It was very clear that we don't want a customer like Apple. If you go bring a very large customer very early on and that customer is going to force you product in a direction you may not want to go. Apple as a customer more than likely is going to dictate your road map. You have scaled Cloudflare employee number 45 and you saw the companies grow from 200 million in valuation to public market cap of $60 billion. Your choices of companies have been great. You are early member in Splunk, Cloudflare and AI.

I'm a big believer that if the market has a big opportunity, you as a company will get lot more chances to make it work. If you're in a in a market with a small dam, you do one thing wrong and you're out of business.

Your playbook of building the GTM at Cloudflare.

One of the obsession at Cloudflare was how to provide the value of the customer really, really fast. The holy [ __ ] moment we talk about would happen really fast with Cloudflare. Hi, this is Sadhhat Alawwalia. Welcome to the Neon show. I'm your host and also managing partner at Neon Fund, a fund that is invested in some of the best enterprise AI companies between US and India corridor like Atomic Work, Clouds and Spot Draft. Today I have with me Manish, president at Arise. Manish, welcome to the Neon show.

Yeah, good to be here. Thanks for having me. Uh, your journey is, you know, amazing. You, you scaled Cloudflare, one of the employee number 45, stayed there for 10 years.

Correct, that's right. Yeah.

Wow. And you see the saw the company from, let's say, grow from 200 million in valuation to public market cap of $60 billion.

That, that's an amazing journey. Is that where you learned the chops of GTM?

Yeah, absolutely. Uh, I mean, my background is in go to market. So I, I did, I think first company I learned go to market was at Dell. Uh, but I would say that Cloudflare was the one where I really applied what I learned in the past and continue to hone it and and and also kind of as the company starts to grow, the way you go do go to market changes as well. So I think Cloudflare I would say is where I learned the most and from my from my experience perspective.

And your choices of companies have been great, right? You were early member in Splunk, Cloudflare and AI. Like, how did you make those choices?

Yeah. Yeah. I mean, for me, I, I think sometimes luck plays a role, but I think broadly when I think about the companies to join, there are a couple of things I always look for. One is, uh, I'm a big believer that if, if the market you are in, if the market has a big opportunity, you as a company will get lot more chances, uh, to make it work, right? If you're in a, in a market with a small dam, uh, you do, do one thing wrong and you're out of business, more than like, right? So, so for me, um, when I chose Splunk, I just felt that and big data was something up and coming and I felt that, you know, everything up till that point was structured data indexing and and Splunk was the first company where you, you and index unstructured data and make it searchable and I just felt that the dam of this is massive and and and Splunk was doing great work. Uh, similarly for Cloudflare, um, the, the, the market, initial market of Cloudflare was in the tens of billions of dollar and and and CloudFare find, actually I felt found this very niche market where anybody who own the internet, who who doesn't have the scale like the larger companies, you know, like think about the Yahoos or the Googles of the world, but they still need their website to be fast, they need website to be secure, they their website to be online and there was nobody serving that and I just felt that the, the, the market is big and it's underrepresented market and there's a company actually who's thinking about that unique opportunity, they should be should do well. So I think it's just always for me come down to how big the opportunity is and then I always bet on the team as well, you know, sometimes, um, for the most part, uh, like I mean, you pick any company, like a lot of multiple people have the same ideas and and very few founders are able to make it successful and it really comes down to how what is the belief of that founder in that company, right? Or in that market and and usually that is the belief which carries you through through the, you know, the ups and downs of a company and I felt that, you know, the Splunk founders were were like really believed in what they were doing. Same thing I felt about Matthew and Michelle. They genuinely believed in what they were doing and I could see that, uh, that they would do whatever it takes to make this work. So, so, so and same thing, you know, now I joined Arise, I felt the same way that big market and the founders really believe in in in the company and and the market. So, so luckily for me, it worked out.

And, uh, you have always chosen companies on the dev tooling side rather than application side. Why is that?

Uh, it's, I think that's by chance it happened, but I think only only thing if I think about my background. So my background is in databases. So my first job out of college was, uh, I, I was a back end developer.

Maybe you can share a little bit about where did you graduate from?

Yeah, absolutely. So I, I studied mechanical engineering from, uh, from Punjabi Engineering College, undergrad in 2001, and my first job, uh, was, uh, was a software engineer, backend developer for, for databases. So I, I knew I very early learned how to write SQL and Oracle database and whatnot. So data is something I was always comfortable with and I understand what that really means.

And, uh, and and that's why, you know, Splunk, kind of I understood what this the Splunk is trying to do and and.

And how did you move to the US?

Yeah. So, so, so yeah, you, so I came to the US, uh, to do my masters. So I came to US in 2003 to study, uh, uh, study, um, masters in, uh, management information systems, MIS, at Texas A&M University in College Station. So I came, came for that, uh, did my masters there, and then I got a job at Dell in Austin, um, in the initially in the business intelligence group.

And then I moved because I always had then knack for, for, for the business, so I then moved into the marketing team.

Uh, and then I also end up doing MBA from University of Texas in Austin. So, so I end up doing two degrees in the US, but yeah, uh, that's kind of my journey has been.

Got it. And how was the breakthrough in Splunk? Like, how did you identify, first of all, because dev tooling was not popular back then.

Right. Right. So for me, how this happened was, uh, was one of, one of my friend from Dell was working at Splunk. And so after going to business school, I actually end up going to management consulting, you know, like they, they were at that time was the highest paying, highest paying employers, right?

Uh, I, I went there, I did two years in the management consulting, and I was in the Bay Area. So I moved to San Francisco after doing my MBA, and, uh, and that was the time when, you know, the companies like Facebook and and Uber and Airbnb was just coming up a little bit and and the, and and and I remember, uh, Facebook was the company which went public when I was here, I think around 2009, 2010.

Um, and and and I felt that, you know, this is where I want to be, you know, I want to be part of this young, some of these younger companies and if the company does well, one is I'm going to have amazing experiences and that's would be a way to really create generational wealth. You know, it's part of riding that wave.

Um, so I started applying to a lot of startup startups at that time. Uh, but startups usually do not hire people with like MBAs and who come from management consulting for their, in their mind, uh, you guys, you know, all about process and data and analysis, but as a startup, you do none of that, right? A startup is all about taking actions and and and and learn from that experience and you know, improve from there. So for me, Splunk happened because I knew this person from Dell. He was working at Splunk and he made the introduction and, uh, and I was able to get in through that referral and that's kind of how Splunk happened. But I, I, I got excited about Splunk because I could understand the, the, what the product was and it was making a lot of sense. But I do remember at that time thinking like, I think I want to go work for Airbnb because, you know, there's always, you know, these sexy companies, yeah, out there where you like, I think that is the cool company, but I could not get an interview at at Airbnb or or or Uber or or Facebook for that matter. So for me, uh, the, the company which was in data infrastructure kind of chose me and and I, and I chose them, and that's how kind of it happened.

And why did you leave Splunk early?

So I, I left Splunk early for, uh, for a reason that I just did not get along well with my boss, basically, and and not, uh, not like in a personal sense, but I think I just did not agree with his decision-m and and I, and I just felt that, um, uh, that, you know, the decisions being made were not right in the right interest of the company and I just could not take it, basically, and and for me, what happened was there was this other company which was doing really well, it's called Latus Engine, uh, they were in in the in the data space. So what they were doing was using predictive analytics to, uh, predict what should be your upsell, cross-sell place, you know, like think about, you know, when you're on Amazon, it tells you if you buy this, you should be buying this. Uh, they were doing that for B2B companies and I felt that that was an interesting space, uh, and they were leveraging big data to make those pieces and I just felt that I can really learning from Splunk and apply to this company and and and and I just felt that this was a better fit for me, uh, but on the hind side, um, I, after leaving Splunk, my actually boss was got fired within two months of me leaving. So it became a moot point, like in a way, moot point, but this is what happened. Yeah. So, but like I learned from that experience is like, never leave a company you believe in for, for like, you know, these kind of reasons, you know, you have to stick it out because you, things changes really fast. Uh, and one thing doesn't change is like, you know, the, the, the trajectory of the company, like if you are an entered company which has amazing product, customers are happy, big time, stick it out, you know, you will, those problems will go away over time and and I, I, those things stayed with me at Cloudflare and I end up staying there 10 years because of that.

So, so you are, sharing some interesting trends, right? A company which has a large TAM, uh, right? The founders are directionally right in how they're building the product because at early stage, nobody can tell, which direction the company can go. But if it's the last time, you give yourself enough surface area.

Surface area to to experiment and fail and and still being able to recover and and know, put something, build something from there.

Yeah.

And let's say some of the other dev tooling companies, uh, or for example, companies which are infra, that, that I believe you have always been part of infra, right? And now they have becoming more and more cool because, you know, they say in gold rush, give picks and showers.

Right. Right. Right.

Is, is that what you are always looking for, even when you are investing as an angel?

Right. So I think, you know, um, I, I believe that, you know, like you, I call it infra company, I sometimes call it plumbing companies, you know, your plumbing is something when you're building a house, right?

Uh, once you put the plumbing, you never change it, that's the reality of it, right? Unless, uh, even if the plumbing breaks, you just go, whatever plumbing you have, you fix it, and I feel that a lot of the infra companies are that, where if you can be part of, uh, uh, part of the journey for, for company, you know, and and be part, be the plumbing for them, they will just never replace you. So, so that's one of the reason I enjoy infra. I just like infra. It's not sexy, right? It can, you know, if it, it does not have, does not get the same level of, uh, publicity like as the application companies, but I just feel that, you know, sometimes it's the boring industries which are the one who end up doing well in a long run.

Yeah, but today, if we see, you know, uh, companies that are getting love and attention, DataBricks, right? Valued almost like a 200, 100 billion dollar company, again, a plumbing company, you don't change your data store.

Right, right. Yeah. So those companies came out, but I think if you think about it, uh, today, right? I mean, if you look at the world today, lot of the AI application companies are growing way faster than the companies like, pick like Replit or pick Lovable or pick, you know, any of these companies. They are able to go from like 10 million to like 100 million within like matter of months or quarters, right? Which is not going to happen for plumbing companies, like on infra company, because infra companies usually grow slowly, right? I mean, typically historically, and same if you look at the DataBricks journey, uh, they've been now around since 2016, you know, from the Spark days, and now they've been around 11 years, uh, and it took them a while to get to where they are. Uh, so, so I think it's, it's a, in a long run, if you have a great product, like I just said, and and you are the plumbing company, infra company, you're going to do well, but it will take you a while to get there. Typically, it's not like in overnight, you will will not become a success, you know, like data, if you're database company or something, but today, you're seeing like a lot of the companies become overnight success with application AI companies.

And which are the spaces that you think will grow in the next 10 years? For example, markets before starting a podcast, we are discussing about voice. So in your opinion, including voice, what are some other spaces that you see real revenue growth, not just factual valuation growth?

Right. Like, and you mean, from a AI application perspective or in.

General.

In general, but but carrying that tailwind of AI.

Right. Right. I mean, I think I do believe that the the middleware companies, right? Which kind of sit between the application and the and the models should do really well in the next few years.

So, like think about like Arise. I picked, they are in in this in the Evals and observability space, where the biggest challenge today, you know, as you think about building the AI agents, is is how do you know agents is going to do what it's supposed to do. You know, it's, it's very easy to build an agent, but it's very hard to scale an agent. So, and you need the right infrastructure for that to happen, and and those, like like Eval is one example, where it tells you whether your agent is doing what it's not doing. Observability is, is, you know, make sure that your agents are performing as they're supposed to in in production. I think that will be a big market. You know, it's, it's very similar to like DataDog examples, you know, what DataDog did 10 years ago, or Splunk did 15 years ago, or like all of that. So I, I believe that this particular space, uh, and if, like any space should be like hundreds of billions of dollar, right? I mean, and and the reason why I feel it be that big is, if you add up market cap of like DataDog, Splunk, New Relic of the world, it's more than 100 billion today, and and I think AI observability is going to wave bigger than, you know, the, the infrastructure or the APM today, right? So it should be like a big, big opportunity in my mind. Like, you know, that's definitely it should be big. I think, uh, inference should be another big one. I, you know, as you think about, uh, that, that potentially could be a big market. Uh, I mean, I think anybody who's going to like sit in the middle, like think about, you know, when I think about the DevOps, the, and contrast to DevOps layer, right? You know, Dev DevOps, you have your observability, you have your security. Uh, I think, you know, they potentially is like security companies should continue to do really, really well. You know, if you look at Cloudflare today, I mean, they, they are, they are doing really well because now people, you know, believe that they're going to play a major role in in in in in the world of AI. So, so, so I think it's the boring com industry which going to do well. Uh, the application side should do well, but I think the problem, like the way I see it is, is they, there may be 20 companies which do well today, but of that, maybe one or two will be successful in the long run because it's, it's very easy to challenge the, the mode of those companies, right? Because end of the day, their biggest mode is their their consumer, right? Who's using those, and and these days, we know that consumer is very finicky. They, they don't mind moving from one application to other, like, you know, any given day, like the, know, if you look at the developers or or the users, they have a very low attention span.

Yeah. They, they immediately moved from Cursor to Claude.

Right. So think about Cursor and Claude, right? I mean, within a day, people make the switch. And same thing I feel like, you know, if you're un, I'm Lovable or like what they will, people will move very, very fast, right? So, so that's why, um, even though the TAM wise, application maybe have a bigger TAM over over the next 10 years, but it's very hard to pick the winner in that space. It's much easier in my mind to pick winners in the in more of the middle, the infrastructure space.

And and why do you say it's easier to pick winners in middleware?

The plumbing, right? Once the plumbing is in, it's very hard to change it.

But in application, usually it happens, winner takes all. In plumbing also, it's like the winner takes all market.

No, not always. I mean, pick any industry, right? Like even if you pick observability as an example, you have what, six or seven companies who are more than billion dollar today from a market perspective, right? Similarly, if you look at the databases side, you have multiple companies who are doing really well. So I think it's not that winner takes it all, especially on the infrastructure side. I mean, even like if you look at the, the cloud space, which is the, um, application, uh, security and and performance, there's still multiple players. Like there's, there's Cloudflare, there is, uh, Fastly, there's, you know, all are billion dollar companies, right? So, so there is a space for multiple companies, I believe, uh, uh, but in application side, I think what's happening is that the, the, the user actually like kind of move in hordes, like, you know, they love one company today and they all are using it, and then they next day they start using other, like other company, and they just, the horde moves, move from one to other, and and they don't really have, no reason to like stay back, you know, like, if you, if you look at the history, right? The companies which own the data did really well, like think about Salesforce, right? I mean, Salesforce did really well as a companies because because they now have all your, uh, business data, all your sales data, you know, and and it's very hard to take move the data from one platform to other platform. So, so and and also, um, you end up building all these workflows and process on top of it, so it becomes extremely hard to move and that's why Salesforce is still a major company. But if you think about a lot of the, you know, companies which were not owning the data, I know a lot of workflows company came, they kind of came and and went and and did not really became that big, like, you know, so, so I just feel that in a long run, it's much easier, I believe, to pick a winner where you're sitting on in the back end versus on the front end, unless you are a data, you, you own the data, like sales.

But what about enterprise applications? Right. How, like the examples that you took, Lovable, Replit, they are more, let's say prosumer kind of application, you know? What about enterprise?

So enterprise, if you are enterprise application, I believe that if you are the one who keeps the data, then you will be successful because moving data is very, very hard, unless that becomes very easy of the future. So, so I, I still believe that, uh, any enterprise, you know, like if I'm Workday, or I'm Salesforce, or I'm HubSpot, or Freshworks, I mean, you have a strong play still because you, you own the data.

But let's say today, HubSpot is 80% down.

Right. Right.

So how, how do you justify?

I mean, that's a piv, that's a, that's a pivotal shift in the market. Right. Because I think now what's happening is that the, the software is potentially is going away and getting replaced by AI. You know, you know, like if you think about the, the world where, uh, in the future, I believe that you are not going to do clicks to get something done. You're just going to ask in a natural language, right? So which means that like I think these earlier software companies, more than likely is going to get replaced by AI systems where, uh, they, AI system is not going to have any UI or none of that, no clicks, and it's just basically you interact in a natural language, and we see this like with the, know, with the Cloud Code, or with with the Cursor, and all it can do wonders, right? I mean, like even in my current company, um, all our systems data, like Salesforce, HubSpot, and everything, uh, even Gong data, we actually put into BigQuery, me, you know, every data, and then we have a, uh, uh, we have, uh, Cursor sitting on top of BigQuery, and if anything I want, I just ask Cursor, and Cursor gives it to me. So I don't need any analyst, I don't need anybody to stitch the data together, none of that. It's just one, you know, you can just ask in a natural language. So I think in the future, now would be, as a user of Salesforce, they're not going to do clicks and add information. I think this is going to tell the system, you know, I'm working on this deal, open this opportunity, do this stuff. So unless these companies find a way to, uh, move away from software and become AI companies, I, I feel that they're, they're not going to be very big in the future.

And, you know, looking back at your Cloudflare journey, what was the revenue of Cloudflare when you joined?

I mean, when I joined, uh, there was very little revenue. I think it, it was like few millions here and there.

A single digit million?

Single digit million, for sure. Um, so what we had when, when I joined the company was, uh, a free plan and and a $20 plan, which we called Pro plan.

And there was no enterprise plan.

There was no enterprise plan. So when I was hired, along with Chris Merritt, Chris Merritt was the president of the company, we were hired to help build out the the basically the sales motion, because the PLG motion started to do well, and and we were having, we were had a lot of free users and and good amount of pro users, and and we felt that it was the right time to start thinking about building the enterprise motion, or the sales motion, not enterprise, uh, and that's kind of when I joined, basically.

And when you accepted, what was the revenue?

When I left, it was like about 1.2, 2 billion revenue.

Today, it'll be like two to three.

Today, I think it's, it's a little over two billion now. Yeah. So it's, it's growing very fast. I mean, I left the company end of '23, and and they are growing, uh, somewhere between 30 to 40% a year. So which means should be over $2 billion now. So, yeah, it's a strong business, a good business.

So if you can share, you know, your playbook of some of the mistakes that you did in building the GTM at Cloudflare, and what also things that you did right.

Mmm. Yeah, yeah, yeah. So what, what things we did right, uh, and things mistakes we made, okay. Um, I mean, what we did right, I think, and as I think about our Cloudflare journey, right? What things we did right was, we never tried to boil the ocean, you know, like we actually, you know, I'm a big believer that, uh, you can't be good at everything, and when I, what I mean by that is, if, when you're building a company, right? You can't be good at, um, serving all kind of customers. You can't be good at serving a customer through a subscription and also through a sales team. You can't be good at selling a customer in the US and also in Europe and also in Asia. You can't be good at selling like two products at the same time, right? So, so you have to pick and choose a combination which really works for you well, and you have to pick the right customer segment. You have to pick the right motion you want to sell it through, and the in which market. And at Cloudflare, I felt that we had that understanding and clarity that over a long run, we, this company has has a massive potential, but over a short run, we just have to go win a particular segment of the customer, and if we do a good job with that, and we will earn the right to go win the other segments of the customer. So we never try to boil the ocean. So initially, uh, our focus was very much, bring more of the lot of the customers through the PLG and and and get them to value really fast. You know, one of the obsession at Cloudflare, uh, we had was, uh, how to provide the the value to the customer really, really fast. So, so our self-serve motion was built in a way that you come, you swipe your card, you can turn on the service within a matter of minutes. All you have to do is, you, you move your DNS to us, and suddenly your website is going to, uh, uh, going to load faster, it's going to be secure, it's going to reliable, all of that. So, this, you know, the holy [ __ ] moment, you know, we talk about, uh, would happen really fast with Cloudflare. And what that allowed us to do was like, just build this flywheel of lot of developer, lot of the, you know, the, the, uh, web masters and and founders just start putting their websites on Cloudflare and and and and and we felt that we had this very unique motion as a company, and and this is something we should build on that versus like start, you know, go find a new motion. So, initial enterprise plan was nothing but the the same product which you have on PLG with the vital glove service, you know, because what happens with a lot of customers is, they come through PLG because they love, uh, that it's very simple to start, very simple to use, you know, very simple to get to value, but they, they want something more than that, like they want to make sure that, hey, there is actually somebody who's can fine-tune my environment to make sure I have configured Cloudflare properly, or if something breaks, actually, uh, there's a phone number I can call, right? Um, they also like, hey, I don't want to have to pay through credit card every, every month, because then I have to go expense, it's just too much work. I rather pay, you know, on, on an annual basis, and we started seeing those customers, right? So, first customer, enterprise customer, we have was Bain Capital. So Bain Capital was using our self-serve product and they were happy with it, and, and we just put a phone number on our website, you know, if somebody wants to call us, and that phone number was pretty much like cell phone, my cell phone, and, and, you know, like, you know, we had a phone basically on the desk where I was sitting, and Matthew, our founder, CEO, used to sit, Michelle used to sit, Chris used to sit, and the phone would ring, and we, one of us will just pick, and the idea was that, hey, if somebody calls, we can learn something from it, you know, and we actually got a call from the CIO of Bain Capital, and he started, he was like, hey, I love your product and all, all the good stuff, and but he's like, but I need more than what I'm getting from it, and what, but he was looking for a vital service. He's like, I want, you know, he's like, I'm not confident that we have configured this properly, I want to make sure that, you know, there's a solution engineer who can help me with it, and then he's like, I just hate two expenses every month. You know, what are you doing? And I remember asking him, how much would you willing to pay if he can, you know, put all this together into as as a plan for you? He's like, yeah, something around $3, $4,000 a month is okay. And, and I'm like, okay, that's like almost $50,000 a year. And I was like, you got it. And that's kind of was the day we like, okay, we are launching our enterprise plan. And it was nothing but our PLG product with all the vital service and and the things people wanted. But over the then over the period, then we end up adding lot more products and pieces which made enterprise very unique compared to the self-serve. But the initial couple of years, the enterprise product wasn't like different than the PLG. It was nothing but a vic of service, right? So, so, so this is kind of how Cloudflare go to market was built, was in a very thoughtful way, without trying to boil the ocean, really, we fine-tuning to the needs of the customer and just serving that customer really, really well, and and then we just continue to like add more and more layers of go to market to it, right? Then, uh, initially, it was very much bringing PLG, selling them to enterprise, then we started also going with outbound motion to sell, you know, get get customers who are not coming to the PLG, then we went and opened an office in in in in London, then we went and opened office, opened office, opened office, opened office, opened office, opened office, opened office, opened office, opened office, opened in Singapore, uh, then we will launch our second product, then the motion become from acquisition to like more more expansion, and then we felt we are ready for the large enterprises, then we really went after the big, you know, companies, but it was like in a, in a very thoughtful, sequential way, we went about it, and and we were, one thing was very clear to us, that we wanted to grow fast, but we never wanted to like that we have to triple the revenue every year. We were like, if we can double the revenue every year and go slightly faster than that, I think we will build a very strong business which will survive the test of time.

So if you can recall, you know, roughly which year did you cross 50 mill and which year you cross 100 mill?

Yeah. Yeah. So I believe we crossed 50 million. So I started with Cloudflare in 2014, early '14. I think we clo crossed 50 million in, I think end of '16, 2016, in 3 years, and then in, then at that time, we were more than doubling the business. So then we must have crossed the 100 like within the next nine months. Uh, so we went public in 2019, and at that time, we were doing about 300 million. So, so in our case, I think, uh, we went from like couple of million to 300 million in, in a, in a matter of, uh, five years, and and then after, after going public, we, we actually accelerated our growth because, you know, as a public company, we were, we were able to have bigger awareness, and then COVID happened, and then we went from 300 million in 2019 to, uh, a billion in 2023, basically.

When you basically.

When the end of which I, I left, basically, right? So, so, so the growth happened fast, but, but it was not like what you see today, where the company is going from 10 billion to $100 million in a, in a, in a year, or going from 100 to 150 million in a quarter. It was not like that. It was little more organic, uh, growth. But we were always close to doubling the business in early years, or, or in some cases, tripling the business.

So what I'm learning from you is today, like most of the founders, they rush into their enterprise go to market really early on.

But you took your own sweet time to to build that enterprise motion.

Right, right. Because, you know, especially if you're infrastructure product, right? The number one thing customer want from me is, is this, the stability of your, right? Is are you stable? Are you going to not go crash? Whatever, you know, like, you know, I'm, I'm building. So if you are become that critical, um, point for, for a customer, for their application, you have to be very careful. Can you really serve them well or not? You know, end of the day, you only are successful, your customers are successful, you know, no point signing like a very fancy customer, but if you can't serve them well, and and more than likely, you know, they're going to churn. So you have to be have intellectually honest and say, based on you, where you are, which, what kind of customer can you serve well, and you do a hell of a job in making those customers successful. ful, and then at the same time, continue to build your capabilities, and and then when you feel that you're ready to serve, like the, the biggest and the greatest of the customer, go do it at that time. Um, other challenge you could also happen is, if you go bring a very large customer, very early on, then that customer is going to force you product in a direction you may not want to go, right? I mean, if, if you bring in, I don't know, Apple as a customer, more than likely, like Apple is going to dictate your road map, you know, like, so you, one of our competitor, Akamai, biggest customer was Apple, and we would hear all the time, is a my road map is dictated by Apple. And so for us, it was very clear that we don't want a customer like Apple, and all early on, because for us, the, the mission of the company was, how do you make internet, help make internet better for everybody, which means that we wanted to serve a wide range of customer, like wide range of the internet, versus the select few. So we intentionally actually would not go after the large customers.

So you said no to Fortune 500?

Like, we, 100% right. If they come to us, we may entertain, but we never went to them. Like, in the first three, four years, we did not go go, we did not go after any of the customers at all. Uh, but if they come to us, we will. But there are times where we did say no to the customers, like, no, we are not ready for you. And, and I think that honesty is important because at the end of the day, the great businesses are built over decades, not not months or years, and and you have to have take that long of review, is, is like how do you want to sequence things, and and end of the day, you have to, no customer is too small. I mean, if you know, you, if you're happy customers, is what you really need, because that, you know, really build a flywheel for your next set of customers. So you are better off doing that, versus getting the, you know, first million dollar customer.

But founders, how do they overcome that fear that if Apple is knocking on your door and you're not ready for Apple scale yet?

Right?

Apple might go to your competitor.

Right. Which is, I mean, yeah, you let them go, right? I mean, because the thing about Apple and all is that they, they do come knocking on the door every two, three years about a particular tool, right? So it's not that if you lose Apple today, you completely lost your right to Apple. You will get another chance, you know, like in the next couple of years, and that's okay. So that's where, you know, that patience becomes very important. Uh, where you actually, um, uh, intentionally say, I'm not ready for you, but, you know, let me come back to you in two years, and then you will go win the business. Like Apple became Cloudflare customer eventually. Uh, and, and so it's not that the Apple was not did not, but it did, but that happened like in 2020 or something like that.

Two, after almost like six years.

Almost like almost six, seven years, yeah.

So, so among all the companies that you joined, like Splunk, Cloudflare, Arise, they had, I would say, very early signs of product market fit at the time you joined. How did you decide that the PMF is there or not?

PMF is there or not?

Yeah. I mean, I think because I had a choice is to pick a company with a PMF, then I did choose a company with a PMF, right? It make become your life much easier. Um, you know, PMF is something you can't force it.

Like, how do you define PMF for, for, in your definition?

Yeah, I mean, like the PM in my mind is like, there's a pain in the market, and you, you clearly sold that pain for that, you know, that's a PMF, right? Because a lot of times, uh, the, the pain is not clear, but you have built a product and you continue now hunt for the pain, right? So, so the, so that it has to be. So for, for Cloudflare, for example, the, the pain was real, in the sense that, like, let's take Splunk, right? The pain was real, there was the massive amount of unstructured data out there, but it was not indexable before Splunk, right? We saw if you can't index the data, you may not can't make it searchable, means that the data is useless, right? Uh, but, but there was, there was so much, um, uh, uh, you know, there were, there was so much unstructured that there got to be a use case for it, and Splunk did have that problem early on, where they were like, hey, we are the unstructur, we are the big data platform for unstructured data, and and a lot of customers like, I get, but I don't know what do I do with this data, and that's when, uh, the, the Splunk actually thought about, let's build a use case of where the, you know, we can be, uh, we can be powerful, and they, then they, that's where they got into that overall, the, you know, the data center observability space, basically, right? Which is, this idea is that there's insane amount of logs and not being generated, if you can make them searchable, you know, what the hell is going on, and, and so you have now, you build the use case, and that use case is very clear, there's a real pain, everybody body who has the data centers, they have the bunch of switches and the routers and what not, and they need to know, are they working properly? If something's going to fail or not, and now you have, you're solving real pain. So I joined at the time when they kind of figured that out, like it was clear, right? That, you know, you need this, and it's great. And for Cloudflare case, um, it was very clear, you know, like, it's very clear that every website, like I remember reading this this study, um, about Google, because Google will track, you know, think about their ad revenue, right? And their ad revenue was directly correlated with how fast the website would load, right? You know, which is, you know, if the website is fast loading, means the customer is going to take action, they're going to buy stuff, and, and, you know, it's going to generate more ad revenue for them, and, and but if you look at, you know, back in 2012, '13, there was only 2% of the website who actually was using some kind of a CDN solution or some DOS or whatnot, and, and is, and I'm thinking like, all these websites do need this solution, but there's nobody who's who's delivering it except the Cloudflare. So the PMF was very clear, like there is a PMF out there for, for what Cloudflare is doing. And same thing I saw with with Arise, where, uh, the traditional observability just doesn't work for AI, and, and everybody, it's like, every company you talk to, they have either working on a AI project, or or they have planning to work on AI project in the next 12 months, and, but these, none of these companies know how do you take these agents at scale, and one of the key pieces to run agent at scale is to have the right tooling from a evaluation, observability perspective, and, and there are only very few companies who are actually doing a good job of that. So the PMF is again, very, very strong here. Uh, so for that's kind of how I look at is like, there's a real pain in the market, and you actually do sale, solve that pain, very, very clearly, then you have a PMF.

And in case of Arise, because now, you are well known in in the Bay Area ecosystem, you know, as a, as a leader who could take a company from, let's say, 10 to a billion.

You would have multiple offers, I assume, right?

Right.

So, so why only this space? Because now you were not only looking at, I assume, companies with PMF, or infra companies, but why only choosing Eval and observability for AI?

Right, right. I mean, I think, um, partly because of familiarity. Um, so, uh, before joining Arise, I, um, I was with Insight Partners.

And you worked full-time with?

Uh, I did work full-time with Insight Partners, and Insight Partners had two companies, Fiddler and Reiterate, and biases, which were in the similar space. So by being there, I understood this industry well, and I understood the, the, uh, the challenges these companies have, and and the from a product perspective, what do they lack? And I felt that Arise have everything these companies don't have. And even though without these companies didn't have a lot of these things, but, but they were still doing really well, just that just kind of gave me the conviction that the Arise have a very strong future, basically, you know, end of the day, like we all have a limitless options, but, you know, some, you just al, I always bring feel that if I know something, I feel familiar about something, I'm confident about something, I'll just do it. I will not chase the shinier thing than that, you know. I'm, I'm, I'm, I'm a big believer of that. I, I'm usually don't chase the shiniest thing.

No, you don't super optimize. Yeah. I just run optimize, you know, like I I I calculate the odds or something and and and if something has a, you know, good odds, then you just go do it. And for me, Arise felt that one is it's space was interesting. The product is amazing like you know Arise customers love their product like no matter who you ask, they just love it. You know, I was just in India some time ago and Arise has this open source product called Fenix and I can't tell you developers like I love Fenix. It's it's a game-changer product. Um, so for me, I felt the product is great. The the founders are technical, super smart, bright and and and they I would I would bet on them any given day against any other founder, right? The strong strong founders and and strong product leader, visionaries, right? And I felt the founders are strong. The install base of Arise was very very good. So Arise have customers like Atlassian, Walmart, um Wells Fargo, uh they have like Uber, Door Dash, um they have Bookings.com. Uh you know, like when you look at these customers, you're like these you can't make these customers happy unless you have an amazing product. So, so I built the conviction that the product is amazing space is space I understand and the founders I love like what more do I need basically you know like I could keep chasing the shinier thing but then you know then I was like no this this is really good product really good uh future and and I just felt that this is fit within the wheelhouse of the work I have done in the past you know I I like being in a like a dev-centric company where you know you have the developer love and I this company has all of that so I chose it yeah.

And probably I assume uh you know you look at things like 10 years steer. Yeah, yeah. I always look at like things in decades not in years. Like I mean that's like I'm a genuine believer in that greatness doesn't happen over in a few years. I mean you sometime you get lucky but usually it doesn't happen.

And and why join a company whether now you had like large credibility of you could have started your own company? Right, right. Yeah. I mean I think it's just I I could have but I just never you know I'm I'm not very technical even though I have engineering background right? So one is I need a technical co-founder to do something. I just never clicked with somebody to the level that we could start something. So it's it's very much that versus it's not like any calculative thing it just I never got to it you know. If it happens in the future it's great. I love building businesses. I mean don't get me wrong. I love I get so much joy out of it. So so let's see how things go. I mean I'm happily at Arise. I think it's a great company. I think I found home here. So I do want to you know build this business and and you know hopefully help make another iconic company.

Yeah. And uh can you tell us some uh uh process that you built at Cloudflare for building a go-to-market team? Like what are the parts of the go-to-market teams that you built? You know, a journey from 10 to 50 specifically. And this is helpful for founders who are listening.

Also from who are going from 1 to 50 million if they have PMF. Right, right. So in terms of like how the go-to-market team was at Cloudflare, right?

Yeah. So I mean I think our go-to-market team was fairly standard in the sense that uh the the different roles we had like so we had uh the traditional SDR or BDR role which is their job is to because we would get a lot of inbounds so their job was to work on the inbounds, qualify them, schedule the meeting, pass it to the AE. Then we have account executives. Account executive initially we really hired um like a more mid-market uh kind of account.

All based in Bay Area SDR and a? Yeah. So Cloudflare, we were very big on uh FaceTime. Like you know until we until the the um uh the co time. Uh we had multiple we have multiple office but everybody was supposed to come to office five times a day. Uh week like Monday through Friday it was required. Uh so the we had initially we had office in San Francisco and and then the second office we opened was in London basically. But initial go-to-market was all as San Francisco based. So we had SDRs, we had account executives, we have solution engineers. Uh these are basically your kind of the pre-sales people. Then we have a couple of people doing partnerships. Uh then we have like a very traditional customer the post-sales team which is customer success manager and customer success engineers and and then we had a customer support team for the L1 L2 uh tickets basically. So it it was a uh traditional uh model from a from a team structure perspective but the profile of the people we picked was more unique in the sense that uh we did not just hire people based on the experience. We hired people based on uh like the hustle and people with the owner's mindset and people who kind of have the chip on their shoulder. They really wanted to do something, you know, like we hired a lot of people out of uh universities, you know, like we hired like a lot of the SDRs out of Santa Clara University and and they did tremendously well uh for us. We hire like lot of mid-market AEs. Not we did not hire like your traditional enterprise AEs early on. Why?

Um, I think that the reason for that was that we wanted to build a fast velocity motion uh which was like the the deal size was about $50,000 and and we wanted to do deals like with the with the sales cycle less than 30, 40, 50 days basically. Fast fast motion. And and our worry was that if you bring in lot more enterprise people, they may push the company in in enterprise segment faster than we want to go basically. And and so our intention of our part to like bring people who's going to drive a lot of the fast velocity in go-to-market motion and we'll bring in the enterprise people when we think our product is ready or the company's ready for it. So, because, you know, end of the day, the the kind of people you hired has a big influence on where the company go from there. Yeah.

Right. So so in terms of even hiring the leadership team in go-to-market, we were hiring people who were more open about like doing things in a different way versus like come with a playbook. So we never hired somebody who like, hey, I have a playbook, this is how I do things. We were like, no, we don't need you because we were like, this business is unique that we want people with like first-principle thinkers who who who just um look at things and and then make the call of what is the right way to go about it versus like, hey, uh, I'm going to I I need to build a segmentation of there's SMB, there's a mid-market, there's enterprise. For enterprise, I need to put people on the ground in different around the world. Then mid-market, I'm going to put here. Like we're like, no, none of that. We don't want that. So, so we were like very intentional about that.

And one more thing I want to check with. Now I'm hearing constantly with founders that GTM in the AI force world is changing. What GTM has changed or team structure has changed in Arise as compared to Cloudflare?

Right. The GTM, I think, you know, how the GTM is different in the AI world. I mean, I think um, if you're selling AI, then the the way buyer go about is very different. So, you know, you can look at it in from two different ways. Is how is the GTM is different for a traditional software company? And and I'll come back to that in a second. But if you're selling in if you're a AI product, right? The the what's the difference is that uh, you know, in in a traditional older world, you always have to go displace an incumbent, right? There's a there's somebody spending money on something. You are actually saying that, you know, you move that spend from that vendor to us, like, you know, so it's very much about you kind of are a disruptor and you telling the world that, hey, we are faster, better, cheaper than, you know, what you're using, come to us, right? But in the AI world, it's you are not displacing spend. You're not displacing vendors. Actually, all you are doing is telling them that we have built something which will allow you to either make your AI work investment or AI is going to may help you in either reduce your cost or or drive the top line, right? So, means that uh, the the the traditional motion of like, you know, very consultative selling and whatnot doesn't apply as much to to the AI product, you know, like of the past. Uh, what really important now is the education, right? Because everybody is hungry for education around like, hey, how do I go build agents? What are the right tools? Where do I get started? How do I make sure my system doesn't hallucinate? How do I scale it? You know, like those questions are there. So, means that your go-to-market, you have to lead with education more. And and and education and which means that you uh you spend a lot more time in building documentation and also your brand has to be more educational. And then you can attract the developers to start using your product. And if they start using the product, then you use those developers as a way to go wall-to-wall with those those customers, right? So, so how you lean in is slightly different. So the, you know, the role of DevRel becomes way more important than what was like.

And you are saying because AI the budget didn't exist, for example, in case of Arise, Eval and a observability never existed?

Yeah. So we are not displacing any budget. Actually, we are we are actually in a way are part of the this bucket of the AI investment they are making. We we just want a sliver of that. So we don't have to go say that, hey, go remove Datadog. No, like keep Datadog. You know, you have you need APM, right? But but if you want to really care about scaling your agents in production, you need the right tooling. And we are one of the one of the tool for that, right? So it's a different conversation. For example, uh, so so that way the go-to-market is slightly different. But then if you're selling to enterprise, right, you still have to go to the same procurement process, the security.

But are you selling to mid-market or enterprise?

We are sell to enterprise. So we over over strength is large enterprises.

And why, you know, your specialty initially what you built at Cloudflare? Going deep in mid-market and then go to enterprise. So why did, you know, in here?

You choose enterprise where enterprise, as you said, right?

They bend the product. Right, right. So, you know, why it happened here is um, so we have an open-source product, Phoenix. And and what we realize, very interesting insight, actually. I I learned this after coming to Arise is that the the biggest, like if you think about, you know, you would think that every every developer would want to use open source. But what we realize is that if the developer at the larger companies wants to use open source, and if you're a developer at a, let's say born-on-the-web company, you actually want more of an off-the-shelf self-serve product. It doesn't have to be open source. And the reason for that is, uh, if I, if I'm, let's say, work for a bank, I'm a developer, I don't, I care about my data. So I don't want to move my data to the cloud. So open source is easy, you know, I can use that, right? So for us, what happened was because of open source, we got lot of developers from very large companies start using us. And in a way, then that just pulled us into those accounts. So, as an example, Wells Fargo is a big customer um of of and they were Before Wells Fargo became a large customer of Arise, there were like 100 developers using the Phoenix product. And that's the reason why we were able to, you know, get into the Wells Fargo. So in this case, we went to enterprise not by choice, but because customer pulled us into that direction, right? That's kind of, you know, one thing happened. And other thing happened also for us was so, uh, we started as an observability company. And and then now we are a more we are end-to-end solution where we do both Eval, but observability, you need it when you have something in production. Your agent is in production. And enterprises, like the large enterprises, where the agents are in production more than, you know, like the if you look at the Silicon Valley companies, they all are building agents, but none of very few of those agents are in production actually. But if I am, let's say, Bookings.com, if I'm Uber, or if I'm if I am DoorDash, I actually have agents in production. So I really need an observability solution. And that's one of the also reason is like those customer pulled us into into the mix um, so that's kind of how the journey happened for us. Uh, but like if from our perspective, I think we are very, very strong with the large, large enterprises. But now because we have a very strong Eval product as well. Uh, we are now winning with in the in the digital natives as well, very much. So now at this point, actually, Arise are doing really well across a lot of customer segments, which is a lot. So we as a company are, you know, trying to like keep up with the pace of like, you know, like that we are getting pulled in all the all the directions. But but the good news is that we have a product for like, know, lot of different kinds of customers right now.

How big is the team right now? The the overall sales go-to-market team is a little over 60 people right now.

60?

60. Yeah. Yeah.

And overall team?

Overall Arise about 150 people.

Okay. So large part of the team is go-to-market.

Yeah. Go-to-market team is quite big now and it's it's growing really fast.

And everyone is based here in SF?

Uh, no, it's it's a distributed team. So we have uh, like I think the critical mass is in the Bay Area, but then.

For the go-to-market team?

Go-to-market, a lot of in the in the Bay Area, but we have uh people around the US as well. Uh, then we have a team in Germany, we have a team in UK, we have a team in in Singapore. So we are now uh truly global go-to-market organization.

But isn't it too early to to build a global go-to-market?

Right, right. I think in our case, what happens, the customers are pulling into that direction, right? So for us, for example, uh, like we have lot of customers out of UK now and there's massive demand. Similarly, uh, even before we went to Singapore, we now have uh lot of uh, like big customers in Asia and and they pulled us in. So it's more that instead of us intentionally, you know, going and winning the market, it was the customer pulled us into those markets, basically. You know, I'm a big believer in in, you know, like, you know, when you think about the go-to-market, is that you never want to go into a market cold, you know, because, you know, what happens when you go into the market cold is that you have to invest a lot of amount of money to warm that market, know, from a marketing perspective, you have to hire the sales team and whatnot, and it take them a while to get the first customer. So, if you think about, you know, you added all this cost into the model, right, marketing cost and the people cost and what not, but the revenue comes like six, nine months or 12 months from there. And if you, if you for whatever reason, picked the wrong market, now you wasted time and you wasted a lot of money, right? So, so if you can uh find like a traction in a market for whatever reason, in our case, we found traction in like like Asian market and and also UK and German market, um, that it was a no-brainer for us that we already have customers who love us, we already have lot of open-source developers who love us, like it it's a no-brainer for us to put people in the market. So, it it was a no-brainer decision for us. But if that wasn't the case, we wouldn't have gone into those markets.

And and the folks that you are hiring, you know, to lead, do you have a similar function? SDR and AEs?

Right? Yeah. We have we call them BDRs, but yes, same BDRs and AE. Yeah.

And and what kind of profiles are you looking when you're hiring them at Arise?

The the BDRs, both.

Yeah. So I mean BDRs uh, I think BDR profile varies, but we are hiring a lot of out of the college people as well for BDRs or with with a one one to two years of of selling experience. Uh, for AEs, we are hiring some more experienced AEs because, you know, we sell into the large customers um, and typically AE will have experience, so five to 10 years of selling experience basically.

In the enterprise space.

Yeah.

And do they, these AEs need to come from selling plumbing products or you are hiring application AEs also?

Yeah, we kind of all we we want to hire AEs who are hungry, who hustle, who knows how to run a sales process, who know how to do multi-threading and all, you know, that the who know how to do sales uh, what they have sold is less important because we can teach them how to sell what our product, we can teach them about the industry. Uh, so we are not like uh uh only hiring AEs who actually are coming from the similar space. We have AEs who come from very different industries.

And let's say for a for a exec today looking to join a startup, either an infra or in application space, because there's so much noise in the AI world.

Like the principles that you laid out earlier in a conversations.

Extreme PMF.

Right.

Right. Plumbing, great founders. How how do they figure out these things about a startup when they're exploring too?

Right? Like, yeah, I mean, I think there's no there's no magic formula. You have to just spend a lot of time with the with the with the founders.

Like you spent at Insight Partners?

Right, right. Yeah. So you have to go spend a lot of time with the founders and uh, and and and then pick the ones where you actually click and and feel that, yeah, this is the right home for. I mean, I don't think there's any um shortcut per se, but I mean, some of the shortcuts I mean, if you really, you you basically, you know, if you want to make sure that you have the right founder, you ask tough questions during the interview process, right? And you sometimes u ask questions where it might be irritating to them, and then you see how they react to it, right? And then you get a sense for like, you know, how how well they take the bad news, how well they well they take the criticism, you know, like, you know, those things you do do want to assess. So there's few things you can do in the process, but but I don't think there is any um shortcut to just spending time with the founders and u um, and and and see where you you are able to build the comfort, right? So never like rush into into a job, I feel, and you take your time.

Like now you are creating the real plumbing for AI agents at Arise. So there's a narrative that lot of AI is hype, only 95% of AI agents are, you know, getting into production, rests are failing. What's your because now you sit at set the back?

Yeah. I mean, AI is not a hype. Like I mean, I think we we all I mean, we should we agree on that that AI is not a hype. But the statement that that lot of investment went into the AI and they the results are not uh what we expect them to be at this point. I mean, there's little to show for it. And part of the reason is that uh, they just the the the companies have not put the right tooling in place. You know, like a lot of companies made this mistake of uh, just using the existing tool to make AI work, which did not which did not which do not work. But now I think you will see a lot more agents going into production and doing really, really well because now, you know, like we see with our customers, like, you know, where their volume is just uh, is is is growing exponentially right now. So I think we are reached to that inflection point where we should start seeing agents uh being used lot more effectively than what is in the past because the right tooling is in place. You know, you definitely, you know, like if you think about how can you have an agent running at scale if you don't have the Eval or obser AI observability in place, like otherwise you are totally uh flying blind. And especially when the system is u non-deterministic, it can do, you know, you have no idea whether it's performing the way it should be. And that's part of the reason why um lot of these agents failed in production because the right tooling was was not there. And so now I feel that the companies are putting the right tooling though. So we definitely see a big big increase in the usage of agents.

So what what in a to get an AI agent to work in production really well? Apart from Eval and observability, what kind of other tooling do you require?

Right. I mean, I think um, I mean, if you think about uh broadly, if you think about the agents, what you have is a few things. You have your LLMs, you have your tools, uh, then you have your like RAG system, and right? So, so you basic and and then you you have the orchestration where you build the agent, right? So, so these are the things you have to get it right, basically, right? The the piece around when you're building your agent, like, you know, let's say you use orchestration framework to build your agent, uh, you have to put the the right evaluation uh system in place uh, so to to make sure that your agent is doing what it's supposed to do. So, as an example, right, agents are AI systems are non-deterministic, right? Which means that for input uh, it will give us different output every time.

Every time. Yeah.

So now, how do you know that your agent is doing what it's supposed to do? So the way you figure that out is you actually quantify the output of your agent into different metrics which you know, and and then you test your agent to make sure that it's it's a it's those metrics are true to what it's supposed to be. So the metrics could be, let's say, hallucination. You want to make sure that it's not hallucinating. You want to make sure that there's a correctness of answer. You want to make sure that there is no PII information. There could be like hundreds or thousands of different different metrics you you need. So first, you need to have the right system in place where you actually can run those evaluations at scale. That's first point. And now, let's say you are happy with it. Uh, now you move that agent into production. So now in the production, now another thing gets introduced where now in a traditional software, you actually are restricting the user to interact with the software in a certain way. You know, you restrict like what kind of inputs it can you can give to the software. But in an agent, the user can interact in a natural language. Now you introduced this this point, like the how the prompt being asked uh could be very different than what you tested in in development. So now whatever you test in development, that needs to be continuously tested in production. So those evals becomes the online evals. So, means that every time that agent gets called, those evals runs to make sure, yes, agent is doing what it's supposed to do, right? So that's where the concept of observability came about, which is u, you know, in a traditional traditional um uh observability, which is which is a for a deterministic system, it's what it's good at is telling you whether is your system is doing what it's supposed to do, you know, the yes and no, right? Whether u is is your is your service up or not, right? Whether uh, let's say latency is high, high or latency is low. So it's kind of answer you in a yes or no. Uh, in the world of agents, if you think about it, you could be in a world where you actually agent is perfectly, you getting you, you know, you have a Datadog dashboard, and everything is green on the dashboard, that you know, the it's doing all the right thing, but it the agent may be giving you totally garbage, hallucinated answers. And the system will say, oh, yeah, it's all green because, you know, like the the system is running perfectly fine. It's all green light, everything. So now in in production, you actually need an observability where it can actually tell you whether not that the the the traces are right, but but where the problem may be, you know, so let's give you an example. Uh, if you look at the agent, agent is a uh multi-step of workflows, right? So, you know, you make different tool calls, you are uh retrieving uh context from let's say, your RAG, you actually all calling calling other agents as well, right? So the answer is not that you actually were able to make a call. The the the the the key thing is, did you make the right call or not? You know, did the context you got from RAG was the right context or not, right? Did you actually make the right tool call or not? So, it's it's less about did you make the tool call, it's more, did you make the right tool call? And that's where, you know, the AI observability come into place. It tells you that actually you made the wrong tool call, or the the context you got from your uh RAG retrieval, actually that context were wrong, or oh, your context came right, but the answer you got was was totally different than what the context was fed, right? So, so those things are so important to make sure that your agents are performing as supposed to in production. And and that's where, you know, that this comes about is like, you know, to your question, what you need in production, you need all these systems in place, then only you can be uh sure that your agent is going to do what it's supposed to do. And and if it's not, then you can go fix it very, very quickly.

And let's say for enterprises today, when what use cases they qualify, you know, because now AI agents are hype and people will build AI agents left, right and center, compromising their security.

Right?

People are also giving their teams to spin up AI agents, you know, they create a central platform, for example, uh, you know, like Glean.

And companies will give their teams power to spin up AI agents, right? So how do they realize that whether do do they need an AI agent for a specific use case or not?

So your question is like, when where do you put the agent? Yeah.

I mean, I think agents is like nothing but workflows. I mean, I think where I mean, you want to put agents in any anything where the the steps are kind of defined to do the job, right? So, so I'll give you an example. So one of our customer is um DoorDash. And DoorDash has uh one agent uh for um for u refunds. So let's say, you know, you you get a DoorDash order delivered to you and you're not happy with the order or something wrong was delivered to you. So then you can ask for a refund. And now the if somebody asks for a refund, there are certain steps you take, like, you know, even if you're a human on the back, certain steps you take to make sure whether this is right or not, and then you issue the refund. Agent is a perfect thing which can do, right? So, so uh, so DoorDash uh is using Arise actually for for that agent where uh agent actually asks for um, can you take a picture of the order? So for example, it takes the picture of the order to really say what what is like, is the right right thing was delivered or not? Then actually it's actually goes and checks the history of of that particular u uh uh person in the past, like how often do they ask for refunds or not. So based on that, actually it can predict with a lot of certainty whether to give a refund or not, right? So that's a perfect example where now why do you need to human for that? Agent can easily do it. So those are the example where, you know, it's it's a there's information needs to be processed by a system or or a human, you use that information to go check on something and based on that, you make the call. Agent is perfect for that, you know, so those kind of scenarios, it I mean, we see working really well.

And these agents are for DoorDash are created by Arise, like or you just?

So, so we, so agents are created by uh, so we are the layer for Eval and observability. So agents do created by uh, you know, they can you can create agents through like frameworks like let's say LangChain or or TrueAI or whatnot, or you can actually, you know, uh, just uh, write your own agent. But we are the Eval observability layer for those agents to make sure of that. Because if you think about right, if agents, if if DoorDash agents start to perform uh, like start hallucinating and and let's say if they start accepting all the refunds or if they start rejecting all the refunds, it's a big problem for for DoorDash. They actually have to make sure that agent is doing what it's supposed to do and and and it's accurate, you know, so so that's why observability becomes so important. Like if if you can't you can't just allow yourself to uh to be in a in a black box like that. And and we have seen so many examples, right? You Air Canada, which is a customer of Arise, um, before they were Arise customer, they launched an agent uh to help with bookings, the tickets and and and it was mainly around uh booking the tickets through miles. And people find a way to like give it prompts where actually the agent end up issuing miles to those customers to those customers. And then those customers use those miles to buy start buying tickets. And Air Canada had no idea that's happening, you know, because they didn't have the right tooling in place. And and eventually they realized, so they did shut down the agent, but then they got sued by those customers. And then they had to actually honor all those customers with those tickets. So then they came to us and then now we put the right, you know, right the Evals and observability in place. And now that agent is doing really well. Right. So the idea is like, you know, if you really want your agent to scale in production, h you need to have the right tools in place. And first you test that in development, and then you have the right tool in place, then only the agent is going to you can be sure that it will deliver the business outcome you wanted to deliver. So, uh, it's a it's a great point that you made. But let's Arise, how do you discover customers like Air Canada who have a specific pain point because it's it's a great pain point right to solve? And then if you solve for such kind of a large customer, right?

Then the customer would be willing to pay at scale, millions of dollars.

Right, right. So I mean, I think either customer discover us or we go find the customer, right? I mean, it's a it's a typical, right? So so for us, we are very fortunate that we have open-source product where uh where that is driving a lot of awareness in the market about Arise. Uh, and so a lot of the customers find us through through for example, open source or or the word of word of mouth. But then you a lot of customer to search, right? So we invest a lot of money in in SEO and in SEO and making sure that uh customer can find us. We invest a lot on education. We have, you know, we we invest uh uh like through like education through DevRel that helps. So there are like a lot of things. These is no magic wand per se. You have to do a lot of things where you become discoverable by the customer. Um, other thing we are doing is we also have built very strong partnerships, you know, with uh AWS, with GCP, with Nvidia, with IBM, where uh they also introduce us to a lot of their customers because, you know, if I am, let's say GCP, I really want the consumption uh to go up for my LLM, and the consumption is only going to go up if if you have the right, you know, system in place for agents to really perform. So it's in their best interest to bring Arise into the mix. So, so they, you know, like it's like typical go-to-market, you have to find a way to build distribution and awareness in the market. And and there are a lot of ways you can do it.

But this is amazing like what you are showing is that there are real problems on the infra side and how they're affecting customers like in this example in case of DoorDash agents hallucinating and issuing refunds, in case of Air Canada.

Right.

Where action of an agent can get them sued.

Get them. Yeah. Or or big financial implication or reputational risk, right? Yeah.

Yeah.

Cool. So having the right, so that's why, you know, like I know the narrative in the market is that agent AI is not doing what it's supposed to do, but I think it's not that it's more that the people just have not taken the time to put the right tools and tooling in place. So, so when do companies like Air Canada decide that for a certain workflow, let's say booking tickets through miles, or can they hand over from a human to an AI agent?

When when they are 100% sure?

Yeah. I mean, I think, you know, the the I mean, so what I have noticed is that human in the loop is still a thing, right? I think, you know, where the the we still don't have the trust for agents to be fully autonomous yet. So, which means that if let's say today it takes you 100 agents, let's say support example, right, to to process your support tickets, maybe with the with the right um agents in place, AI agents, you need 10 people, and 90 of the pe people can be replaced by the agents, but you still need some human the loops to make sure it's doing what it's supposed to. And over time, the trust gets built and everything, you know, you work through all the corner cases, and then maybe it can become autonomous. But even until today, it's not, I've not seen any agent to be 100% autonomous yet. I think we will get there, like, you know, it's like autonomous cars, right? Um, I think in 2015, like 95% things like of the technology of autonomous cars was figured out, but that the last mile, it took like 10 more years to get to the point where now actually they are autonomous autonomous, right? So I think agents will be the same way. It will be faster than autonomous cars, but it will take another few years for it to be like really be the truly autonomous agents.

In your opinion, then the current, you know, we discussed about it. The the system of record layer is not going anywhere. But the current hype where SaaS companies like HubSpot are down by 80%, whereas Service Now is down by 50%, whereas and it happened because a private company Anthropic released a new version.

Yeah.

Uh, right. So is it like at one one area, the narrative is the agents are not delivering. So 95% of demos are failing. And the other end, the narrative is because Claude can build everything now. So that's why, you know, the companies that are leaders in their category are dying.

Right, right, right.

So it's very confusing.

It's confusing, but, you know, you know, if you ask me, they are the two agents in in the world right now which are like truly the most powerful. It's the Claude code and the and the Cursor. Those agents are amazingly insanely good. So I think the it's a matter of time that agents which are built by companies uh start doing what you expect them to do, if as long as they have the right tooling in place. I think we will get there. I think in the in the next 12 to 18 months, we'll see a massive inflection point. Now, we are as a company are betting on that, right? Where um, we have so many customers who are using Arise, and we expect that the the consumption of those customers should go like 5 to 10x in the next 12 to 18 months. Right? So, like if you take example, um, Sierra.ai, right? Sierra.ai uh got 200 million ARR last quarter, and then they added 50 million ARR the next quarter. And it's not that they actually signed $50 million new deals. What happened was the agents they deployed in production for all these retailers, and now they these agents start to see a lot of volume. And and they are charging based on deflection, right? So more deflection, more the money they make. So I'm pretty sure that this $50 million ARR they got the next quarter, a big chunk of that is actually existing customers just, you know, like start using more. Same thing, I think we should happen with like Arise, where, you know, like as the c the agents start to go into production, we should also see a big jump in in the consumption. But that's what we are betting on. And and we are already seeing those signals, you know, like, I mean, it's very clear if you see from the Sierra of the world or Decagon of the world, it's happening.

And and now let's step into your heart as an angel. So you would have received like in the last couple of years, maybe few hundreds or thousands of pitches from founders.

Mhm.

So how did you choose the 15 companies that you choose to invest in?

Yeah. So for me, I how I chose, I chose the company where understood I understood the space. I think, you know, like example-wise, um, uh, like Atomic Works, for example, right? Um, I invested because I when I, I understand ITSM. I understand that ITSM is a space where there's there's a bunch of steps needs to be done every time, and agents can easily do it, right? So I understand the space and I understand the application and I see the pain. And then u the I I know the founders really well. So I just felt that this feels right. So, so for me, it's like if I really understand the space and understand what is agents trying to do, and I can wrap my head around, then I'll go invest in it. If I cannot, like then I I just won't, basically, right? I mean, if if you come and say that, oh, this agent can do uh, the XYZ in healthcare, I I don't understand healthcare, so I don't know, right? I remember somebody pitched me this idea of this agent uh can read the X-rays and um, and uh, you know, in a typical way, when you know, when the X-ray happens, you uh the doctor only looks at the area where you have the pain and you just check that, and it doesn't look at the rest of the X-ray. So it's just, you know, this lot of information is wasted away. And the idea was that this agent can read the whole X-ray and create a database and actually then uh it can extrapolate, you know, like based on all this information about the the health of the person and what not. And I'm like, sounds like a good idea, but I just cannot wrap my head around how this is going to work. So I will not invest in something like this. I'm sure it's a great idea, but I would not. So for me, it's something tangible where I can understand like what what it is.

What made you invest in Composio, for example?

Yeah. What?

Or Portkey?

Yeah, yeah. So same. So Portkey, because the middleware, like I mean, I'm a big believer in middleware. I was like, you guys going to sit in the in the middle of the application and the LLM. I I think I'm going to invest in it. And I felt that Rohit and all just like smart guys, good guys. And I I, you know, that's kind of why I went with uh Composio. Composio, why I invested. I think Composio changed their ideas actually when I invested to what they are now.

What was the idea when they when you invested?

Uh, Composio, gosh, what was Composio's initial idea? I mean, Composio's initial idea was more like, you know, like how they are building different APIs to be a for for to be able to bring like data in one place. It's something of that sort. And but for me, Composio, I invested because of the founders. I just like those guys, you know, they are from IIT Bombay and like humble and super smart, you know. And I felt that, you know, it was early enough that the the valuation was low. I was like, I think I can bet some money on these guys. It was like very much investing on the on the founders than the idea. But surprisingly, of all my investments, that's one of my better investments. So, end of the day, I think you kind, you know, the question becomes, are you betting on the horse or or the jockey? Right? So in this case, I invest in the jockey, and it's turned out to be the good investment.

Yeah, because the idea changed.

Idea changed. Yeah. Same thing, Cloudflare. I think if Cloudflare also came through um a business, the uh business school competition, and the idea was very different at the business school level, and now what it became.

What was the business school?

Business school idea was very much about uh uh like a more like ad revenue. So you basically build the network and and lot of traffic, and then you can make money through the eyeballs. That's kind of the idea versus like actually be the security, the the layer, the network layer. So, so it was very different monetization idea versus what it became over over the years.

And when you joined at employee number 45, what was the product and the idea that they were selling back then?

So at that time, it was uh three things: CDN, so content delivery network, and u DDoS, which is distributed denial of service, and and DNS. And the idea was like setting as as one bundle. And because nobody was doing that.

I think they are still the leader in.

They're.

Still the leader in that, but that was that was what at that time was was the then the VAF came about and then all the things came about in it. But the initial idea was very much like, let's have the the best bundle a company can buy in the market and at a very, very reasonable price.

That's, in your opinion, in Neon portfolio, we have seen the highest amount of acquisitions, profitable acquisitions, happen in infraspace. I'll share one example. A company called Requestly that mocks HTTPS requests.

Mhm. Right. Uh, they got bought out by BrowserStack. BrowserStack is a $5 billion unicorn in India. We have a company called ZenDuty, which is a SaaS platform that competes with PagerDuty. Uh, that we, let's say, entered at a 6 million valuation. They got bought at a 25 million valuation by a PE called Zarant. Like it's a PE backed company, Zarant. Similarly, a company called Logic. It's a Bay Area company in observability, competing with, you know, Splunk on some observability use cases. They got bought by a PE called Aika. Aika PE company.

So, so why do you, I'm still trying to figure out like we have like five acquisitions in Neon portfolio. Is it incidental with us or is it?

Right. I mean, acquisition is a real thing right now. Um, I do feel that, um, any company right now, uh, any company you pick, the the the likelihood for them to stay independent for in a long run is very, very low.

Why do you say that?

Yeah. Because I think, um, the the reason is the market is changing so fast and, uh, and a lot of the things you are building kind of end up becoming a feature in a bigger stack, and you're better off becoming part of the bigger stack and, and, you know, versus like stay as a feature because you kind of become irrelevant. So, and it depends on the founders, right? What is the aspiration? Um, uh, but that's so, but but a lot of the founders feel that, hey, um, the market is moving fast. What I've built is working today. What if it doesn't work in one year? Because, you know, the AI is moving so fast, there's a lot of things which, you know, like makes sense today, may not make sense. Right? So, and and but and but but if you're getting acquired, the big, the other company can get a lot more out of it, and you can go, you know, you can get a decent outcome. So people are doing that. Uh, you know, so it's, I I get that. But for me, like for example, when I'm an investor, I'm okay with that. But when I'm picking a company to work, I I want to pick a company where they their aspiration is not to get acquired, but has to stay independent. Like Splunk and Cloudflare, like wanted to stay independent. I think Arise as a company, I do see founders, you know, that because one of the founders is a second-time founder. He has taken a previous company public as well. So for him, it's more about really making this company work and and and building something tangible. So I'm I'm excited about that. But you never know, you know, like for the right price, everything everything is is sellable, right?

Yeah, but that's happening for sure. I mean, you hear it all day long and and at a very massive elevations is getting acquired. You know, one of our competitors, UlangFuse, uh, is a company out of Germany. I think they had like 13 employees and, uh, and they recently got bought by ClickHouse. And I mean, they did not disclose the number, but we believe like it's close to a billion dollar acquisition.

Wow. 13 people?

13 people. Yeah.

And any revenue?

They had a revenue and I think they were like 67 million in revenue.

But they they were not a they didn't get acquired for their revenue obviously.

No, I think they they got acquired for the technology. I mean, I, um, but but yes, uh, but they got bought for like almost a billion dollar.

But today, the other narrative is, uh, any technology is not a moat. Any technology.

Yeah. Technology not a moat. You're right. Yeah. Yeah.

So, so then why are larger companies buying smaller companies when technology is not a moat anymore?

Uh, I think because because the because they can build something larger. Like, you know, they can build a basket. You know, like one particular thing may not be a moat, but the basket is a basket. Like, you know, Cloudflare, for example, right? Where, uh, we had competition from like different CDN providers, within with DNS providers, and DOS providers, and whatnot. But if you go ahead to like, if somebody just wanted DOS, we may not be the best solution. Like we were a great solution to DOS, actually. I take it back. But like, you know, for some pieces, we may not be the have the all the bells and whistles like, you know, others. But we have the best basket. And I think a lot of these companies who are acquiring, they feel that I want to build the best basket out there. Then I I'm not displaceable, basically. But one within the basket, few things may become obsolete or become like a commodity, and that's okay. But the basket will still have a lot of value. I think that's what's going on.

No, thank you so much Manisha. I really loved our conversation. Learned a lot. We love to do part two sometime, you know, of this conversation where I can dive deeper into some aspects.

But completely, you know, thoroughly enjoyed the conversation.

Oh, thank you. No, it was a great conversation. I enjoyed it as well. Yeah, very thoughtful questions.

Thank you.

Awesome.

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Why "Boring" Infrastructure is the Best Path to a $60B Company | Manish Jindal, Cloudflare & Arize

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