EP 108: Olivier Pomel (CEO, Datadog) Shares Every Lesson From Scaling to $40B

Olivier Pomel built Datadog into a $40B company while burning only $25M in capital. In our conversation, he shares the fundraising lessons, operating principles, and core insights that made this possible. He also discusses the pros and cons of building a tech company in NYC, how early-career professionals should approach learning AI tools, how Datadog is building a trusted relationship with their users around AI features, and much more (00:00) Intro (01:30) Datadog's Early Challenges and Lessons (04:32) The Core Insight Behind Datadog (05:20) Building the Right Product (07:46) Transitioning to Open Beta (09:38) Expanding Datadog's Product Line (11:26) Platform Vision and Modular Products (18:12) Empowering Product Teams (22:14) Acquisitions and Integration Strategy (33:18) Customer-Centric Approach (36:39) Bottom-Up Sales Strategy (46:04) Navigating Founder Challenges (46:37) Building a Collaborative Management Team (48:32) Staying Close to Customers (51:36) Maintaining a Culture of Discipline and Efficiency (56:26) Pricing Strategy and Market Positioning (01:01:24) Fostering a Low-Ego, High-Performance Culture (01:06:32) Hiring and Promoting for Long-Term Success (01:12:21) Fundraising and Investor Relations (01:16:03) Deciding Whether to Sell or Scale (01:18:26) The Impact of AI on Software Development (01:23:24) Choosing New York Over Silicon Valley (01:28:04) Final Thoughts and Reflections Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA 🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1 Follow on Socials 📸 Instagram - https://www.instagram.com/theloganbartlettshow 📱 X - https://twitter.com/loganbartshow 🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow About the Show Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member. Tap follow and enable notifications to stay ahead of the game.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Logan Bartlett?

  Welcome to the Logan Bartlett Show. On this episode, what you're going to hear is a conversation I have with co-founder and CEO of Datadog Olivier Pomel. Datadog is a $40 billion business doing over $2.5 billion in revenue, and they got there while only burning $25 million in capital. Olivier and I talk about that incredible journey, including the early days of Datadog and their decision to build a platform architecture that enabled modular applications to be built on top of their existing infrastructure. We also talk about a number of the different product-related considerations they make in building additional modules, how they think about scaling them in the early days, what the success metrics look like, how they empower the team, among a number of other considerations.

• 0:47Logan Bartlett?

  We also talk about how Olivier learned to acquiesce as a self-described "control freak" and give autonomy to his different leaders, including his belief that you should hire executives that are more peers than they are subordinates. A really fun conversation that you'll hear with Olivier here now. Olivier, thanks for doing this.

• 1:07Speaker 01

  Thank you for having me.

• 1:08Logan Bartlett?

  So maybe for people that don't know, can you describe what Datadog does?

• 1:12Speaker 01

  Oh, yes. So we do observability and security for companies that are into cloud or in the mix of cloud and on-premise. So we sell to engineering teams and we sell to companies that are big and small. So anything from one-person shops or students all the way up to the largest companies in the world. And started a company in 2010 in New York, went public in 2019. So it's been almost five years now.

• 1:38Logan Bartlett?

  And you all struggled to raise money in the early days. I'm curious, how did that inform the business that Datadog ultimately became?

• 1:47Speaker 01

  Yes, so that was kind of horrible in the beginning. So it was very hard to raise a seed round. And it was a seed round in 2010, 2011, which was a very far cry from what you'd call even a pre-seed round today.

• 2:00Speaker 01

  The so in the end, I think in looking back, anything that is a constraint or a hardship actually is a good for the future. If you survive it, you know, you're more likely to make it later. On our end, I think two things that we got from that was one, a real focus on solving a real problem. So we never felt like we were winning early on, but we had to get constant reinforcement that we're doing the right thing. So we focused on that until today. It's a core tenet of the company, make sure we solve real problems. And the second one is we're always super scared that we would not actually manage to raise more money. So we wanted to to build a business from day one that was very efficient.

• 2:41Speaker 01

  And that would be profitable in the end. And again, to this day, like and every single step on the way, even when it was a lot easier for us to raise capital, we've been very disciplined, very efficient. But I think those two things are really trace them back to the early days and the hardships we had.

• 2:58Logan Bartlett?

  Do you think that's a universal truth that like anything that is a struggle ultimately proves to be a benefit? If you get through it?

• 3:08Speaker 01

  Well, I think so, but on the other hand, you know, we're this this heavy survivorship bias. But yes, look, if if whenever you get asked, if you could look back and you know, change anything, would you change anything? Probably not.

• 3:22Logan Bartlett?

  Yeah.

• 3:22Speaker 01

  Like all the mistakes we've made, actually they got us there even before starting Datadog. So I before starting Datadog, I was at an educational software company for eight years. I think like four, five years into it, I really wanted to start my own company. I could not at the time because I was waiting for a green card and I had to stay put for a few years. I found that, you know, upsetting, you know, I really wanted to do other things. At the same time, all that time I spent there, getting my team, growing with that company, making mistakes, learning from my mistakes, actually led me to be a better founder and a better manager. And in the end, I think we probably were more successful because of it. So again, everything that came at a cost actually paid back later.

• 4:06Logan Bartlett?

  It's interesting. Yeah, it's hard to know the counterfactual, but I assume starting in. Two thousand seven, I don't I don't know if you actually had the same insight at that point in time, but the market would have been, you probably would have been too early to what ultimately became Dated Dog, or it would have been a different business in some way, shape or form.

• 4:20Speaker 01

  I mean, and thank goodness we didn't start a business there. I was, so I was starting with the same co-founder. And our idea at the time was a platform to look for apartments for rent or something like that. So I think, I think we're much better off not having started.

• 4:33Logan Bartlett?

  And especially if you struggled to raise in two thousand ten, two thousand seven, two thousand eight was not the best time for for fundraising.

• 4:39Speaker 01

  Probably not.

• 4:40Logan Bartlett?

  Yeah, yeah. So what was that core insight in the early days of Dated Dog?

• 4:43Speaker 01

  The core insight was really, it was a problem we actually had, you know, which was, in our previous company, I was running Dev Team, I was Alexi, my co-founder at Dated Dog, was running the Operations Team. And we started as a small startup, so we basically hired everyone on our teams. We were very good friends to start with. We had worked in three different companies before together. And we also tried hard not to hire any jerks. And yet, you know, we still ended up down the road with operations that hated development, development that hated operations, and the teams pointing fingers at each other all the time. So the starting point was really, let's bring them into the same platform, let's have them see the world the same way. Let's get them to work together. That was the starting point for Dated Dog.

• 5:27Logan Bartlett?

  And and you did something unusual where you didn't actually write a single line of code for the first six months. Was that purposeful in going about building business?

• 5:36Speaker 01

  Yes, it was very purposeful. It was actually before we even struggled to raise money. And at the time, our biggest fear was not to not to manage to build software, it was to build the wrong thing. Basically solve a problem that didn't exist. And that was informed with some of our past experience, you know, so I had worked, we had worked actually together at an email startup at the peak of the dot com boom, and then during a good part of the bust too. And we had built that great product, but we hadn't really talked to any users and you know, it sort of kind of failed to launch in a way because of that. Even though we thought the product was pretty amazing, we thought it was really Gmail before its time.

• 6:18Speaker 01

  And after that, you know, building software for teachers really, it really drilled into us the fact that you can't just, you know, make up yourself what you need to build. You need to talk to users, you need to understand what they're actually going to do with it, what goes through their mind, what's their sense of the problem, what did they do during their days? And so we were very scared when I started a company of building the wrong thing. And we over indexed heavily on talking to customers, making sure that we solve every problem for them and that we would not just produce waste.

• 6:49Logan Bartlett?

  Yeah, I heard you say something funny that when you don't have a product, everyone wants to talk to you and tell you about your problems. So when you do have a product, they they start shutting down or it becomes a little bit more, you know, you're on the other side of the table. Can you speak to that point?

• 7:03Speaker 01

  Yes, I mean, so here's the thing, we so we sell to engineers. And engineers really don't like being sold to. And anytime you come to an interaction and it kind of looks like a sales interaction because you have something to pitch, it kind of, you know, it it deepens it, it it dirties it, you know, so people are not as open. Whereas when you hear to really talk about problems completely from, in an open and in a way, completely from the customer's point of view or the prospect's point of view, people are way more open. So when we didn't have anything to bring to the table, everybody, including important people at large companies, were very open to spend time with us and share what was working for them, not working for them,

• 7:47Speaker 01

  what they hope the industry would become later. When we had something that they might try, it became more difficult.

• 7:54Logan Bartlett?

  And so in the early days, you you launched with a alpha version of the product and then ultimately moved to a much more open beta. And I heard that that transition was particularly informative on like some selection bias of who was ultimately giving feedback on the product. Can you can you speak to that?

• 8:10Speaker 01

  Yes, so we we had a fairly protracted closed beta. So we know we had a waitlist and you would select who we thought was the best fit to get in. And then we'd spend time with them and. Traction was sort of middling when we did that. I think we we had a lot of people on board on the platform this way, spend a bit of time, forget to come back. Up to a point where we were actually worried that we were just not going to, it was not going to work. So we said, you know, hey, what the hell, let's open the platform. We're super worried that, you know, it was still embarrassing enough as a product that, you know, the whole world would see it and, you know, and they would, they would see how horrible it was and get turned off and it would be the end of it.

• 8:54Speaker 01

  Of course, that was not the case, right? Of course, when we opened it up wider, we actually let it to our, whoever was coming upon the product to self-select and decide whether they were going to spend more time on it or not. Turns out it worked a lot better than us trying to figure out who's the best fit at the right time, with the right image of problem, and he's going to remember to come back tomorrow. So that's when the traction study began. For anyone who's wondering, I mean, in general, and that's actually the advice I get to the founders I advise or companies I invest in. The chances that your product takes the world by storm and everybody realizes it's horrible are non-existent.

• 9:35Speaker 01

  What's most likely to happen is that you'll get a little spike of attention when you open it up and then it will quickly die down. But at least you'll get some users and be able to to build upon that.

• 9:46Logan Bartlett?

  So in the early days, you had this vision of bringing DevOps together. And the cloud was, was the cloud just a derivative consideration of who self-selected and opted in to that?

• 9:59Speaker 01

  Yes, the starting point was DevOps. And then as an extension, there was this idea that we would build a platform. And really that we would bring different personas, different use cases into that platform. We actually were very careful not to use the word monitoring initially because to us it was a work from the past, it was, you know, what happened in the 90s and it was reduced, reductive. And we had broader ambitions than that. The fact that we serve the cloud was just, you know, cloud was what was new at the time, what the new companies were adopting, but it was still very much a toy. Like even in 2010,

• 10:38Speaker 01

  companies with any form of maturity or real scale were not building on the cloud. That happened after that. So you could say that the, the, the success we've had and the success that the cloud adoption has given us, that was largely timing and luck. We didn't understand initially that, or we didn't dream that the cloud would be so broadly adopted and we take over pretty much every single segment everywhere. And also the thing we didn't fully realize initially was how bringing DevOps together was actually at the heart of that transformation, that, you know, being able to provision your own infrastructure yourself as a developer,

• 11:20Speaker 01

  completely redistributed the, the roles and brought those two different teams where close and nowhere to be. So having a platform that empowered companies to do that was actually super, super important at the time and it was a big key to our success.

• 11:34Logan Bartlett?

  And, and so you, you spoke about this or touched on this earlier, but you, you wanted to be, you wanted the platform or to build out a platform and be able to be modular over time. What, what decisions did you make in the early days that set that up for success?

• 11:51Speaker 01

  So, I mean, we, everything was always meant to be a platform. We avoided hardcoded data models for most of it. We had this very flexible way of tagging to bring data together. We always anticipated having new data sets, new, new modalities for data to get in now. And that was almost to a fault. When we started, it was an open platform and you could put all this data in and we started putting some monitoring data into it initially, but we didn't call it the monitoring product. And as a result, it was very difficult for our customers to understand what they were supposed to do with it and why they should buy. Um, so we had to go through a phase, you know, about it, I would say a few months after we opened

• 12:35Speaker 01

  up the product in a, in, in public beta, where we had to actually say, yeah, actually, no, it's, it's a monitoring product. Uh, it's not a, you know, collaboration platform between Dev and Ops, like everybody loves the idea, but you know, nobody is able to explain to their boss why they should pay for that. Whereas it is monitoring, uh, everybody understands. Um, and that's straightforward. You make a case for it, you get it purchased and then off we go.

• 12:58Logan Bartlett?

  Did the trade offs in those early days of like being first principled and, uh, going to where the market was, was ultimately, or you thought it was heading over time, did that feel, um, maybe overly religious and not pragmatic? Uh, cause there's the tension of just like solving the immediate problem versus building to where the, you think the market can go. And so I, I, how did you think about that?

• 13:19Speaker 01

  Yeah, I mean, there was definitely, and, and that's actually a, um, a concern that had been relayed to us on multiple occasions by the investment community, uh, which was, hey, everybody who's successful in this space is building a, a thin, uh, product. Like, uh, they start with a wedge, um, and they, they build this slice for this very specific role that does a very specific thing and that's who they get adopted. And then maybe they can grow from there. Whereas, you know, we started with this very horizontal, you know, like we have this broad platform and it does a lot of things, uh, but it doesn't just try to replace a thin slice of the ecosystem at once. I think it took a while for this vision to really, um, uh, prove itself.

• 14:04Speaker 01

  So we did some of that when our cloud monitoring product became successful on infrastructure monitoring, uh, but even then I think it was still seen as one category. Maybe we were, you know, redefining that category a little bit. Maybe when you look at all product and when you looked at the monitoring products from the nineties, it didn't look the same, uh, but still it was considered one, one category. I think in the late 2010s, like before we went public, what really happened is that we were able to layer more products on top of that. We had, we had a long management product, an APM product, a number of other products in different areas and then different personas with security, with phenops and things like that. And that's when we've actually proven that that platform vision actually works.

• 14:49Logan Bartlett?

  And you have an interesting view of the modularity of individual products. And there's this tension that exists between those best of breed solutions and the complexity, but full-featured set that they have versus the simplicity and ease of use that you're trying to offer within a suite. Can you talk about the tensions and sort of how you think about getting to a fully-featured product in the Datadog world?

• 15:12Speaker 01

  Yeah, so I mean, first of all, the goal with every single product we ship is in the end, on its own, even separate from everything else we do, it should be best of breed. So it should, you know, beat the hell out of anything out there. But it's going to get time, it'll take time to get there. Most of the categories we enter have a lot of existing players and they've been building features for these categories for, you know, 10 years. And there's a lot, title stakes are typically pretty high to start playing there. And then there's a lot of stuff you need to build to be the best product there. But what we see and what we win is that when you have a broad platform approach

• 15:51Speaker 01

  and when the thin category you replace is completely integrated with everything else, all of the other categories, and they share the same user base as opposed to having, you know, you have five engineers in one thing and then five others using another thing. And then, you know, they actually have no idea what goes across these boundaries.

• 16:11Speaker 01

  When you have all of that, like it becomes irrational for customers, you know, to just not buy everything as part of a platform. So that's been the The path we've been on.

• 16:22Logan Bartlett?

  In the first initial couple adjacencies that you went into or additional products, you were solving for that same buyer set. And so you built out more modules within the same buyer set. And then ultimately you moved into different buyer sets. Yeah. How did you think about that?

• 16:38Speaker 01

  Yeah, so I mean, we initially was the same buyers. So the operations folks and the CIO, CTO, depending on the company, buying what we're doing. Today, we might have some different buyers, you know, the CISO, for example. But the motion remains the same, like we still get adopted largely bottom up on top of our platform. When we sell to security customers today, we typically land with observability and then expand into security from there. But we'd say the same fundamentals remain.

• 17:05Logan Bartlett?

  So if someone, if a founder is listening to this or a team, would you recommend focusing on, I mean, obviously some selection bias here, but focusing on that persona and building the functional areas within the persona versus taking the product? I guess the opposite would be kind of be the ServiceNow model, maybe where they've expanded into other personas over time with the same product.

• 17:27Speaker 01

  I would say at the end, you probably need to do both.

• 17:30Logan Bartlett?

  Yeah.

• 17:31Speaker 01

  But there's different levels of risks and different levels of reward, you know, with those. When you expand within the same persona, the risk is that you run out of things you can do for them. Or, you know, you run out of incremental wallet share you can get, you know, and that can be problematic in the long run. When you expand to different personas, like typically you see these big open spaces, like it has all these problems, all these reasons why we should be successful at solving them. But turns out it's always harder than it looks. You have to discover the users, you have to build credibility with new parts of the organization. So it takes more time, like anything else. And when you, when you invest, you have different amounts of risks and rewards for everything you can do. And it can take a lot longer for certain of those.

• 18:16Speaker 01

  Endeavors to prove themselves and we've seen that with our products.

• 18:20Logan Bartlett?

  In structuring those new product teams, how do you go about thinking how to empower these individual teams on their own? And give autonomy to make decisions while maintaining the same shared service or infrastructure? Or design or feel of what the product is that is data dog?

• 18:39Speaker 01

  Yeah, so there's always a lot of push and pull when you have like, like is our case, but half of the engineering team or product team works on the platform and the other half works on the specific products. But there's a lot, a lot of push and pull between those two sides, you know? So you need to be able to create new things from scratch without having them be platform wide initiatives. And at the same time, you need to keep running things in into the platform. So that we don't have to have different ways of, of, you know, doing the same thing for different products. So I mean, the way we do that is by having small teams, building products, focusing what matters the most for those products. If what matters the most ends up being very platformy in the end, you know, we'll, we'll, you know, push it back to the platform.

• 19:24Speaker 01

  If it is not the case, then, you know, there's no problem. And we're fairly disciplined also in terms of who we evaluate the maturity of the virus things we're trying, you know? So anything we do might start as an experiment, then we might try to scale it and we might try to standardize on it. And at some point we might try to stop using it and then retire it in the end altogether. So we have to, you know, be fairly explicit about where everything we do, whether that's a UI pattern or a data store that we've built, where it stands in that lifecycle and, you know, what we're going to keep doing. But it's okay to experiment.

• 19:57Logan Bartlett?

  Is there a single leader that ultimately gets tasked with kind of being the GM of those new products as they get going?

• 20:03Speaker 01

  So we have different product teams are looking after different products and we try, the way we build product is. We we keep those teams very small and we start building with customers as soon as possible. So we're not spending two years, you know, in a secret lab with a team of 500 building a product. And then we have a big reveal and voila, that's it, you know. The way it works is we're going to start with a team of 10 or seven or five. And from the very first day, they're going to involve some customers to try and figure out what problem we actually need to solve for them. And then there, as they keep building, they keep adding more of these customers and spending more time with them.

• 20:46Speaker 01

  And as those products more get more successful or as we get more clarity on what the roadmap needs to be and what value there is in delivering that roadmap, we can grow those teams and increase the velocity.

• 21:01Logan Bartlett?

  How do you think of the the product success in the early days? Or the what are the different inflection points of more investment versus maybe pulling back?

• 21:11Speaker 01

  First of all, it's really hard. When you have an organization that has a product that is very successful or several products that are very successful, which was our case, it's very hard to start new products. Because it's a completely different mode for people. They need to go from being mostly successful to being mostly unsuccessful. And it took us some time also to realize that, like, actually, we need to treat that a little bit differently. Because you don't want people to feel like they're on the end of their career because they went from that thing everybody wanted to, okay, it's not working yet, you know. So is it because it's your fault? So I think there's a different, there are definitely a different mode there.

• 21:52Logan Bartlett?

  And so in empowering that or enabling people to do that, you've also had success in acquiring businesses.

• 22:00Speaker 01

  Yeah.

• 22:00Logan Bartlett?

  That it seems like there's a profile or type of business that,

• 22:06Logan Bartlett?

  you know, something in a new area that maybe the founders still want to stay on board and are excited about being a part of the Datadog platform versus just exiting for financial considerations. I mean, how did you sort of build this and think about that funnel? And when to acquire versus when to organically build out?

• 22:22Speaker 01

  Yes, so our approach to M and A is that so there's a very broad roadmap we're going after. We build this broad platform across different categories, across different personas.

• 22:33Speaker 01

  So there's no shortage of things to build and probably more than we can build ourselves with the same amount of time. There's no specific part of it we think we should acquire. Um. So we're fairly open in terms of what we can acquire or not. Uh. Instead, we're constrained by the universe of companies we can acquire, and that would make sense for us. So we build that funnel of do these companies correspond to things we what we are interested in? Yes, no. Um. Do we like what they're building? Yes, no. Uh. Do we think they would sell for the right reasons? And the right reasons for us are they want to build, but with more leverage.

• 23:07Logan Bartlett?

  Hmm.

• 23:07Speaker 01

  So we're never interested in hey, they've built something, but they're sick and tired and they want they want out. The fine reason to sell, but it's not the great reason for us to buy.

• 23:16Logan Bartlett?

  Hmm.

• 23:17Speaker 01

  Um. So we are we're looking for all that. Then we look for, you know, the cultural fee, all the other things, etc. So by the time, I mean, when you start, you have, you know, thousands of companies in your funnel. By the time you get to the bottom of it, like there's a handful. Um. And, you know, maybe one every now and then works, and that's what we've been doing so far. It doesn't constrain us on the size of companies we're buying. So we we look at companies that are tiny, we look at much larger ones. Um. But naturally, there are many less larger ones. So the chance that you'll all the planets will align, you know, with a much larger company are, you know, few and far between.

• 23:52Logan Bartlett?

  And and in post-acquisition, you you have a, uh, I mean, at least I had heard you you rewrite the product onto the Datadog platform and then try to ship something quickly just to prove that you you can, I I think. Uh, how how do you go about doing that?

• 24:08Speaker 01

  Yeah. So I mean, first, we replatform everything we need to replatform. Uh. Depending on how these products are built, maybe some components leave on the customer uh environments that we don't need to rewrite all that, for example. But all of the backends, the UIs, everything else needs to be tightly integrated with what we do. So we replatform everything. Um. There's a cost that comes to that with that. Uh. But when you think of the value we deliver in the end, what we sell, that that's actually core core to what we are, and that's why we uh we make those acquisitions very successful in the long run. Now, when we buy a company, the only thing that matters is what happens after we sign, right? The deal itself is easy. Um. The all of that part, fine, everybody will do it.

• 24:54Speaker 01

  What happens in the year that's that that follows is where all of attention is even when we actually work on the deal itself. And for that, we like to very, very short views on the acquisition to start showing some value. And so we do it, we do a three, six, nine month plan, you know. The goal is we need to ship something in three months, which is a very short amount of time after an acquisition. And it could be anything, really. Could be something internal, maybe something we show to some customers. It doesn't really matter what it is, it matters that it's in three months. And what it does is that it creates urgency from the moment we close a deal, both from the company we acquired, but also from the rest of our team,

• 25:38Speaker 01

  to actually get together, integrate, you know, get things working and have something to show. And it it creates some confidence on both sides too. Like it shows to the new team that actually, yeah, I mean, we're welcome here and we can get moving and it's fast and it's great. And it shows to the the existing team, hey, those people we just brought in, they actually know what they're doing and they're helping us move faster. Because the biggest risk when you do M and A is not that you waste the money you spend on acquiring a company. The biggest risk is that you sort of kneecap the rest of your R and D organization. Because they think, okay, you know, why did we acquire this thing?

• 26:20Speaker 01

  You know, it's not helpful, these people are not working out, or you know, they're resting, investing, you know, what, what, why am I, you know, working so hard? So it's very, very important to build that confidence and show that value very early on.

• 26:33Logan Bartlett?

  For a very data-driven company and product, I'm told that you all are maybe a little bit more intuitive on your own insights on the product side and listen to more qualitative feedback from the customer rather than quantitative data from the customer? Because maybe that's a lagging indicator. Can you, can you talk a little bit about this?

• 26:55Speaker 01

  I mean, first, first of all, I mean, we're still knee deep in data, right?

• 26:58Logan Bartlett?

  Sure. I imagine this is a very relative.

• 27:00Speaker 01

  Yeah, most of what we do and the products we build are informed by reams of data and customer activity and what we see them doing and where we see the value. So there's, there's a lot of that. That being said.

• 27:15Speaker 01

  I I am personally uh very careful about using data where eh you know it is not meaningful. Um so I'm uh you know I torture people when they you know present you know percentages from a with an N of seven and and two digits to the percentage and things like that. Like it's uh it's not because you have three numbers that it makes sense to compute an average, basically. Um to your point also, it's very hard to understand for a lot of what we do, whether a number is a leading indicator or like a lagging indicator. And a lot of the tendencies folks have, you know, when they look at customer activity and things like that, is to mistake the two.

• 27:53Speaker 01

  You know, so the uh the analogy there is, you know, it's a doctor that's uh you know, just start paying attention when you don't have any pulse anymore. Like I said, it's a little bit late. But most of the customer metrics you can get uh you know, kind of looked at that in the end. So it's a that. One last piece that's interesting to mention is that uh data is not going to help you make your products simple. Um it's a you customers are not going to, the moment you start getting into uh questions about, you know, is it simple and other things like that. Customers don't really respond well to this kind of questions. There's not a lot of of uh information in the answers they're going to give you because asking the question itself just uh creates more work in their mind than they would just looking at a product and you know.

• 28:38Speaker 01

  So it's very hard um I think to uh to keep a product simple just by looking at at data. To simplify a product, I think you have to edit, and I think editing uh is something that is done in a more, you know, um uh let's say a uh opinionated way than it is not coming from the data.

• 28:58Logan Bartlett?

  Hmm. How do you empower the the teams to to make those uh decisions? Cause I can imagine you have an artisanal taste to that and and the team does, but at an individual level, how are they empowered to really have this design feel?

• 29:15Speaker 01

  Uh I mean, look, empowering the teams is a is this the only way to scale, right? Um so I I said earlier, we're building new products with small teams. Um and you know, those teams need to be able to move. They need to be able to change things. They need to be able to create new paradigms. Whether in the end, we we carry those paradigms over to the rest of the product, whether we choose to retire to retire them after testing them, whether we think something is just, just, you know, too confusing and, you know, we need to take another pass at it. I think that that's part of the, you know, product lifecycle. But yes, I mean, the the goal as we scale is to keep pushing the decisions down as much as possible and empower people so they can keep running on their own.

• 29:55Logan Bartlett?

  Early on, you you built a foundation for machine learning, I guess what we call artificial intelligence today. What did you learn about scaling up a AI infrastructure and building in the early days or early-ish days of what now would be considered that category?

• 30:12Speaker 01

  Yes, it's interesting, right? I mean, when we started, AI was a dirty word. It was mostly used as a, you know, bullshit buzzword.

• 30:20Logan Bartlett?

  Yes, it was pejorative in some ways.

• 30:23Speaker 01

  Exactly, yes. And now, now it's fashionable again. But give it a few years, I think maybe we'll we'll we'll some back and forth on what's what's fashionable and what's not. I would say the the the biggest learning from applying machine learning on the the rims of data we have and with our specific type of user is that precision matters a lot more than recall for everything we do. The lies, so to speak, customers will tell you about AI in our field, or will tell us about AI, is that if I have, if I'm given the choice between the false positive and the false negative, I prefer the false positive because you send it to me,

• 31:09Speaker 01

  send me the the IDD inside and whatever the machine found out for me, and I'll decide whether it's it's appropriate or not. The reality is you send two false positives in a row and people just turn you off and they never pay attention ever again.

• 31:23Logan Bartlett?

  They start ignoring.

• 31:23Speaker 01

  Yes, and that's been the the the challenge, the biggest challenge actually with incorporating AI in the in the workflow is the interface with the humans. Make sure that you build the right trust relationship and that, you know, you you actually, you know, help people get to the next step thanks to that. And I would say today, that's still pretty much the frontier we need to cross, even with the, I mean, there's so many new advances now. I mean, I don't need to

• 31:53Speaker 01

  say a lot more about that, but there's so many new doors that are open, new things we can do. Higher expectations also from the user base and the customer base. I think the biggest issue is still, how do we build that trust relationship with the user? And how do we have them actually lean hard on the automation of the AI?

• 32:15Logan Bartlett?

  Yeah, it's interesting because you see a lot of the creative things that have like infinite iterations and are a little bit more abstract. And it's awe inspiring to see that, but you don't, in the enterprise, you need accuracy. And you can't have hallucinations. A hallucination in an ideogram or mid-journey picture is, that's art. A hallucination in your system is an error that is going to lose a lot of trust.

• 32:43Speaker 01

  Yeah, exactly. I mean, also, look, if the machine generates a dog with three legs, let you spot that immediately and you reroll it and that's fine, you know. Whereas if the machine gives you a complex interpretation of what's going on between your your applications and, you know, and what's not, you're not going to be able to see the issue right away. And so if it turns out there's actually an issue, you're going to lose a lot of trust. If the customer goes down the wrong path and spends, you know, half an hour doing that. It's perfectly fine for people to go down the wrong path based on what humans tell them. But they are very unwilling to do that based on what machines tell them. But I think the bar is really high in terms of delivering value there.

• 33:26Logan Bartlett?

  I heard you talk about how a lot of companies end up being sales driven in their culture or engineering driven, and you all wanted to be customer centric or customer driven in that. Can you speak to the trade offs of orienting in one direction of sales versus engineering? And how you guys prioritize being customer driven?

• 33:47Speaker 01

  Yeah, I think it's a, it requires constant discipline and constant attention. Because it's very easy for a company to veer one side or the other. Uh, you know, to caricature, uh, if you go sales driven, uh, you'll do whatever you need to close the next deal. Um, that's what's going to drive the roadmap. Um, that's what's going to drive the economics and everything. Uh, whereas if you're engineering driven, uh, you know, you start with a solution, you build this beautiful tower. Um, and then, you know, maybe you let the customers in, but only if it doesn't detract from the, uh, the beautiful, uh, journey we're on and the destination we've set already. And I think for, um, what we do in particular, we can't have either of those.

• 34:32Speaker 01

  Uh, we need to start with a customer. We need to start with, instead of starting with a solution, instead of starting with the money, we need to start with a problem, like what's their problem? What, what do they have in their environment, in their life? That is horrible. And how can we make it better? How do we show value there? That's the, uh, uh, the core of what we need to focus everything on.

• 34:51Logan Bartlett?

  Hmm.

• 34:51Speaker 01

  And it's, uh, it goes beyond what can we do for them today also, which is, well, what can we do that's actually going to be great two or three or five years from now? Which means for that to be great five years from now, we, we need to be a great company five years from now. Which means we need to have good economics, we need to invest, we need to build like, you know, so it, really everything has to be seen through the prism of, oh, do we, like, if we project ourselves, uh, five years from now with that customer, who is everything amazing for everyone?

• 35:22Logan Bartlett?

  Hmm. Uh, the manifestation of this within the, the company, I've heard there's some examples like engineering rotating through on support, uh, maybe for a week a year or something. I'm not sure if that's, if that's still the case, but are there, are there things you've done from a cultural standpoint to, to enable this within the organization?

• 35:41Speaker 01

  Yeah. So we, we did actually for a, for a long time, we had engineering rotate into support. Uh, we don't do it anymore because it's a, uh, it was too many people trying to fill the system. Uh, it was a bit too complex to run at scale. But in the idea behind it was really to close that feedback loop. Like we're big fans of feedback loops. Uh, but to close the feedback loop between, hey, I had a great idea and I implemented in the product. And then seeing what it actually works in practice. And it turns out, like always, like, you know, some genius ideas are actually amazing. And some others actually not that great. And you only realize that after the fact. Everything, by the way, everything I keep telling, you know, everyone who listen that, but

• 36:23Speaker 01

  everything you find that is horrible in the world around you started as a genius idea. The intent was not to make everybody miserable.

• 36:31Logan Bartlett?

  Your go to market initially was bottoms up. I know you don't like the term product led, but that seems to be one of the terms that's in vogue today. How did you make the decision to go about selling that way with the, I guess, the alpha, the beta? And then from there?

• 36:46Speaker 01

  Initially, look, we had no sales team. And we were selling also to cloud companies and cloud companies in 2010 were smaller, newer tech companies. So bottom up motion was just completely unnatural. That was the only thing that would make sense for that. And that's what we started with. It turns out as we scaled and as the other part of the market came to the cloud, like, you know, later on, enterprises, big enterprises started moving to the cloud, things like that. When that happened, bottom ups was still mostly the way we went to market. And we've tried, you know, various ways of top down also.

• 37:28Speaker 01

  You know, the earlier thing we tried in top down, for example, was when we started raising money from big VC investors. Everybody was eager to make a lot of intros and, you know, get us in front of their best and brightest companies. It never really worked great. Like, and the reason for that, and the reason we still sell bottom up, I think, even in the enterprise, is that we're the kind of product that is inherently, has to be the team's idea. Like, people need to want it. They need to feel a specific relationship to the product. The moment it's something that your boss's boss's boss passes down, like it's a suspect. So that's why all type of products with all type of users, they are much better bottom up.

• 38:13Logan Bartlett?

  And so then that sales motion, in the early days, it sounds like you all held off on hiring like traditional VP of Sales or salespeople for a while. Is that fair?

• 38:25Speaker 01

  Yes, yes, we, we were, we wanted to, to figure out what the right way was to go to market. Like, we didn't want to rebuild an enterprise sales force from the very beginning in the same exact way every other company had built them. Some of that might have been, you know, we were just a bit naive and, you know, clueless. But, you know, if you fast forward to where we are today, it actually yielded better economics and much, a much tighter sales process in general. I would say, I don't know if I would recommend it to everyone to do exactly the same way. I think now you probably have a pretty good idea of how every single piece of, you know, software, whether that's for developers, cloud consumption, not consumption, everything can be sold.

• 39:10Speaker 01

  But at the time, at least, it made a lot of sense and it took diligence for us to do so.

• 39:14Logan Bartlett?

  One of the things that in going bottom up versus going more enterprise oriented is, or outbound, uh, sales oriented is you, you don't have the same number of dials that you can pull necessarily. Like, most of our enterprise businesses, you hire more reps and that's the way you, you grow the business. What were the dials in the early days that you all thought about to actually generate more leads? And make sure that you're, you're able to scale?

• 39:40Speaker 01

  Well, so here's the thing, I mean, those dials still apply. So that's why I don't like product led growth because it actually, it takes both product and sales. The, um, the sales team still is going to get you in front of customers. There's still a coverage model. There's still, you know, a math of how many, how many reps do you have? And, you know, what part of the world do they cover? And productive are there? And all of that stuff. Uh, but the main difference is that you don't just talk to the, uh, the, the C, the, the CIO, the CISO, the CTO. You actually talk to the people in, in the trenches who do the work. And they're the ones adopting and they're the ones growing the product and all of that stuff. Earlier on, very early, um, we were, like many super early companies, were inbound driven.

• 40:26Speaker 01

  So we would produce marketing, um, in the form of, you know, events or blog posts and things like that. And then we'd get people signing up on the product and then adopt it and then go. I think that model for us, pretty much for everyone, at some point plateaus. Like you reach a point where your natural reach with the content and the events and everything else is limited. Uh, and then you need to go further to actually find customers and wherever they are. It's actually a pretty traumatic uh transformation to go from inbound to outbound. Uh, most companies struggle with that uh because it's a different culture, different sales culture. It's a um, there's different metrics, different everything that relate to that. So uh, I remember it was a hard time when we did that.

• 41:11Speaker 01

  But again, to scale past a certain point, I think there's no, no other choice.

• 41:15Logan Bartlett?

  About how big were you when you started layering on the outbound?

• 41:18Speaker 01

  Uh, I think we, we did that uh, um, maybe we were a hundred million revenues.

• 41:25Logan Bartlett?

  So, so pretty late on a relative basis. Um, in generating the content, uh, a lot of technical companies will try to do rotations for engineers, produce one blog post a month or whatever. And inevitably it falls down the, the stack rank and then they're not happy with the product and, and um, that they're actually putting out there. And so you guys had a, a different, maybe tried that and then ultimately hired people specifically dedicated to producing content?

• 41:52Speaker 01

  Yes, we did. And the problem was um, it's actually hard to operationalize having engineers produce content. And what we found also is that it's not, it can be very, a very uh unproductive use of people's time. Because people might be excited about writing, uh, but uh, they might not actually enjoy writing all that much, writing all that much. So you'll see people um, you know, spending their day in front of the, in front of the editor, typing almost nothing. Um, and just because it's painful to start writing. So we, we decided not to do that. Uh, we stopped doing that. So today we have a team of writers uh that are at the intersection of tech and journalism typically.

• 42:37Speaker 01

  We look for people with both experiences and they're the ones, uh, they actually can't be marketers. They can't be non-technical, otherwise the content just doesn't come out right. Uh, and they're going to spend time also with internal teams. They're going to spend time interviewing people internally, they're going to spend time uh validating everything they put in there. They're going to spend time researching with the data, uh, and they're going to produce content there. We still have engineers on a occasional basis publish. But that's more, it's more than nice to have if people are motivated, if they have something they want to say, if they have, if it's not too painful to them. But we don't rely on the team to do that too much, otherwise.

• 43:15Logan Bartlett?

  In building a go-to-market motion as a technical founder, was there something that was particularly counterintuitive to you that you learned along the way?

• 43:25Speaker 01

  I don't think there is anything that was so counterintuitive. I think, look, the way I see, these are all systems. And your systems have feedback loops and they're instrumented in some ways. I think the thing that's the hardest to

• 43:43Speaker 01

  come to grips with as you build the sales systems is that while they are extremely well instrumented, like everything that happens is captured in a CRM, all the interactions, you know, you have increased numbers and just about anything. The numbers are pretty lumpy and pretty hard to interpret over short periods of times. So even though you can measure to, with, you know, with many decimals, actually the productivity of your team and the conversion rates and all that, that you actually need to let things play out for quite a while before you get an idea of what's working, what's not working.

• 44:18Logan Bartlett?

  Yeah, the architecture point, that your sales organization has an architecture similar to your engineering

• 44:24Speaker 01

  Yes.

• 44:25Logan Bartlett?

  team or is building an architecture or is architected in some way. It's a, there's a lot of parallels between the two and making sure the systems don't have breakpoints or single points of failure or anything.

• 44:35Speaker 01

  Yes, and look, it's very important to build the feedback loops everywhere. So how do we know that what we're doing is working? How do we know we're spending our time in the right place? If we did something horrible, how will we know about it? Like it's, it's hard and when, you know, I think, you know, one thing we've done, for example, very early on is that we have these very short-term contracts with customers and we still do that today, you know, we can buy our product month to month. Even if you buy on or if you're in a long, a multi-year contract with their dog. You can use any of our product for, you know, a month if you want and then turn out of it and use another one instead. I think what it gives us is this very, very, very quick feedback on whether the product is good enough and valuable enough.

• 45:20Speaker 01

  And that helps us improve very quickly. It helps us get bad news in a way that's impossible to ignore because, you know, as you grow as a company, you train yourself to ignore bad news.

• 45:35Speaker 01

  And you have this opportunity right now to fix it. The flip side is, or the alternative is, you you sign everybody on a three-year contract. And if they stop using the product in month two, you don't have to face the hard truth, you know, for another three years. And by then it's too late to actually fix your product. You haven't, you know, put that into the feedback loop.

• 46:01Logan Bartlett?

  I've heard you describe yourself as a little bit of a control freak in the early days. How did you learn to empower the teams as you scaled? You touched on it a little bit earlier, but it maybe didn't come naturally.

• 46:15Speaker 01

  Yeah, well, I mean, look, I think many founders can relate. Like typically, when you start a company, you might be a bit of a control freak. It happens. I've seen it a number of times.

• 46:25Speaker 01

  I think for me, it was a very new

• 46:29Speaker 01

  and good experience to learn to give space to people around me. So I knew how to do that already with my co-founder. We had, we knew each other, we had worked together for a very long time. That was not an issue. But adding more people to the mix, I think, was more difficult. Very early on, like we had hired the

• 46:49Speaker 01

  head of product. And I was butting heads with him all the time because, anyway, he was a control freak. I was a control freak. We were going on each other all the time. And I think it almost blew up in the first three months. At which point we decided to hit reset. Um, and that's actually how I learned to, uh, to give room. And that's something I've kept doing after that, as I've added, I've added more, um, uh, people to manage, senior management teams, so people that report directly to me. I find that I should be able to think as the people on my team, as peers. They're not people I manage. If I have to manage them or their career, it's probably not the right fit. Uh, I should let them do their thing and we should talk about, okay, what's best for business? What do we do next, et cetera?

• 47:34Speaker 01

  Maybe we can disagree on some things, but here they are doing their job. Um, they're peers, I trust them and have the space. I shouldn't be making decisions for them. Um, I encourage everyone who's building a management team to try and think that way. I think it's liberating and I think it's, uh, also have you set the bar, the right level when you hire.

• 47:54Logan Bartlett?

  Is that early product leader still with the company today?

• 47:57Speaker 01

  Yes, though he's retiring at the end of the year.

• 47:59Logan Bartlett?

  Okay, is it, this is an Ammit, is it?

• 48:00Speaker 01

  This is Ammit, yes.

• 48:01Logan Bartlett?

  Oh, wow.

• 48:02Speaker 01

  Yeah.

• 48:02Logan Bartlett?

  So, so, so, I mean, for people that don't know, Amit was a quasi co-founder in the early days. I've heard, uh, and he, he, a lot of stuff rolls through him. It's not just chief product officer.

• 48:13Speaker 01

  Yeah, he did, he did a lot of the customer stuff for a lot of time too, which for us is very related. Uh, again, the product is all about what the customer thinks of it and the pro, and the problem we solve. So we thought actually the two were very, very related. Um, and, uh, yes, and so that, that was a very productive relationship for the company. And, uh, and that almost blew up, completely blew up, uh, in a, in a firework, uh, in about two months in.

• 48:39Logan Bartlett?

  Wow, that's, that's amazing. Um, you, you still do things though to stay close to the customers despite, uh, or close to the business despite the empowerment. And I've heard there's some tactical things you'll do going through, maybe still support tickets or reading employee reviews. Uh, can you speak to the ways at which you stay close while still empowering people?

• 48:57Speaker 01

  Yes, I think it's a, so there's two things. Uh, one is, um, data should flow up to management from everywhere. Like there shouldn't be any filtering. Uh, of course you'll, you'll have these filter views anyway, like people will bring, uh, will build retrospectives and reports and things like that. But you should be able to get data from every single layer of the organization. I see it as important to sample data from just every single part of the org. Because that's, at least to me, that's my contact with the reality. Like, what does the fabric really look like? When you read customer support issues, when you read transcripts of sales interactions, when you read, you know, what engineers are complaining about, complaining about on the engineering team. What does it look like?

• 49:42Speaker 01

  What do they talk about? And does that actually agree with the numbers and the reports and the other things I see? Like, are these, is it part of the same reality? I see that also as part of training the model. Like, you know, you, you, you're a machine, like, you keep, you keep seeing those things. What you see actually completely informs your perception of the world. So you can't just look at nicely polished, simplified, a little bit, you know, rose-tinted versions of the world that you might always see as a, as a, as an executive. So that's that. So that are all the way up. On the way down, though, it's very important to go through the management team. So you can't just go and go find somebody on the sales team and tell them, no, no, you do this, you do that.

• 50:26Speaker 01

  Like, you actually have to go through the management for that. Otherwise, when you don't do that, like, you completely, and you, you notify, I mean, you, physically, you disempower your management team. People know that they shouldn't listen to their managers. Instead, they should listen, they should wait for, you know, whoever, for the top to come and tell them something. But I think it's very important to, to have this asymmetry. You can get data from anywhere, but you can't act on anybody at any time. The other thing I found is that when you sample a lot of the data this way,

• 50:57Speaker 01

  everybody in the company starts caring about the details. So instead of looking up and trying to think, okay, so what do I need to show my boss? They start looking down and think, okay, so what's actually happening here? I better understand that. You know, the first time I, or people see an email from, you know, me or C-level or VP level about a very, very new issue with a customer and asking a question about it, the first question people ask themselves is, why, why is he or she looking at that? And then the second thing that comes after that is, okay, maybe I should probably know more about it than all these people up the chain, so let me look into that. And create this culture where people care a lot about what's actually happening, more so than what they're trying to pass up to the hierarchy.

• 51:43Logan Bartlett?

  Is it right that you all only ever burned $25 million roughly?

• 51:48Speaker 01

  Yeah.

• 51:48Logan Bartlett?

  And now you're approaching $3 billion run rate or something?

• 51:52Speaker 01

  It was still a two and a half billion, but yeah.

• 51:54Logan Bartlett?

  Two and a half billion, so approaching. I think that's, I can say that, maybe you can't. Outside of simply strong product market fit in the early days, what enabled that culture of cash conservation and efficiency in a way that maybe isn't obvious?

• 52:14Speaker 01

  Yeah, I mean, look, it's the culture of discipline in general.

• 52:20Speaker 01

  You, so first, whenever, like the easiest way to spend a lot of money as a company is to have too many things in your sales process. Because it's tempting to, you know, you spend more on marketing, you spend more on sales, you can, you can, there's always more things you can do, more people you can involve in your sales process.

• 52:39Speaker 01

  And so our approach there has been to be, to always understand before we add something to the sales process, what's actually load-bearing and what's not in the sales process? So maybe we're going to try one thing, we'll try it small, in a small fashion first, or we'll try it and then we'll remove it, then we'll see, you know, this is actually make a difference. The problem if you don't do that is you keep adding things. You know, typically in zero-interest worlds, the pressure is just to grow faster. So of course you're going to spend more and you're going to, you know, weigh all the different things. And the problem you get at the end of that is that you have no idea what part of it you can remove or not. And you're scared because, look, the pressure is still to grow after that.

• 53:24Speaker 01

  So you have to save money and you have to grow, but you don't question who can remove, so it's a bad situation. So on our end, we are always very disciplined about deciding what to add, what not to add, and keeping those economics fairly straightforward. We've also been very disciplined about not doing bad deals, you know, so we, when we look at

• 53:47Speaker 01

  competitive situations or customer situations, just because somebody else wants to do something that, you know, everything's stupid, doesn't mean we should be doing it. And again, if you look back at the, what I said earlier, about solving problems for customers in the, not just for today, but for the long run, like this is the right thing, like if we want to be. The best company and the best solution for our customers and make a big difference for them. Five years from now, we need to keep being investing in our product for those five years. There's a cold hard math to that and I can come back to that. And that means we shouldn't actually be doing bad deals. That's part of it.

• 54:25Logan Bartlett?

  So that phrase load bearing, to make sure I understand the point. So you would keep the sales organizations more nimble and figure out what were the mission critical functionalities within that and not layering with sales engineers or solution architects or whatever it is around the process because then it muddles the what is actually mission critical?

• 54:48Speaker 01

  Well, the sales engineers, of course, you always, I mean, you're going to have sales engineers, you're going to have all of these things, right? But then you can, you can say, hey, we need for these products, we need specialists that are going to sell them because they need to understand the product better. So then you need to scale another organization on top of it and you have these more people that show up in the customer meetings. Maybe you're going to say, hey, we, we do a better job with customers when we can do an assessment of the business value, which we also do, by the way. But then the question is, how do you, how much you scale that? Do you also end up having one more person that comes to the meetings with customer every single time because they're here to assess the business value?

• 55:30Speaker 01

  So these are all these different things you can do and they all make sense, you know, individually. But if you're not disciplined about what understanding, what makes, what actually makes a difference when for which customer in which situation, you end up scaling all of those organizations at the same time. And you end up in situations such as, you know, before I studied Datadog in my previous company, we tried to buy a service from, I think it was Sun at the time, it was a server. They, we were just happy to order it online, but you had to contact sales. We contacted sales. And then 10 people showed up. I just wanted to buy and knew exactly what I wanted to buy. And 10 people showed up.

• 56:12Speaker 01

  And you had all these different functions and they produce a memo about where we're going as a company and all those things. None of that was required. All of that was, I'm sure, incredibly expensive for them. And it's just a byproduct of an organization that has grown without understanding what actually was needed or what made sense for the customer perspective. So we tried to be very, very disciplined with all that.

• 56:33Logan Bartlett?

  So pricing, in the early days of Datadog, you wanted to make it really easy for customers to get going. But there's this balance of what type of business you want to be, the premium product or maybe the cheaper solution out there. How did you think about where to fit on the pricing curve?

• 56:54Speaker 01

  Yeah, so there's two things. I mean, one is what kind of cycle do you want to have with your customers? Do you want a cycle where you charge for your product? Every year, your customers tell you, wow, that's a lot of money.

• 57:14Speaker 01

  And I need you to deliver value for that. So here are these other things I want you to do for me. And by the way, the product is not valuable enough, so you need to fix it. That's a good interaction because it means it pushes you up. It pushes you to do more. It pushes you to solve more problems. It pushes you to be better. That's the interaction we chose. The alternative is to be a price disruptor, which is you just cut, select the customers on that. And then the conversion with the customer is, well, it's cheap. Or, you know, if you remove this and this and this could be even cheaper. And that one pushes you down. So that's, I think, in the short term, might work. Maybe some business models, things that are more consumer-y, I think it works.

• 57:56Speaker 01

  You know, when you can get mass adoption that way, for example. I think for what we do, it doesn't work in the long term. Because there's a fairly cold math in terms of what it takes to build a company that could be successful in the long run.

• 58:16Speaker 01

  There is a

• 58:19Speaker 01

  Like you need to be able to, first of all, you need to be able to sell your product. You know, so say if you do something that's 10 times cheaper. Uh, can you still actually pay a sales team uh to sell it? Or is it so cheap because the cost of sale is fairly fixed? Like, you know, you still talk to people, you spend time, et cetera, et cetera. Uh, you market them, you have to find them. Uh, so how much of your revenue is going to go to sales as a result of that? And then as a consequence of it, how much do you still have to invest in product? And then where can you be, you know, two, three, five, 10 years down the road based on that investment you're going to make? So we chose the uh the cycle of, hey, we're going to be pressed for value by customers. And we're going to be on the hunt for delivering more value and doing more for them over time.

• 59:03Speaker 01

  As opposed to, you know, hiding at the bottom of a bill somewhere, hoping to be forgotten and having uh and and shaving from it, you know, perpetually.

• 59:11Logan Bartlett?

  Yeah, competition, I mean, this is an old adage. I don't know if you agree with it, but competition will typically come from some orthogonal area. And it's, it usually starts as a low cost option or a lower cost option. And then it builds more full featured set over time. How do you, one, do you agree with that? But then two, um, how do you make sure that you don't get disrupted from a adjacency with some lower price option?

• 59:37Speaker 01

  I mean, look, so we, so first of all, we're always careful about what customers actually use, right? So the way to, the way to look at competition is not to read computer websites. Uh, because that will drive you crazy, I guarantee you that. Especially in a field like ours, you know, where there's a steady stream of new companies. And when we, when we started that up, there was also too many incumbents already. But the way to understand competition is through the eyes of the customers. So what do they say? What do they see? What do they like? What do they not like? What do they think is viable? What do they think is not viable? So that's completely the prism to use uh to look at competition. Then when you look at disruption, so you have to look at what's good disruption, what's bad disruption.

• 1:00:19Speaker 01

  So bad disruption is, are they disruptive from a price perspective? Because they have a poor business model or, you know, they, you know, they raise a half a billion in zero interest and they're using that to subsidize their customers. Are they building a business where they can't actually, they can't actually invest and they're not going to be around in a few years? That's bad disruption. Typically, this one, a little bit painful because, you know, these people are there and offering deals. But you know, and we've seen that many times over, like, you know, these are not going to be around for the long run. And those deals have a short lifespan.

• 1:00:59Speaker 01

  The good disruption, like new technology, new modalities, new topologies for data, things like that. That we actually try to bring upon ourselves. We, whatever we see something in the market or we can think of something, we try to, we also have an example of that is we release a new product at a conference last year, it's called Flex Logs. That makes log data basically in order to manage it cheaper. And we aim to do more and more of that over time, you know, for our customer base.

• 1:01:31Logan Bartlett?

  I've, I've, I've heard Datadog's culture described as low ego slash drama. How do you, how do you actionalize that or make sure that that remains the case?

• 1:01:45Speaker 01

  Yeah, I think it's a, so that's the stance of the company. That's the stance for the management team, my co-founder, myself.

• 1:01:55Speaker 01

  I would say the, the biggest thing we look for in people and the biggest threat of the company that leads into that is that we need to be, need to have a growth mindset and we need to be humble, whether that's in front of our customers or internally. You know, it means when we go meet with a customer, we, we're here to, to learn about their problem. We're not, we're not here to teach them what to do, things that we want to be taught.

• 1:02:21Speaker 01

  It doesn't come naturally mostly, like you've been building something for a long time. You have a lot of opinions about what works and what doesn't, like you actually need to be, but when you meet with a customer, you need to be here to listen. So that's humility. The same thing works internally when. Uh say, you know, you're a star engineer. Um you've been hired because you're super smart and you're super good at your job, that's a given. Uh you build something. Uh but somebody else on your team. Uh who's been here maybe a little longer or, you know, he's also very good or. Or he's, you know, just basically someone who's maybe your manager or, you know, the actual whoever. He's telling you, no, no, actually, you know what, it's not. It's great. But we shouldn't be doing it that way. Let's try it again this other way.

• 1:03:04Speaker 01

  Um we want people who, when they are being told that, well, maybe they will. Um uh explain, discuss, etc. And then they say, you know what? I'll do it. I'll try it again. As opposed to. Oh, you know, this is bullshit. I know what I'm doing, you know, etc., etc., etc. Uh and I've seen many, many smart people, very smart people. Uh falling the trap of being too aware of being very smart. And reaching a plateau because of that. Um because, you know, they're not open enough to the uh to, you know, trying again, learning. Um not having necessarily that growth mindset. So when you force um to be humble, you know, with a customer and we're forced to be humble. Uh internally, I think it it lends itself to a culture where, you know, you don't take yourself too seriously.

• 1:03:49Speaker 01

  Um and, you know, relating to the low drama and and low ego side of things. And I think it's a great, you know. For the for future performance organization, I think it's great.

• 1:03:58Logan Bartlett?

  Are those characteristics that you think you can assess i- in an interview? And it's kind of innate to people? Or is it something that people can learn when they when they come in the doors?

• 1:04:09Speaker 01

  It's, I think it's hard to assess in interviews. You can, you get some signals from it. Uh I think in interviews, you understand. Uh, you know, who smart people are, um how well they understand their job, that that's good. When you look at their experience, you get some sense of how they've progressed and how they've grown. Uh but the two things you uh you're going to uh to learn to see only when they're on the job is, are they actually productive? You know, some people are very smart, very good, but not very productive. Uh and, you know, do they actually have the growth mindset? I would say early in career, uh growth mindset can be trained pretty well. So we do a lot of campus recruiting.

• 1:04:49Speaker 01

  Um and if you have the right organization, if you have the right managers, the right mechanisms and culture inside the company. I think you can really train that growth mindset. And and as a result, you know, we've been very successful with seeing people. Who joined the company as interns and who now run huge parts of the organization just because they they've always on the hunt for the the next stage and they want to grow and you know they happily are going to take the next problem, make it disappear and then grow one level.

• 1:05:19Logan Bartlett?

  Are there quantitative ways that you think about measuring the culture? Or how do you go about making sure that there's not drift from these principles that you you espouse?

• 1:05:30Speaker 01

  It's hard. I don't think we have any hard data on that. And you know, one thing that's specific to us is that we've never written it down. So we've never, you know, we don't have like, you know, you know, to have principles or, you know, seven values or any of that stuff. We've considered doing it a number of times. Maybe we'll do it in the future, you know, as we need to, you know, hire and onboard, you know, thousands of people every year. But so far we haven't done it. What we do instead is we do the traditional employee surveys. But one thing that that we we still do to this day is I will personally read every single comment in the employee survey that we do them twice a year.

• 1:06:13Speaker 01

  And they're a great way of just getting a sense again of the the fabric of reality, like what are people thinking? What did they complain about? Who did they speak about the organization? Who do they? What's happening in there? And is there, do we see some change over time or are there some things? You know, when you slice it by team or geography, things like that, are there some things that we should be paying attention? Because it looks like in this part of the world, maybe people behave a little bit differently or think a little bit differently.

• 1:06:40Logan Bartlett?

  Alfred, you describe culture, and this is a a good reminder as as who you hire, who you fire and who you promote. Can you speak to that a little bit of how you how you think about it?

• 1:06:50Speaker 01

  Well, I mean, who we promote is people who are, you know, have a real growth mindset, humble in front of the problem, low ego, low drama, but also who are doers and fixers, you know? So you want people who are productive, are going to get things done and are good at, you know, fixing issues. Like the best people in organizations, you send them problems and order comes out. And in general, you you can get a sense of who these are because. You know, it's like fluid dynamics, you know, so uh work finds amazing people and avoids the others. So when you see people lining up to get to work with with some people on the team, like you know, like they just met problems disappear.

• 1:07:35Logan Bartlett?

  I know seeking bad news is an important thing. We touched on it earlier, but how do you make sure that you're a that you maintain a culture that seeks bad news?

• 1:07:43Speaker 01

  Um, well, I mean, again, you focus on the details. So anything that's aggregated is suspect, right? Because when you aggregate, you lose the color, or maybe you tone down some things, maybe somebody doesn't want to look bad. And you know, it's completely human, but they don't want to look bad. And they don't include something that reflects poorly on them or the teams, things like that. So that's why it's important to go back to the source, go back to the data and see what's actually happening in there. So that's number one. We discussed that already. The second one is having feedback looks that are actually painful to the organization. So they make it hard to ignore, you know. So when you attach revenue to a revenue product, which is something we do, you get

• 1:08:25Speaker 01

  that very hard feedback when the product was not ready, when it's not good enough, when there doesn't produce enough value for the customer, you get this, the revenues going down, churning, things like that. It's hard. It's impossible to ignore that. And as a result, you know, you you sort of have to face reality and and go for the bad news.

• 1:08:47Logan Bartlett?

  Are there other things that force that force the the bad news process that you guys have have found? I mean, revenue is a great one. Are there other things you guys have done?

• 1:08:59Speaker 01

  I mean, look, we every single step on the way, we will try was to figure out what's what's broken. I don't have to look for it under. So I'm the CEO. So usually when something arrives in front of me, it's bad, it's bad news. But we try to make sure that every single layer of the organization, people understand, you know, when

• 1:09:19Speaker 01

  Naturally, people will always try to make things look better. So say when you're a product manager, when you, when you go and get feedback from a customer, if they tell you two compliments and one bad thing, just ignore the two compliments. The one thing you want to tell you is a bad thing. And so that's the only thing you should pay attention to. So again, we try to apply that to pretty much every single part of the. There, there are some exceptions, of course. I mean, you, you don't want to be, to be just negative about everything, like, you know, and depending on the function and the culture, like, people can complain more or less. But, you know, in general, whenever it comes to the business and customers, it's very, very important to focus on the, on the bad news. The good news, you're going to, you don't worry, you'll get them.

• 1:10:01Speaker 01

  You'll see customers ramp up, you'll see, you'll see them use more, you'll see that. That's going to be fine. The, the bad news, you really need some, need some, some special attention.

• 1:10:08Logan Bartlett?

  What have you learned about hiring executives that you would tell your younger self or someone that's early in their company journey?

• 1:10:16Speaker 01

  Well, so first of all, it's important, as I, I think we touched on that already, but hiring two partners is important. I, I think the, it gets used so much further when you can lean on an executive team and have people just run the show. And, you know, I keep joking that my, my, my goal is to grow the team so that, you know, I can be at the bar downstairs all day and, you know, nobody will notice. But look, at the end of the day, these are the people who need to run the company. So it's, it's important to over invest in having the, the right team around the table.

• 1:10:48Logan Bartlett?

  What, um, I, I, I've heard that, um, I mean, I think this is intuitive, but the most talented people typically are looking for jobs. How, how have you gone about attracting those people? Now I'm sure it's easier than it was once upon a time, but, uh, how, how do you think about, uh, getting people to want to join in the early days?

• 1:11:06Speaker 01

  In the early days, I think, I think it depends on the, on the functions. Uh, I think when you're small, like, you know, you can attract engineers very fairly easily, I would say, because. Engineers, I mean, and I'm an engineer, so I can completely relate. Love the ability to shape everything from scratch, leave their mark, you know, have a have a lot of say, you know, in the architecture. I would say on the go to market side in sales, it's harder. Um, because the folks who are very good at it.

• 1:11:37Speaker 01

  We likely go where, you know, the chance of them making a lot of money is higher. And that's typically not was not a stage startup with a lot of different, you know, hypothetical and things like that. So I think as you grow, you know, you should expect to keep hiring and keep, keep building as you keep attracting better and better people for the, for your sales organizations. The two are a little bit different in that way. Look, at the end of the day, as we, as you scale the organization. The, um, the, the thing that matters is to give people impact. And that both on the sales side and on the engineering side is to give people impact. Make sure that they can build things, they see the results, they understand what impacts the business and they understand what impacts their own career.

• 1:12:20Speaker 01

  Um, I think that's completely similar, even though the, the motivation structures between engineers and sales might differ a little bit otherwise.

• 1:12:28Logan Bartlett?

  I, I heard you say that you sucked at fundraising, uh, in the, in the early days. Do you, uh, do you think you actually, uh, got demonstrably better at it over time? Did the business just get, uh, so much more obvious and the numbers just so much more inevitable that, uh, that people gravitated to it?

• 1:12:46Speaker 01

  Well, we'll never know whether I, I got better at it or, you know, the business itself.

• 1:12:50Logan Bartlett?

  Certainly got easier.

• 1:12:51Speaker 01

  Yes, it, it definitely got easier, but, but also look, like everything else. It's a question of understanding who you're talking to and, um, who the other side of that relationship works. Like what time investors, um, looking for? What, uh, what world do they live in? What, what's, what they're looking at, like who do they understand the various categories? Like some of the things, um, some of the words that were used, I was using initially, that actually didn't work at all. Like I was, I was very naive about what, what the category we're setting into. I was very naive about the, uh, the state of the art, you know, what the other, uh, communities out there were doing.

• 1:13:29Speaker 01

  I think you have to understand all that and understand through which prism the investors are looking at you to to to make that case. And that's something I've had to learn with VC investors. And now something, as a company also, we've had to learn with public investors, that we're a public company. We need to understand what they're looking at, how they understand the company, why they would invest in Datadog versus, you know, the other many, many stocks they can invest in. And what their frame of references to understand where we're the same and where we're different.

• 1:13:59Logan Bartlett?

  Is there, it sounds like each of those, or those two things are fairly distinct and each individual investor might be distinct in their approach and how they're they're viewing things from an industry standpoint or whatever it is. Were there, was there one or two insights other than just putting yourself in that investor shoes or trying to understand the the language that they were using that you would tell yourself 10 years ago?

• 1:14:24Speaker 01

  Well, I think that's, that one's definitely important. The other one is to build relationships. And that's true, that was true with the VC investors before, as true with public investors now. Investors build trust over time. So the biggest advice I give, for example, when I invest in a, in in new companies and people have, are going through a hot heat or a hot series A or something like that, and they say, hey, I've got what I need. I don't need to talk to anybody else anymore because I've got two term sheets already and I'm, I'm, I won't, I won't, I won't spend more time. What I usually tell them is, no, actually, talk to a few more people now. You probably will not take money from anybody else right now. That's fine.

• 1:15:06Speaker 01

  But the people you talk to right now are going to start doing the work. They'll ramp up on your company, they'll get to know you, and then they won't stop doing that work. And then by the time you need to raise a letter the next round, they'll have built conviction about your company and they'll be the ones who actually will make your next one happen. So it is worth it to build these relationships over time. Now that we're a public company, we do the same. Like, you know, the biggest misconception about an IPO is that it's an exit. It's actually not. Like, it's a starting line, not a finish line. After that, you're on the treadmill, actually, and you need to deliver every quarter. When you go on a roadshow and you talk to all those public investors, what you're not doing is getting them to invest in the IPO. What you're doing is starting this relationship.

• 1:15:52Speaker 01

  So that maybe they will invest a bit now, maybe not. Usually they will, but they might sell right away. But really, what you want is you want them to do all the work and track your company and build this confidence in what the management team is doing, what the company is doing, so that over time, they will build and build and build a position and they'll be long-term investors in your company.

• 1:16:10Logan Bartlett?

  I realize it's a deeply personal decision, but along the way, every successful company seemingly has people beating down their door trying to acquire the business. Not speaking about any specific things, but along the way, like, as those situations potentially presented themselves, how did you think about the different constituencies, the decision to keep going versus not? Just like at a high level, what would you recommend for founders?

• 1:16:40Speaker 01

  Well, so, so, first of all, it's, again, it's a personal decision usually. I think there's usually reasons to sell and not sell. And everybody is in a different personal situation. For us, the first time we got those

• 1:16:56Speaker 01

  opportunities, we, first of all, it was an occasion for us to understand that we've had come along pretty far, you know, from the company, nobody thought it was going to be successful, nobody wanted to invest in. So that was, it was good, in a way, to take stock of that. So that was positive. Second, it was very important for me and my co-founder that we actually agreed on what needed to happen next. So we spent quite a bit of time talking to each other about, hey, what do you think of this? Just to make sure that we don't end up in a situation where, you know, one is really deciding for the other and the other one resents, you know, and then you end up with a, you know, poor situation a few years down the road. So we were very careful about doing that. And the last thing is, we tried to have a

• 1:17:42Speaker 01

  A framework for deciding, you know, does it make sense to sell or not? And it came down to two things for us. One is when you don't sell, it means you think you have another 10x. Like, can I, can I grow that another 10x? Maybe, probably, okay, well, let's try that. The same thing is understanding that when you pass on one of those opportunities, it's at least a five-year commitment. And the question there is, okay, so I believe it's a great business. I believe I can scale it. And in addition to that, do I actually want to do that day to day? Like, do I want to do that for at least another five years? I think those are the two ways. I mean, again, maybe these numbers are completely wrong. I don't know.

• 1:18:25Speaker 01

  But these are the two ways that we, we try to, to frame things and, and help, you know, decide, because otherwise it's just nerve-wracking to try and decide.

• 1:18:35Logan Bartlett?

  Artificial intelligence, we touched on it a little bit, machine learning. If, there's a lot being said today about how it's going to change software development. If you were an engineer, 22, 23, 24, looking at this and you, you hear different considerations about if it's come for certain jobs or not, like, how would you work with the tooling around AI and try to future-proof where you're headed in your career?

• 1:19:05Speaker 01

  Yeah, I mean, the first thing I would say is right now, if you're going to graduate in the next year or two, you really shouldn't skip on, you know, matrix multiplications, because apparently, if you can multiply matrices, you can raise, raise a hundred million dollars, you know, so that's, that's good.

• 1:19:22Speaker 01

  But more seriously, I think, look, it's super important for everyone to lean to the tooling there. I think

• 1:19:29Speaker 01

  software development in general lends very, very well to being amplified, augmented, accelerated by AI. We're seeing that today, and I think we'll see a lot more of it in the future. So it's important to lean into the tooling and, and, and learn, you know, all of that. The, the way I think about it, by the way, is that it's yet another step in the A curve that we've been riding of a continuous improvement of developer productivity. Like if you go back forty or fifty years, you had people programming on the

• 1:20:06Speaker 01

  in machine language and punch cards. You know, and then you had machine language and keyboards and screen. Then you had a more advanced language on keyboards and screen. Then you had, but you still had, when you wanted to code, you still had to go buy a book at the library. And, and then, you know, you, you know what there is in the book and that's it. Then you had the internet, so you could learn about anything anytime. Then you had all of the libraries in the open source. Then you had SaaS and cloud with the APIs you can use to simple things. So now, when you look at the, the AI augmented development, I think that's another step on that curve. And I think every single step of the way, like these major steps, we improve productivity anywhere from no fifty percent to ten X.

• 1:20:50Speaker 01

  And I think we'll see something similar with the, with AI.

• 1:20:53Logan Bartlett?

  Are there, are there particular things that you're paying attention to outside of the core benefits to, to Datadog within artificial intelligence?

• 1:21:03Speaker 01

  Well, I think to us, the, the opportunity we have at Datadog, by the way, is that it's a, on one end, it drives a lot of cloud migration and consumption and.

• 1:21:13Logan Bartlett?

  AI does.

• 1:21:14Speaker 01

  Yes, and you know, move to storing your data and using more of your data. Like all of that is a, like a, I would say plays into some of the existing ways that we've seen. But also, you know, if you follow the, the, the curve of productivity I just mentioned, a lot of the value actually is moving from writing the code to understanding it, to adapting it to the real world, to securing it, to understanding what happens when other people make changes to it. So actually it pulls more into our, our part of the world. So it creates a, I would say, a very big opportunity for us to go after. It's also hap- so happens that the AI-written code, or AI in general, makes the code less predictable.

• 1:21:59Speaker 01

  It used to be everything was very mathematically predictable. Now, now you end up with probabilistic executions and things like that. But I think there's going to be a lot of, of new areas to cover there.

• 1:22:10Logan Bartlett?

  And so on that, on that point on where, where you all fit in to it, just to make sure I understand. So the, the, the dev side of the house is increasingly going to spend just less time on the low levels of code writing. And it's going to become more dev and ops together in that way and actually understanding and executing. And so then a system that can understand what's being written and the different bugs and all that is going to be even more important.

• 1:22:39Speaker 01

  Yes, exactly, exactly. Like it takes, right now, it's, it still takes a lot of time to draft the code. Like you, you have to figure out what to type, you, you know, you go on Stack Overflow, you, you know, you spend more time, then you fight with the compiler, like all that stuff. Uh, I think a lot of that is going away. I think now to get from, okay, maybe I should do this to have a first version of it that I can prop in my, in my code. I think can go pretty fast. Uh, but then the question is, does it actually do what I want? Uh, oh wait, somebody changed some other module. What does that, what does, how does that impact what I do? Um, I see those errors in production. What do they mean? Um, or, you know, it, it looks like then there might be a secondary vulnerability here. How do I address that?

• 1:23:20Speaker 01

  So all of those things still have a lot more to do with the behavior of the code and the world around it than the code itself. And I think that's where a lot of the value is going to be moving forward.

• 1:23:32Logan Bartlett?

  So I guess to wrap, um, New York, so you've been here now 25 years. Uh, you resisted moving out to the Bay Area. Was that actually asked of you in the early days of Data Dog? Did investors?

• 1:23:45Speaker 01

  Yeah, some, some investors, um, uh, hinted that it would be easier for us to raise money if we went to the Bay Area. And also that there was more talent there for what we're doing, which is true. There was more talent there for what you mean.

• 1:23:56Logan Bartlett?

  How is Data Dog a different business in, in your mind than it maybe would have been? And I realize it's hard to totally know, but.

• 1:24:03Speaker 01

  Yeah. And again, the counterfactuals are always difficult. But I would say, look, just looking at the, the companies we competed with, because when we started, there were like, every single major VC fund had a champion, like they had a, a company. Led by super smart people that were way more fit to market than we were like they had worked at the hyperscalers before or work in the in the systems management field, which my co-founder and I hadn't. So these people had all the ingredients for being successful. VCs had already backed people in this monitoring, observability. Yes, or they did shortly after. Yeah. But, you know, there was always that. And we were a lot more successful than they were.

• 1:24:46Speaker 01

  And I think part of it is um we were closer, further from the tech world, but closer to the real world. Um, so less into the uh the echo chamber of what kind of makes sense to the tech community right now. Um, and more into the, but what are the real problems that the real companies have about this and, you know, what who can we solve that? That's one thing. The second thing is, look, we were not nearly as hot as the other companies. Um, so we could never um uh think that we had it all figured out. And we just needed to ship the product and the and the customers would come.

• 1:25:25Speaker 01

  We were always super scared that we were not going to get it right, that we're going to be out executed, out founded by, you know, just about anybody out there. And so we focused super hard on those early customers and, you know, all of the values we discussed earlier, you know, whether that's the efficiency of the business, but also the humility in front of the customer, all that, you know, really comes from that. And I think that's a that's a risk I see today, by the way. When I see um young founders uh getting super, super successful very, very early in terms of the the fundraising traction. I worry a lot that they're not going to be set up for success.

• 1:26:03Speaker 01

  I see that too with AI, for example, right now, where a lot of uh brand new companies raise amazing amounts of money without much to show initially. And the risk there is really to fail to understand that, yeah, but at the end of the day, you're just here to solve somebody else's problem. And your focus should be completely on that. Anything else, whatever anybody else is telling you about how great your business is, doesn't mean anything because you haven't solved that problem for the customer yet.

• 1:26:31Logan Bartlett?

  It seems like every successful company has at least one difficult fundraise. And it's, I think, one of my views is that if it comes earlier, it's better than coming later because it puts all those principles. You don't need to culturally reset all of that stuff along the way. So I guess to some extent, the early days being more difficult, maybe was a benefit to the ultimate success.

• 1:26:54Speaker 01

  Yeah, and look, it's like being, being efficient and disciplined is a good thing. Like in the long run, it works really, really well, you know. So if I look back at the markets like a few years ago, you know, valuations were crazy. They were in no interest rates and they, and everybody was spending, even on public markets, everybody was spending a ton to grow.

• 1:27:21Speaker 01

  After that, there's been quite a bit of contraction of the multiples. The companies had to be profitable all of a sudden in the public markets and in the private markets. And I think it really was very, very painful for many companies in our space. As, you know, they had to, you know, turn on a dime and, you know, all of a sudden they were not able to invest anymore. In our case, it didn't change all that much. So we changed the diet a little bit, you know, so we, we grew profitability a little bit further and faster than we would have otherwise. We slowed down a little bit of hiring just because we were not completely sure what the, the future would look like. But, you know, we were still able to keep investing, you know, thirty percent of our revenue into R and D. And that hasn't changed the posture of the company all that much.

• 1:28:04Speaker 01

  I would say there's a lot of long-term benefits to building a company that is, you know, disciplined and profitable.

• 1:28:12Logan Bartlett?

  Your sales pitch to future founders that are thinking about.

• 1:28:17Logan Bartlett?

  Picking a location, a headquarters, uh, do you think all the things you said earlier about ability to focus on the customers and being removed from Silicon Valley, does that still hold true in the world that there's more venture firms here? And would you recommend New York for the next generation?

• 1:28:32Speaker 01

  I think so too. I think, look, there's definitely some trade offs. I think if you, uh, for certain types of talent, there's still a lot more talent in the Bay Area than in New York. Like you'll have more choice. That's a given. On the flip side, that talent typically comes with more employee churn. So people change jobs more. It's, there's more places to go to for one thing, but also it's more in the culture. Like it's a bit weird to stay a very long time in a place, you know, in the Bay Area. Whereas in New York, it's more common, uh, like if people are happy with their jobs and treated well and working on interesting things, they'll tend to stay longer. And then if you go to Europe, which is the other side, that's even, even more so.

• 1:29:14Speaker 01

  Like people will stay even longer. Um, so for us, you know, we struck the right balance of, uh, um, uh, employee tenure and, uh, choice of talent by being in New York. I think it's a great equation there too. And by the way, I'm a firm believer in long tenures. Um, I think you learn from your mistakes, um, and you need these feedback loops. I think we, it's been a theme.

• 1:29:39Logan Bartlett?

  Yeah, yeah.

• 1:29:40Speaker 01

  And the feedback loops take years in what you built. Um, so if you earn a job for a year and a half, uh, you ship something six months in. Uh, you, when you leave, you still think it was a genius idea. Uh, you haven't seen the many ways in which it was not great or not perfect. Um, and you haven't learned. Um, so I compare that to having a, you know, a, uh, a 10 miles experience of running a marathon, uh, you just have no idea. Um, and I think when I, when, when we hire, by the way, one thing we look at is, did people build longer, successful tenure? Like, did people spend three, four, five, six years at a place? Uh, because typically when you do that and you grow at the same time, like that's when you actually have true experience.

• 1:30:24Speaker 01

  Um, people being promoted just by jumping around actually doesn't work that well in the long run. It might work a little bit in the short term, but in the long run, you don't actually experience the same way.

• 1:30:32Logan Bartlett?

  Hmm. Super interesting. Olivia, thanks for doing this.

• 1:30:35Speaker 01

  All right. Well, thank you very much.

• 1:30:36Logan Bartlett?

  Yeah, this is great.

• 1:30:40Logan Bartlett?

  Thank you for joining this episode of the Logan Bartlett Show with co-founder and CEO of Datadog Olivier Pomel. If you enjoyed this conversation, we'd love for you to share with anyone else that you think might find it interesting, as well as subscribe on whatever platform that you're listening to this on. We look forward to having you back next week with another great guest on the Logan Bartlett Show. Have a good weekend, everyone.