The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 41 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Pomel: Within five years, security will inevitably attach to observability
“We have a thesis that security, like, five years from now will be will be, or rather, let me rephrase it will be a no-brainer that you have to attach your security to your observability, because that is what gets deployed everywhere in your application, in you…”
Pomel: Datadog historically avoided 'AI' terminology, viewing it as 'bullshit words'
“For the longest time in the history of the company, we've been careful about using the words AI. We thought they were, you know, bullshit words mostly.”
Pomel: Product AI chatbots require handholding as users struggle with prompts
“It works great for some use cases, but you need to handhold the users a lot more. Like, users don't necessarily know exactly what to ask for and how to ask for it, or what to go next, you know, once they've asked a question.”
Pomel: Month-to-month contracts beat multi-year deals for validating new products
“So for new products and new companies, I would argue, month to month is great. Because your customers can try at any time which means you'll get the hard reality to hit you in the face and you can't ignore it, you know, which is a problem when you have, so you…”
Pomel: AI solves only 2% to 5% of systems management cases
“Right now we'd be lucky to solve, you know, two percent, three percent, five percent of the cases with AI.”
Pomel: Datadog's Toto model beats other models on observability and weather benchmarks
“This model from day one was state of the art, like, it's it beats all the other models, of course, on the, on observability data, we have special benchmarks from that, but also for other things like weather data which was very surprising to us.”
Katz recommends Datadog for hosted APM over stitching custom solutions
“If you are looking to deploy a hosted APM solution, and you asked me objectively, my recommendation, I'd say use Datadog. I'd say that the product, the maturity, the experience is, is considerably better than trying to stitch together something using ClickHous…”
Pomel: Leading with AI in IT monitoring will inevitably disappoint
“It's very hard for what we do to lead with, you know, AI because you, you're bound to disappoint, because the generic case is very, very hard to solve”
Pomel: Customers say they want false positives but hate noisy software
“When you ask, when we first visited customers to figure out what they wanted from our product. When you ask them to pick between a false positive and a false negative they'll say, oh, give me the false positive, I'll decide if it's right, but I want to know. I…”
Olivier Pomel: Y Combinator rejected Datadog over lack of initial product
“We applied to all sorts of incubators, we got to a Y Combinator interview, and then we didn't get into Y Combinator. I have an email from Paul Graham that says You know, a platform is only as good as its first product, and you don't have a first product, blah,…”
Pomel: AI shifts software value from writing code to understanding behavior
“Every time you add productivity, you add complexity, meaning that you have less and less of an understanding of what it is you're doing... So a lot of the value shifts from just creating that thing to understanding how it's going to behave, how it's changing o…”
Half of Datadog design partners drop off after pricing is introduced
“When the product is ready, when it's great, about half of the design partners just disappear.”
Datadog keeps struggling product teams small rather than killing products
“We did not kill products, but we, the way we do it is we start small, and we've waited a long time to scale up.”
Pomel: Resisting acquisition offers is key to building a large company
“To grow to a large independent company, you, one of the hardest thing to do is to not sell on the way.”
Pomel: West Coast VCs dismissed NYC-based infrastructure startups
“Whenever we pitched West Coast investors, like, it was sort of seen as a form of mental deficiency to be to be based in New York and, you know, doing it, doing infrastructure.”
Datadog CTO: False positives, not missed alerts, are the primary enemy in monitoring
“So that, that's why I would say for us to, it's really false positives. That's the enemy. Somehow you'll find out when something goes wrong.”
Pomel: Datadog avoids dedicated AI labs because they set wrong expectations
“We, so we don't have a lab. In general, we're a little bit careful with labs. I think it's sets the wrong expectation.”
Pomel: IT teams won't manage AI separately from existing cloud infrastructure
“You are not going to manage your AI separately from your databases separately, from your network separately, from your security separately. Everything makes more sense when it is managed together and when we can assemble the full picture for you.”
Pomel: AI model observability will take years to fully flesh out
“So I think it's going to take maybe a few years for that category of observing the models themselves to fully flesh out in terms of what the use cases are, the needs and where they go.”
Datadog rejects Apple-style secret product development
“We don't do it like Apple, like we don't disappear in a basement for three years and then, you know, ship a fully formed product that takes the world by storm. Instead, from the earliest days, like we work with design partners, we work with customers, and we t…”
Pomel: AI might resolve 20% of IT issues in a year
“Maybe in a year it's going to be 20%, maybe, but it's going to be gradual.”
Pomel: Datadog raised launch price to $15 then $18 without metric impact
“When we launched a product we priced the product initially, we were going to price it at 12 dollars per, you know, instance, per month and then the night before we said, let's put 15. And. It was, I cannot disclose that. It, We put 15, it worked fine and, you …”
Pomel: Datadog was founded to unify metrics, traces, and logs
“And one of the main reasons why we started a company was to bridge the gap between the different teams and break down the silos. It didn't make sense for us to have those things being separate and, you know, it turns out the problems actually don't stop at the…”
Pomel: Datadog defines success by broad team adoption, not buyer purchase
“The way we define success for those products is we want to get deployment and adoption that is as broad as possible at our customers. You know, so again, going back to what we're trying to solve here, we want to bridge the gap between the teams and bring every…”
Pomel: Real competition comes from novel approaches, not late copycats
“If their plan is to do whatever you're doing, or a fraction of whatever you're doing, ah, four years later, ah, that's not where your competition is going to come from. Your competition is going to come from people who are trying to do it differently, and Are …”
Pomel: Datadog starts with off-the-shelf software before building proprietary systems
“Well, we usually, when we build a new system or add a new data type, we usually start with something off the shelf and something open source. And at a small scale it's going to work. And then as we grow, as we get more and more data into it, as we see the many…”
Pomel: Datadog processes about 10 trillion records per day
“So we're talking about, like, 10 trillion records a day, something like that?”
Pomel: Commoditized monitoring tools succeed when bundled into broader platforms
“It's a category that is not necessarily super interesting on its own, you know, because it tends to be a bit commoditized and, you know, it's a little bit high churn but it actually makes a lot of sense as part of a broader platform, which is what we offer. So…”
Le-Quoc: A team's software understanding is only as good as its monitoring
“For all the customers the understanding that they have of the application is only as good as their monitoring.”
Le-Quoc: Monitor statistical outliers rather than individual metric time series
“I don't want to monitor any particular of the time series, because there are too many of them, just the volume of data is too large. Rather, I want to find the ones that, that sort of deviates.”
Alexis Le-Quoc: Prioritize work metrics over resource metrics in system monitoring
“Not all metrics are created equal, and the ones you want to really care about it are the work metrics. So in that large volume of metrics, if you already classify in, in two classes of metrics, work and resource, you can reduce your search space by a lot.”
Le-Quoc: Vast majority of industry still struggles with data visualization
“Not the sort of advanced customer of ours, you know, say, you know, Airbnb or Twitter or, Spotify, but the vast majority of the industry is, is still kind of struggling with that.”
Pomel: Datadog splits engineering equally between core platform and specific products
“So yes, the breakdown is roughly half of our engineering team is on the platform, and half is on the is assigned to specific products.”
Olivier Pomel: Datadog's largest enterprise customers pay tens of millions annually
“Our smallest customers, you know don't pay us anything, and they're individuals or students. And our largest customers are the largest companies in the world, you know, paying us tens of millions of dollars a year, and we'll have pretty much everything in betw…”
Pomel: Datadog never pivoted and built its original vision
“No, so we didn't pivot. So we were, it's a great question because when we didn't manage to fundraise initially we sort of gobbled the pitch quite a bit. We tried to turn it in different ways, and we thought maybe we'd, we'd do something a bit different, and we…”
Datadog hired its first salesperson at under 30 employees
“We were, I think, a bit less than 30 when we hired the first salesperson.”
Pomel: Datadog was created to unify development and operations around shared data
“The starting point for Datadog was, no, there must be a better way. Maybe those people can talk to each other. And we wanted to build a system basically that brought the two sides of the house together. All of the data was relevant to both. Make sure they spea…”
Pomel: Datadog retains the last 24 hours of incoming data in memory
“The way we run it under the hood is that we're going to keep all of the data that's from the, let's call it the last 24 hours in memory so it can be accessed very, very quickly, and over time we age this data to various forms of, you know, colder and cheaper s…”
Pomel personally monitors customer tickets, closed deals, and employee feedback
“I see a large volume of the tickets our customers are filing, so I want to stay in touch with what's happening with the product. And I'm going to see all the deals we close, and I'm also going to read all the comments we get from employees in employee service.”