Datadog, Inc. Class A Common Stock Goldman Sachs Communacopia + Technology Conference 2026
Review the key takeaways and the transcript of this earnings call.
- Datadog has developed a proprietary dataset from its observability platform, enabling predictive AI models such as Toto to improve application functionality and automated site reliability engineering.
- The company recently acquired Adaptive ML to enhance reinforcement learning capabilities in IT management and observability.
- Datadog is monetizing AI-related observability by monitoring inference workloads, with thousands of customers using these features and generating revenue.
- AI native customers, characterized as cloud native with accelerated demand and no legacy infrastructure, show strong growth and increasing use of multiple Datadog products.
- Datadog retains the vast majority of its customers with gross retention in the upper 90s, and net retention is increasing as customers expand workloads on the platform.
- The company has introduced innovations like Infinite Cardinality and Federated Logs to optimize data usage and cost efficiency for customers, enhancing value and margins.
- Datadog is investing heavily in R&D, consistently allocating about 30% of revenues, over a billion dollars, to maintain innovation and platform integration.
- The company is expanding its enterprise sales team to capture increased cloud migration demand among large enterprises, focusing on long sales cycles and key accounts globally.
- Datadog is seeing unexpected demand from hyperscalers and AI native customers for monitoring training and post-training AI workloads, adding new revenue streams.
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Transcript
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Everyone, welcome to the Datadog session where we are debating strong suits versus weak suits in pop culture and trivia.
Trivia. Hey, it's a real pleasure to have David, CFO of Datadog, on stage with me.
David. Thanks for having me.
Really appreciate you making the time to be with us this morning. We started having a couple of really interesting conversations over dinner last night that I'd love to talk through a little bit with a broader audience. The first one is this idea of applying proprietary data to an SLM or an LLM.
Yeah. Datadog has a really unique data set if I think about the types of observability data that you've been collecting since the founding of the company.
Tell us a little bit about what Datadog can do with that time series forecast, and how you could apply it to the next product cycles in your business.
Yeah, great question. We used to call it ML or correlation. For a long time, Datadog's, one of their strengths has been to be able to aggregate enough information around the signals of the functioning of client-facing applications, and to be able to be somewhat predictive. AI and models have provided a very unique opportunity. We recently made an acquisition of a company called Adaptive ML, which is a specialist in reinforcement learning around areas in IT management and observability that we cover.
That, with the data we have on observability and the functionality of applications and the research lab that we've created, we put out a model a while ago called Toto, but that's just the beginning of what we think will be a very strong competitive advantage, which is using AI and models, some of which will be open source, some of which may be the foundation companies, to own the data and produce models which are going to be predictive of issues around the functionality of applications, observability. We're investing behind that. We talked about that last night, both in terms of people, the GPUs, the inference, et cetera, and that data is not public.
That data Datadog has because of our position and size and observability, and we think that will deliver a lot of value to clients and also be a competitive advantage in the evolution of the platform.
Let's stay on the advantages that it can give your clients. If I think about the Holy Grail in observability, there's this idea of automated site reliability engineering.
Yeah. You have a little bit of that with the Bits product.
Maybe just paint the vision for us. When you talk to the founding team, where could a technology like Toto go over time?
Yeah. The vision here is to produce more accurate and quicker real-time signals which can improve the functionality of the platform and go on the continuum to allow for auto-remediation or close to it. That will be the speed of analyzing problems all the way towards having the platform able to act independently. That will be when we get there, we're not there yet, will be a combination of the evolution of the platform. Our Bits product line is what is going to use these models and data to be predictive, and then clients, in our vision, will be able to make the choice of how much to auto-remediate. To say, for this type of issue, the platform can auto-remediate. For this type, there'll be a suggestion, and then someone will have to press yes.
That has tremendous ramifications, both in terms of the speed and also the efficiency in human capital in this endeavor, that is the vision of our founder, Oli, and where we are investing behind.
The other interesting thread to pull on here is this idea of an inference economy.
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