the two we tend to think, and me personally, but both Pelanor at large that AI for finops should be what we're tackling right now and not finops for AI. I think mainly because currently like if we're looking at 20, 25, most companies are still experimenting with AI and I think it means a lot about the place of finops within there. Both that there isn't necessarily a lot of spend yet, but also that that spend isn't really thought of as something that should be properly managed right now because even leadership thinks about that as like let's understand what's working more and working less and let's find out if we can get more market captures through doing those things. And that's not like a good point in time to try to like for the FinOps team to get involved and we see a lot of people like having issues when they're trying to push their leadership on that too much. But I think even more interestingly it's because the pricing models are not really set yet. So that means that the way people will use AI in two years is probably going to be very different than how they use AI currently. So the way they are paying for things, the reservation models, if they're going to train it or use open source models or use SaaS models and if you, if it's going to be like one company or a bunch of companies and I think that means that trying to create a process out of it too much is going to be incredibly difficult right now for many finops team. So at least our opinion is that we should wait a bit for things to be a bit more stable and in the meantime we can focus on changing a lot of other things with AI and making the current work that people do way more effective.
[00:04:37] Speaker A: No, that's, that's probably a good answer and I think it will probably surprise a lot because you know in the, in the feedback that we got is depends on, on who you ask. People are more worried about, you know, applying the finops for AI and you know, control that but that you oriented on the, on the AI for FinOps and how you know, AI can help on the phenips. It's, it's very interesting. Right.
So Damon, do You have any, any follow up for, for this?
[00:05:06] Speaker C: Well, it's interesting to see that today. You know what I see people using AI for F is when you ask, I play a little bit in the with Amazon Q and you were asking, you ask like give me the top 10 or what is the most expensive surveys and stuff like that considering the we are in the beginning of that and I would like to see again how, how it's developed.
And yes, a lot of we see also a lot of traction on phenol solari, how we're going to as phenops, you know, practitioners, how we're going to apply that.
But it will be interesting to see all the things that, you know, a lot of talks about agents are being there.
I wonder how that will be applied in. What do you think how that would be applied in finops?
[00:06:11] Speaker B: Yeah, that's a great question. And I think what you're saying also is very correct that a lot of what we're currently seeing in AI for FinOps is a lot of hype like the Amazon Q example or the Copilot where me personally, I don't think what they're doing necessarily is super useful because it's super basic. So it takes the data, the most basic data that you have today and it shows you that in a completely new chart that's for example not interactive and doesn't have to do with your current workflow. And I think it's a good starting point but I think it misses the key things that AI can really do for FinOps which is actually finding from the breadth of data that company have and contextualizing those things automatically and creating much more interesting explanations. And I think agents are a part of that. So both for example using things like MCP servers and creating interactions to go automatically and using the more advanced AI models they think more broadly it's about getting the right level of explainability such that everyone could access this data. And I think that's for example, let's say a finance person wants to use Amazon Q or a finance person wants to get an answer to something that's going on with FinOps. They're probably not going to know the EC2ID to ask Amazon Q and tell them like oh look at that resource and tell me what happens and then look at the CSV of the result of that. So the way they could get more value from AI is if they could ask questions in the way you are used to ask questions. So let's say talk about margins, talk about the unit economics of their business Talk about the trends that they are seeing and asking for explanations and then get answers also in terms that they care about. So not in terms of this is the EC2 IDEAS and this is a CSV of its cost day by day, but in terms of who's the owner of that, which project it relates to, which customers it relates to. And that's what makes me so excited about that. The ability to democratize, to contextualize and do that, even if those people are not really experts in what they're doing.
[00:08:15] Speaker A: That's, that's, well that's, that's super interesting. And I think that yeah we have seen that at the beginning, the copilots, the queues, they are very helpful at some point, but it have a limit. And also it's not like the way to scale is not that high because you cannot do anything at the scale with a copilot or with a queue. It's very helpful on several sides. But the like the agentic system is way better and talking about the agents and evolving a bit about the convolution. Like you know, the agents are super great, but we have seen that they could make mistake, like they could be great, but they could delete a database or make like if they there's not like manual control. So where do you think like the line of identity is and the improvement, do you think, or how people should use it to be better on, on these agentic AI systems?
[00:09:08] Speaker B: Yeah, I think even regardless to AI for FinOp or FinOp specifically, I think the trends that we're seeing with AI and let's say with VIVE coding and people generally using very automatically generated code is extremely scary. From finop's perspective, the ability of someone to just press tab a bunch of times and then accidentally having 100 CPUs instead of 10 CPUs because they didn't even look at the hand chart they wrote is something incredibly scary. And I think it's even becoming more scary when some companies, for example, talk about automatically changing your codes, creating PRs, reviewing them and pushing those things to production. And my ideas on that is that AI should augment what people currently do, but not take the actions itself at the end of the day. So that means for example, because the bottleneck is never someone, let's say reviewing a PR and pressing a pro, the bottleneck is often that people first of all don't really trust the insight that they see. So they see for example that a database is not utilized enough, but they want to check if maybe there is like once a week that this database is utilized. And if they're going to resize it, then it's going to make an issue. Or maybe this is like a test project, but in two months this is going to scale up and the usage is going to scale up. So we should keep the right size right now because otherwise in the future it's going to create performance issues. So I think the key thing is for every insight, and this is like a basic example, let's say a bright tightening of a database. But even for more complex things, you need to know the context of how it interacts with other systems, who's the owner of that, get the right question to those people, get the answers and then understand if this action can be taken or not. And I think that's exactly where we're heading to with AI as well. So automatically investigating, integrating with all of those external systems, getting this data and wherever it doesn't have the data, sending a slack message, requiring a response, opening a JIRA tickets, doing all of those extra actions that get the right people involved and then kind of automating that process. And then at the end of the day it could also allow you to see where is the part of the code that you need to change, etc. But I think that's like the easiest part within the process because usually when you have all of the answers, things are quite clear.
So I think that's the way that's kind of involved as far as like remediations and not as far as like automatically creating patch and running like Terraform modules in your production environments.
[00:11:41] Speaker A: I hope that it doesn't do all of that because even if I do mistakes when I do run like Terraform code, I cannot imagine what an agent on like auto doing everything, what could the mess that they could make for Terraform.
[00:12:04] Speaker B: Yeah, for sure.
And I think we, by the way, we also see that today like even in examples that we see within customers that sometimes you can see, like currently it's even like very, very basic examples because I don't think it's that integrated within systems. But for example, we saw a huge spike in one of our customers for like a query that ran on some data lake within their infrastructure that basically costs like tens of thousands of dollars for one query. And when you look at that, it's pretty clear that that query, that SQL query was basically AI generated and the parameters weren't good enough, it scanned way too many tables, et cetera. So I think you already kind of see those issues today that obviously also existed with some analysts not being proficient enough, but I think you definitely see them more where people are just copying and pasting. So that's definitely going to happen more and more.
[00:13:03] Speaker A: Damien, do you have any stuff you want to ask?
[00:13:06] Speaker C: No. I was thinking also that at this point I will trust AI also as something that help you organize and help you nudge the people, which will be like a chatbot, like an agent that they help you prioritize based on past experience. I totally agree with what IDO was saying, but just thinking about something else. Ido, you come from cyber security background.
Do you also see the finops like in the first step of security, in which the security guys were like these people that were bugging you and today it's more of like, you know, something that is fully integrated into all our system.
Do you see finops the same and do you think that, or when do you think that it's going to fully integrate in all our flows?
[00:14:07] Speaker B: Yeah, that's super interesting. I think there are many parallels between finops and security.
And I think by the way, you see even like with companies starting today within the FinOps space, that many of us founders came from security background. I think it's, it makes a lot of sense. So I think very broadly speaking, if you're looking at the cloud and the way people think about the cloud, so probably DevOps observability tool are on the forefront of that. All of those open source that really invents what we consider cloud today are definitely the first line of defense on what's really going on in the cloud. I think cybersecurity was a close second to all of that, mainly because when something new happens, there are risks to it and people need to take actions on that immediately. But I think finops, at least up until recently, was kind of left behind. So many of the vendors were created at a time that, for example, you couldn't even access the Cost Explorer in aws, but you had to download a bunch of big Excel files. So obviously having a basic BI tool was enough. And those people were like BI people, but they weren't really cloud people. And you see that with the fact that they were very late to, for example, understand Kubernetes properly or understand data lakes properly, or understand how do things interact with one another. And I think one of the key things that we bring from cybersecurity is that understanding that the cloud is much more like a graph or a map of resources and not really like a table for finance and that's it. And that things really have a lot of interactions within them and that it's kind of alive a lot more than people think with how things are constantly being deployed and evolved. And you need to take that shift left approach and correlate that with what engineers are doing. I think that really is why also we're seeing a lot of the trends you're currently mentioning of like pushing things more and more to the engineers, getting them involved and getting them to take actions. And I think that's also when you see a lot of the problems that used to happen in cybersecurity also becoming a real problem within finops. So I think like the most common problem people talk about in cybersecurity is like the noise of alerts and the fact that there are many things that are irrelevant. All of the tools throw you like a million ticket and you don't really know what to do. And, and I think recently you see those same trends, especially when engineers are the one facing those tickets in FinOps as well, where people are saying there are too many anomalies, there are too many recommendation opportunities, what can I do, how can I prioritize, does it make sense, is it a false positive, etc. I think a lot of the cybersecurity mindset is also about what are the good tricks to look at that and find the needle in the haystack or to find the interesting thing within the noise of these alerts. And that's also a lot of our focus as well, using AI actually, because AI is incredibly good at smelling what's not good enough.
Let's say you had an anomaly. It's really hard if you're just using a forecast model to look at, let's say, a cost that happened, let's say a marketplace purchase. So you bought through a marketplace, some tool. It obviously looks like an alert. Any tool is going to say that's an alert. But using AI, if you bring the context of that to an AI layer, it easily can tell you that that's not something that's really problematic, but probably like something that the business is doing and is not. Like something that you should open an urgent issue on, although it's like 100k of you spend on a day because it relates to a marketplace, it relates to a vendor. So contextualizing those things is actually like another extra layer of both reducing noise, both compressing things to look into many unrelated alerts and, and combining them to one big incident that have all of that context together, but also to make those alerts or incidents or recommendations much more actionable by the People getting them and routing them to the right person. And I think that's definitely a lesson learned from security. And you can see that in many security tools as well today.
[00:17:59] Speaker A: Yeah, yeah, indeed. And also like the actionability and the remediation and all this, you know, interaction with additional components, it's a different. And also the feature that is more common. And I want to add one thing.
[00:18:13] Speaker C: Sorry Victor, there is a good thing that you brought up. Either it's the false positive. When we all the time as a practitioner say let's put not only anomalies, let's put budget alerts, right? But when you put budget alerts, where do you put it? Like 10%, 20%, 50% and make sure you're getting all these, all these nets, you know, that doesn't give you anything. First if you don't correlate it with time, which would be one way to avoid it.
And secondly, when you have, you know, now with this direction of multi account session, you have so many accounts, so many, I'm sure you get overloaded.
[00:18:50] Speaker B: But this is just, I think the main problem.
Yeah, like I feel the main problem with alerts is like at what level of obstruction you're looking at because let's say you're alerting on every single resource. You're doing like for every resource ad see if it changes. Obviously you're going to get both a lot of false positives because let's say there is an auto scaling group that turns off and on new machines all of the time you're going to be alerted on any new machine if it's like big enough. But also it means you're lacking the context because usually this machine went up, but also this bucket went up and also this networking cost went up and they are related to one another. And if you're getting three different alerts you're missing the full story.
But on the other hand, if you're looking at things like did my cost of this account went up then usually it's really hard to know what happened because even if you see there is a change in the account now you need to go in and understand what service it relates to, what team, which resource, etc. So I think the way we're looking at that is that doing alerts on the higher level things makes more sense if you can once you get an alert, explain it really, really well and, and find the root cause. So we put a lot of effort that whenever we have an alert, for example in Pelanor, we automatically analyze all of those things and Find all of those mini alerts within it that really trigger the cost change and then tie it all to that one big incident and one big story of that what caused the change within your testing account and then it makes those things also much more, less noisy and more actionable.
[00:20:26] Speaker C: And learning Also it's really easy to do it because if you're doing a report eventually it's very interesting.
[00:20:32] Speaker B: Nice, thank you.
[00:20:35] Speaker A: Yeah, for sure.
And yeah, as mentioned it's really difficult because for someone that, for me, myself that I come from an infrastructure team that is very, you know, you get a lot of alerts from Kubernetes, a lot of stuff and being able to differentiate yourself like differentiate what's a good alert from a noisy like you know, a pod over there and it's that but it's not meaningful. So the way that you approach making an interesting change or what has changed or what is an actual problem, it's, it's a great approach and thanks, thanks for sharing that and I'm sure the Polo can do like I would job. But in diving more into, you know, the long term or what we think that the long term benefits would be for AI, like in FinOps, what do you think it's going to be the long term benefit from AI or what do you think it's really going to be changing apart? Like what's going to be empowered? Is it going to be like a FinOps augmented professional? So let's say less manual work and more AI automated, more oriented on the agents. Do you think we are going to have an agent team of finops and only one human or something controlling it? What do you think is the approach that we are going to have with AI?
[00:21:56] Speaker B: So I think there are going to be two things happening simultaneously which are incredibly interesting. First of all, there are so many menial tasks many Finops teams do today that are going to be completely automated and that's going to be great.
So instead of like for example, there are many people I work with that spend probably half of their week chasing teams about cost changes and anomalies and recommendation opportunities and verifying what really happened, does it make sense? Can you make this change, et cetera. If all of that like investigation time, finding the right owner, et cetera is going to be automated, it's going to save people a lot of time. And also when regarding the more let's say reporting aspects of FinOps, if it's going to be very easy basically with the click of a button to describe what report you want to get, get that report and also get the five most interesting trends that happened last month compared to the previous month or last queue compared to the previous queue, and then present that to leadership without taking two weeks every quarter of creating that presentation. That's also going to save plenty of time to what many of the teams are doing today and what's expected expected of them. And that's not even that far away.
We see many of those insights even being generated today with current finOps toolings or with AI augmented finOps in some ways. But I think if you only look at that, you might think, oh, we're going to need less finops people. But I think that's not going to be the gist of it at all. Actually quite the opposite. Because the thing is that Today hiring a FinOps person is often their impact is limited because they are drawn to all of those things that not necessarily have incredible value for the organization, but take a lot of the time off that person. That probably limits how much you can even pay on finops. Not necessarily on a specific person, but if I bring one more person to that FinOps team, it's not going to necessarily bring a lot of values because they're going to be blocked on getting all of those answers and they aren't going to bring necessarily that much value to your organization, but unlocking that possibilities for those people to ask much more higher level questions. So to reason about what sort of architectural changes might be needed or how does the unit economics of this feature really works and does it make sense and where should we put more efforts or be involved much more earlier on within the process of developing a new feature. So when you both unlock the time for people to get involved in that, but also give them the capability to be way more powerful with the time to be able to do that properly, then it unlocks a lot more new opportunities for what finops means for those organizations and make these teams way more central. So I think that's also probably a trend. We're gonna see that these people are gonna have way more power to influence what's going on within the organization because these issues are critical. Like in the end of the day, people are spending millions, hundreds of millions of dollars on cloud and Getting even like 5% more efficient, 20% more efficient is incredibly impactful for the company. So it makes sense to put resources on that if it makes an impact. And the ability to make an impact is going to be much larger when you have more tools to do that properly.
[00:25:08] Speaker C: Yeah, very good. Yesterday we had the Spanish summit and we Were seeing a very nice, you know, demo about, you know, where, you know, the AI is here. And we just discuss about it that the AI at this point is not to replace us, not even close. But it's there to make us better, to help us be more efficient also.
And that's, that's a very good point. I always say that unless you have like a robot that is, you know, mingling with people until that happens, I don't see how AI and something else replace us. I think that we are still valuable still. We have a long, a long to go a long way.
[00:25:54] Speaker B: I'm sure, I'm sure we'll get robots soon as well.
[00:26:00] Speaker C: Yeah, but think about it.
And by the way, I was thinking about it while I was, you know, thinking about what, what I was saying. But even robots eventually think about it when a robot is going to come. Eventually. I think we are a little bit shifty, but eventually will come also like people, we're gonna, you know, it's gonna be like, think, oh, that is a robot. I don't know, it's gonna be some kind of like, you know, distrust, you know, it's not gonna be trustable. And still I think we have many years there to go until, you know, AI and robots replace us.
And that's good.
[00:26:34] Speaker B: Yeah, we have some time.
[00:26:38] Speaker A: Yeah, yeah, for sure. And I wanted to dive into an interesting topic because when, when we talked in FinOps and Barcelona, then we get to know each other and you tell me that you were champion in debating and you are very experienced in communication and you can tell more now to the audience because I think it's a very interesting story. But related to that, communication discussions in FinOps is a very well known topic. And especially one of the main things is getting executive buy in. So from someone that has a lot of experience and a lot of skill in communicating and convincing people, how do you think these skills can be of help to FinOps and how has it been helpful for you in your career and you can explain a bit about your story because I think it's very interesting.
[00:27:35] Speaker B: Yeah.
So as you've said, I do have some experience in debating. So I've done like university debating and debating at large for four years in my past, both like from Tel Aviv University where I studied and generally more globally. And in there I basically, I won the Israeli championship, the European championship and many other tournaments and then also helped organizing some of those tournaments for other people.
And I think people usually when they think about debating, they, they think about like people who like to Argue they think. Actually it's quite the opposite. It's more about people who like to listen and find out like what the other person really means. Because the way debating is, is being done and being adjudicated is mainly about engagement. That's like the words we mainly use. So finding out the main like points of contention and then talking specifically about them and finding out what did exactly the other person say about that and what would change their mind about that topic specifically.
And I think that kind of mindset is often needed, both in finops but at large, because usually people come with their own perception about what's going on in reality and then they end up talking about two completely different things and not really listen to each other and then they can't really get to any agreement. So it might be the case that the FinOps person tells an engineer, you could have saved so much money, why didn't you do it? It wouldn't have taken you so much time. And the engineers tell them something like, but I have my tickets, I listen to my product manager, I can do those things. And both sides are going to be like, especially if things get a bit of emotional, then it can be a bit challenging. And, and I think that the key thing in many of those things is find out the incentive structures and find out what really drives people to do things, then finding out who can make those decisions. And I think that's also like, for example, why a lot of of my focus when helping organization integrate finops to their processes is about creating accountability across the organization. So I don't think it's necessarily about just like chasing people on slack, but it's more about making sure that teams feel accountable and creating things that people can agree on and say, I really drive this cost, I'm really the owner of this cost and therefore I should be the one that looks daily or weekly on the trends, on the opportunities and finding ways to reduce it. And once you have that, then all of the communication becomes a lot easier. But together, usually there are a lot of processes around like agreeing on who really is in charge of what, how you should allocate things like shared costs, etc. And for that I think that definitely people have to come prepared. And I think maybe if I have like one tip, and I don't think it's like from debating, but I think it's more like about how business is really done, is that whenever you're coming to, to a meeting where a decision like that needs to be made, let's say meet with a lot of teams and kind of split ownership on some resource or create a budget and come to understanding about budgets. Always talk to the important stakeholders before that.
So make sure you get to a one on one agreement because getting to one on one agreement is like a lot easier usually. And once that happens, then the big meeting and the important meetings become kind of like very quick and very easy and then you don't really get all of those disagreements that create an explosion and then nothing really happens and things take months because you can't really reach a consensus about those things. So I think that's definitely the thing to do both in finops but even more largely.
[00:31:16] Speaker C: Maybe you can share with us some links to improve integrating.
But I totally agree. You know, there are sometimes that people ask me what are the skills that I need? You know, do I need to know how to program? Do I need to know that first of all you need to be as a phenotype, you need to be a people, a people guide. You know, you need to get along because eventually your finops is in a point that you need to communicate with so many different teams and talking to the type of persons that is the first skills you need less if you know how to, you know, create a lambda or any automation.
More important is people, people skills. So definitely, yeah, for sure. Maybe we talk more about it, about debating.
[00:32:02] Speaker B: Yeah. And I think it's very important to kind of remember that most people want to do good and most people want to save money. Like engineers are not like the enemy. And definitely finance is not the enemy. Not everyone's trying to harm you or harm the business or like waste your time or their time or close your project or whatever.
But it's about what they're being measured on and what can they also use for their own goals. So talking to them, understanding them and helping them create a short story about how you can both help them and help your goals is probably the thing to do whenever you're trying to push a project within an organization where the ownership structure is not really clear like finops usually is in most organizations.
[00:32:51] Speaker A: Indeed like the human factor in finops is one of our crucial things. And being able to communicate itself and being able to put your arguments in place and also especially relating to people and being able to empathize and to put like what's the benefit from them to do this? Or what's the benefit from the company and how they are going to impact. I think it's, let's say one of the sectors that is easier because it's linked to business and it's linked to margins and it's easier to highlight the numbers but also like how these people should be interested in that and not in availability or whatever.
So thinking about this, thinking, you know, what we've been discussing, you know, we like to do some predictions or some comments or some, you know, thoughts from the interviewees about, you know, what's going to happen in five to 10 years in FinOps? What do you see FinOps in the next year? So I want to know like what do you think is the piece meeting in the next five to 10 years in PhenOps, whether or not what's going to happen in Phenops and something that you don't think people is talking about and maybe how, you know, Pelanor is approaching or highlighting that. Do you have any like crazy or prediction that do you think nobody is talking about in Phonops?
[00:34:13] Speaker B: So I think basically that probably the unit less dollar is gonna die. So like what you've said that people, it's easy to talk about it because it's numbers. I think the numbers we talk about today could be considered meaningless or almost meaningless and will be considered almost meaningless. Like saying we've spent, I don't know, $2 million on AWS last month isn't gonna be like the conversation, but it's more gonna be about the cost per value and that's the way people are going to change the way they're talking. So the cost per API call, per customer, per interaction, they think we're already heading there in some sense. And more and more platforms are changing even their pricing model, their basic pricing model to be that moving even from users in SaaS or a license to something that's much more based on a specific interaction. And that's also going to drive the business to talk about things in that way. Because at the end of the day, if your business is growing and if you're getting more value, then it doesn't matter that you're spending more money. I think by the way, especially with AI, it's going to happen more and more that you're going to spend a lot more but get more money because people pay you for that interaction. So the thing that should make sense a lot more is to talk about how that changes over time and how that trends with new feature releases or with new model releases if you're using a new model or with the patterns that people are using your tool. And that conversation only makes sense when it relates to unit. So I think people and finops and leadership is Less going to talk about how much we spent but more about are we doing that efficiently, how much value do we get per dollar is spent and that's going to be a massive shift that hadn't happened yet in most places.
[00:35:55] Speaker A: That's a good one. Haven't thought about that. But yeah, if you relate to what A is doing and you know, how the credit system is working for APIs and all these, like what Claude or what OpenAI does, I think it makes sense and I hope that especially once the provider, you know, after all the providers do relate based on service consumption in some places.
So I think they only need. The only step away is linking it to business value easier and being able to provide the unit economics in an easier way.
I hope AI can help on that and automation can help on that. But yeah, I also agree that finops is always, I always understood since the beginning that is linked to business value and it's not about the cost itself because cost can go to zero if you don't have anything in the cloud.
[00:36:48] Speaker B: But.
[00:36:50] Speaker A: If you go that way, we need to be better at automating unit economics and to, you know, being able to link it. And I would say that probably the Cloud GU or the FinOps ecosystem should evolve to that. Like, okay, this is related to this business. Okay, let's make that API related cost or let's make it more relative and not like the absolute huge mega build from AWS or from any of the providers.
[00:37:20] Speaker B: Right?
Yeah, exactly. And the fact that it's not simple, the fact that it's not easily taggable, doesn't make it less valuable. I think that's where a lot of our focus is in Pellano, making that process more autonomous and more easy. But irregardless, I believe it's something companies should definitely consider doing internally because it brings a lot of value when you do it properly.
[00:37:45] Speaker C: Especially we need to go into a world that is.
Does it make sense? Why does it make sense from business perspective, for usability perspective. And yeah, it's good that we are going in that direction.
[00:37:58] Speaker B: Indeed.
[00:38:00] Speaker A: So yeah, I would like to first of all, thank you for having the interview. I think it's been a pleasure to have you today and I don't know if you have any quote or anything golden minute to share with the audience and if you have any message to share before we wrap up and pleasure to have you.
[00:38:24] Speaker B: Yeah, it's great being here and thank you for inviting me.
[00:38:34] Speaker A: Pleasure to have you, Ido. And thanks for sharing your insights on the AI and Thanks Naaman for for coming back today and hope you you guys enjoyed the the episode and see you in the next one. Bye bye.
[00:38:48] Speaker B: Bye bye.
