Datadog's amazing Q3 results just came out

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Here is how this quarter went versus my prior expectations:

  • Reporting Fiscal Q3 2023 on 11/7/23 before the market opens.
  • Revenue expectation: $533M (4.7% QoQ, 22% YoY), assuming a 2% beat.

—> They managed an incredible $547.5M (7.5% QoQ, 25.4% YoY), beating their guide by 4.7%! Wow, especially if you consider the seasonal headwind they had this Q!

  • Q4 new revenue guide: $562M (5.5% QoQ, 20% YoY) which I would interpret as $573M (7.5% QoQ, 22% YoY) assuming a QoQ re-acceleration to reach more than $60M net new revenue in 2H23.

—> They guided for $566M, which, if they can beat this again by a similar percentage as this Q3’s revenue would be totally amazing. They even increased their FY guide by 2.4% at the midpoint!

  • My Q2 revenue expectation implies about $24M raw sequential revenue increase.

—> They managed $38M net new revenue. With that they should easily manage the $60M net new revenue in the second half of the FY, which I was dreaming about.

  • I would like to see QoQ customer growth stabilize in both cohorts while continued multi-product adoption progress.

—> I was just hoping for stabilization in QoQ customer growth rates, because seasonally they tend not to accelerate from Q2 to Q3, but they managed just that! Accelerating customer growth to 2.7% QoQ, up from 2.4% and accelerating large customers (which make up 86% of revenue) to 4.7% QoQ, up from 2.7% QoQ. Another wow!

  • I would like to see NRR >120%.

—> NRR dropped to below 120%. While this is a miss in my book, I am least concerned about this metric since it is, as I mentioned many times, a lagging metric.

  • I would like to see stable profitability margins.

—> given their recent profitability margin progress, I would have been just happy with steady margins, but look, they even improved here significantly: operating margin grew to 24%, up from 21% last Q and net margin improved to 29%, up from 25% last Q. FCF margin dropped to 25%, from 28% last Q, but that 28% last Q was what I would call a positive outlier; it is up from 15% last Q3! So again amazing progress here.

  • I would like to see high single-digit RPO QoQ percentage growth.

—> RPO grew an incredible 16% QoQ, up from 9.6% in Q2 and 7.5% in Q1, again significantly exceeding my expectations. Billings performed similar: up 16.7% QoQ, up from 1.8% in Q2 and -4.7% in Q1.

—> Going into this earnings season I felt my large Datadog position was my riskiest bet of all. I’d say it payed off. I am totally blown away by these results and from the bits and pieces I listened to from the earrings call so far, it sounds like their consumption is going to continue to improve from here. What a feat …

-Ben

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Some other metrics:
• Number of $100k+ customers grew 20% YoY, they generate 86% of DDOG’s ARR
• Full year guidance on revenue increased from $2.055B to $2.15B
• Full year Adjusted EPS increased from $1.32 to $1.53 per share
• 82% of customers use at least 2 products
• 21% of customers use at least 6 products.

DDOG press release:

DDOG’s results and commentary helping some other cloud stocks, like SNOW & MDB, with statements like: “We are seeing signs that the cloud optimization activity from some of our customers may be moderating.”

And some future AI business related thoughts:

Finally, we continue to be excited about the opportunity in generative AI and large language models. First, we believe adopting next-gen AI will require the use of cloud and other modern technologies and drive additional growth in cloud workloads. So we are continuing to invest by integrating with more components at every layer of the new AI stack and by developing our own LLM observability products. And while we see signs of AI adoption across large parts of our customer base, in the near term, we continue to see AI-related usage manifest itself most accurately with next-gen AI-native customers, who contributed about 2.5% of our ARR this quarter.
In the mid to long term, we expect customers of all industries and sizes to keep adding value to their products using AI and to get their early exploration – to get from early exploration to development and into production, thus driving larger cloud and observability usage across our customer base.
Besides observing the AI stack, we also expect to keep adding value to our own platform using AI. Datadog’s unified platform and purely SaaS model, combined with strong multi-product adoption by our customers, generates a large amount of deep and precise observability data. We believe combining AI capabilities with this broad data set will allow us to deliver differentiated value to customers. And we are working to productize this differentiated value through recently announced capabilities such as our Bits AI assistant, AI generated synthetic tests, and AI-led error analysis and resolution. And we expect to deliver many more related innovation to customers over time.

And then some additional color that the AI model providers are increasing usage since they’re growing faster than the rest of DDOG’s customer base. Most customers, however, are still in the early stages of developing and shipping AI applications.

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