Datadog (initially posted 1/19/25)

My kick-off analysis, welcome!

DISCLAIMER - This is not investment advice and you should do your own due diligence. I make no representation, warranty or undertaking, express or implied, as to the accuracy, reliability, completeness, or reasonableness of the information contained in this report. Any assumptions, opinions and estimates expressed in this report constitute my judgment as of the date thereof and is subject to change without notice. Any projections contained in the report are based on a number of assumptions, and there is no guarantee that projected outcomes will be achieved. I am not acting as your financial advisor or in any fiduciary capacity.

I’m kicking off my analysis with a high growth company, Datadog! I’ve condensed my full internal analysis for something more digestible. As this is still very much in beta version, I welcome thoughts and feedback.

Exec Summary

tldr; Among SaaS names, Datadog is poised to deliver strong growth in 2025 while maintaining impressive cash flow margins and being underestimated by the Street. My price target is $150-170+. Buy the dip from the recent MS research downgrade and trust but verify to see if they nail Q4’24 and Q1’25.

Things I like

• Capturing “AI native” companies that chosen Datadog (6% of ARR in Q3); sets up an opportunity to take advantage of AI success and be a “picks and shovels” software play

• Core monitoring business scales with cloud compute growth, putting a floor on long-term growth at ~15% and a call option on macro improvements driving more digital activity

• Willingness to invest in R&D for rapid product iteration + M&A to expand portfolio to keep the growth engine going

• Gross Margins of 80%+ on a GAAP basis for a business meant to store and analyze real-time data

• Strong sales efficiency of 1.2x in MRQ from having successful PLG and SLG GTM motions

• Monitoring is very unlikely to be replaced by AI agents rolling their own software stack (even with OpenTelemetry/Prometheus)

Things I don’t like / am monitoring

• AI native is double edged - if the bubble pops, a good chunk of revenue could be vaporized instantly. Additionally, management noted likely headwinds from future price optimization in the cohort, likely to hit in Q2 and Q3 of next year

• Slowdown in international growth - any further deterioration probably hits both top-line and bottom-line, and subsequently, valuation

• Valuation likely to be very sensitive to interest rates and potential multiple compression

• Investor base skews toward smaller firms and retail at 20%, exposing the stock to heightened trading volatility, while larger institutional investors hold about 58% of the stock, but with limited concentration among the top

My Call on Q4 and FY25

My estimate for initial FY25 rev guidance on Q4 call is $3,275M (maintaining 22% y/y growth implied by consensus) but giving management cushion to hit an achievable target of $3,400M for the year (27% y/y growth) and beat by ~4% if all goes well.

If you’re wondering where I am on operating income, the answer is I don’t care - short of them shredding cash ahead of a macro rebound, small variations don’t change my thesis.

Valuation

Stock closed at $136.79, implying fully-diluted market cap of $49.3B and enterprise value of $47.0B following the latest convert note round. That translates to a 17.7x ‘24 revenue multiple and 14.5x ‘25 revenue multiple based on Street numbers. Assuming modest multiple to 16x and $3.4B in rev, you back into a $157 price, or a 15% uptick in today’s price. Roll the 17.7x multiple to the end of the year and you get to $173, or a 27% increase. Any beats above my numbers (better cross-sell of the portfolio, improved macro, even stronger AI native performance) should drive more returns.

Conclusion: Buy

As with all high growth names, there will invariably be chop in quarterly numbers that distorts the signal. However, if you believe (as I do) in the near-term AI opportunity, DDOG feels like a reasonable probability play to realize 25%+ return this year. If Q4 comes in at or above my call of $736M, I maintain a high degree of confidence, even if ‘25 guidance skews lighter than what I say. However, if Q4 falls short, then I’d want to know if either one of the core revenue streams is degrading more than anticipated, or if AI natives were front-loaded into Q3.

The Gory Details

You might be thinking, ok Phil, that all sounds great, but show me your work or GTFO. Fair enough: let’s go through the key numbers and then tie it back to the business.

Revenue Breakdown

For revenue, the splits I’m most interested in are US vs. international and AI native vs. not (I’ll call this core for lack of a better term). The company breaks out US vs. international revenue in their filings, and they’ve given approximations of ARR for AI native in four out of the last 6 quarters. I’ve taken the quarterly revenue figures and scaled to my best approximation of ARR¹, then split the AI native out of US. I assume US since frankly that’s where the vast majority of AI startups are raising money, and with the lighter regulatory environment. It also becomes fairly likely when you look at the growth rates:

From Q2 to Q4’23, growth rates slowed substantially, dropping from 31% in Q1 to 21-22% range. This started picking back up again in Q1’24, accelerating up to 27% in the most recent quarter despite what’s still a fairly poor macro environment. Meanwhile, international has slowed from mid-30% growth down to 24% in Q3’24. My takeaways from this are:

• Core NA is slowing to high teens growth, tracking in-line with AWS and more broadly hyperscalers for the near future

• International (and specifically, Europe) is seeing macro weakness as has been called out by other software companies. Barring some aberration in the near-term outlook, international is unlikely to drive higher growth

• Despite the extensive portfolio of monitoring adjacent products, win rates there against other vendors are unlikely to be meaningfully higher (which is why dollar-based net retention rates are also in the mid-110%), contributing maybe 1-2% of growth

• Therefore, all growth upside is coming from AI native

So then, thinking about Q4 revenue, I assumed core NA grows at 18% y/y (contributing $483M in rev), while international grows at 22% y/y (contributing $220M). That gets you to $703M without any of the AI natives. The AI native piece is hard to call since I don’t have high fidelity on the actual ARR, but I feel pretty good at a minimum it’s north of $130M, making $33M a reasonable number to hit. Now, could Europe be softer than forecast? Sure. But Q4 remains one of the largest seasonal software quarters, and I expect Datadog to deliver, which is why I’m calling $736M for the quarter.

For 2025, I assume 17% core NA ARR growth on the year, translating to $2,050M in revenue (growth rate tracking roughly with AWS projections, minus a few points for AI compute that we’re forecasting separately for the AI natives). International is harder to forecast, but assuming an Q4 exit revenue of 220M, I get an ARR of $908M. Assuming they can add at least the same amount of ARR in 2025 (~$164M), we’d get an ending ARR of $1,072M, and a ‘25 revenue of $960M. That gets us to $3,010M without any AI native contribution. My overall call above of $3.4B for 2025 implies $400M of revenue from AI natives in 2025. How do I rationalize that? Well, the AI native cohort is growing like crazy - even if we don’t know the actual numbers, we do know the ARR effectively doubled in a year’s time. Could it do more than that in 2025? I think so, particularly seeing some of the most successful app-layer AI companies (Anysphere and Harvey, for example) continue to raise large rounds and seeing the onslaught of “agentic AI” companies also aggressively going to market. It’s hard to know which AI apps are likely to succeed, but if Datadog is serving a majority of the hypergrowth startups, they’re in pole position to benefit from category winners, particularly as many companies announcing 5-10x growth annually.

Hedging on AI Native Growth

Interestingly, the CFO called out a few things on AI natives on the Q3 earnings call. Here’s the relevant quote in its entirety.

AI native customers contributed about 4 percentage points of year-over-year growth in Q3 versus about 2 percentage points in the year ago quarter. While we believe that adoption of AI will continue to benefit Datadog in the long term, we are mindful that some of the large customers in this cohort have ramped extremely rapidly and that these customers may optimize cloud and observability usage and increase their commitments to us over time with better terms. This may create volatility in our revenue growth in future quarters on the backdrop of long-term volume growth.

First, let’s unpack revenue contribution. About 4% of growth could be anything from 3.5% to just under 4.5%, or in dollar terms, $19-25M. So that’s what was brought in during the quarter. But then, a caveat: “ramped extremely rapidly and that these customers may optimize…usage and increase their commitments…with better terms”. You just had a nice quarter in a tough economy. Why throw water on the great news? Because…a bunch of these AI natives are paying prices that no one on the executive team expects to hold up. How do I know that? Put a pin in that thought for now.

Giving the caveat now could be for two reasons, and I’m not sure there’s a way to know which is accurate, and hence my earlier conservatism in Q4 prediction.

Possibility one: the sales team sees these brand new customers who have bought a bunch of Datadog monitoring and are calling these AI natives up to see what else in the product bag they’re willing to buy. Inevitably, the customer will say, I’m spending a ton of money on your observability products, let’s talk about discounts. And while deals most likely will generate net new ARR, it’s unlikely to be at the same awesome price points and margin as the customer who paid list price to get up and running. The new products may even be free, just to enhance stickiness of the overall Datadog solution and keep those customers. This would lead to immediate revenue volatility.

Possibility two: The exec and sales team saw a bunch of self-serve sales activity from AI natives in Q2 and Q3, with great prices (for Datadog). They don’t want to rock the boat, but know when renewal time comes in a year, a rep is almost inevitably going to have to offer some discounts and show some love. This would lead to deferred revenue volatility showing up in ~1 year from now.

We’ll know a lot more after Q4, but for now, I’m comfortable being more cautious. So, back to the assumption on AI native pricing not being sustainable. How do I know what they’re paying? Well, Datadog has two GTM motions, with product-led growth that allows developers to sign up and get going, and with a broader field sales team. People love to say sales reps are coin operated because you give them a quota and then you pay them a percentage of what they sell. There’s variance and volatility every quarter in what gets paid and what they sell, but over a long run with enterprise sales it’s reasonably predictable. That raises a question mark when I see sales commissions paid in Q3 was roughly equal to what was paid in Q2, despite a $30-40M uptick in ARR. How does that make sense?

Well, I don’t think Datadog shortchanged their reps, but rather they just had a cohort of customers who needed more usage pay for that usage without talking to anyone at the company. As mentioned above, whether it’s the customer or Datadog that reaches out next, I fully expect some level of discourse that leads to what the CFO described - a revenue and margin haircut in exchange for a larger deal.

What about Profitability?

In the quick read up top, I said profitability doesn’t matter, and that’s kind of true. Gross margins have slightly improved in the last couple of quarters, while G&A runs at a reasonable single digit % of revenue, and sales efficiency has stayed overall pretty good albeit bolstered by the AI natives as we just discussed. R&D runs on the higher side, but I’m ok with that - they are very aggressively investing in building out as many products that they can sell to a large customer base, and I think AI coding tools are likely to drive improvements here over time. The company generated 29% FCF margins for LTM (per their investor presentation), and if you squint and look to the future, you can see that moving up higher as they find more efficiencies. A bump of 10%+ doesn’t seem unreasonable over the next 5 to 10 year horizon, meaning this company will generate significant cash flow, with growth being the big question as to how much exactly.

Thoughts on Valuation

When it comes to valuation, I don’t have a perfect answer. Multiples are useful in that they can tell you in the moment a quick ratio to compare against other companies, but don’t guarantee anything. In 2010 SaaS companies were trading for 5-6x revenue. In 2020 that was more like 50-100x revenues. Now it’s 10-20x. Who knows where it’ll be next year.

DCFs also have their problems. Not only is every forecast likely to be horribly off (I’ve never seen a single one that was close), most of the value is stuck in the terminal value when you throw your arms up and decide to stop forecasting each year. Not exactly helpful either.

I decided to employ a thought exercise to help think through intrinsic value, while leaning more heavily on multiples for near-term expectations to balance the two. My approach is much simpler: let’s pick a CAGR for free cash flow for the next 10 years, in this case, let’s say 25%. Let’s then assume the company is a zombie at the end of that 10 year horizon, meaning they’re able to squeeze another 5 years of cash flows at the Year 10 rate before they close up shop. Why 5 years? Enterprise software has a reasonable long shelf life most of the time, and this gives us a time horizon that is plausible, but doesn’t require a perpetuity growth rate that goes on for a horizon that is never going to happen. If we pretend the company just closes shop at the end of those 15 years, we just sum up the total cash flows, divide it by the total shares outstanding, and see what we have. In this case, the 10 year horizon nets us $30B and the 5 years gives us another $34B, summing to $68B in total. Assuming share dilution from RSU / PSU issuance of 2.5% annually over that time frame, we get to 435M shares, and with each share getting $147 in cash.

If at this point, you’re saying, “uh Phil, wtf. How is this any better than a DCF?” I’ll tell you. It’s a greatly simplified thought experiment on what I need to believe to buy at the current multiple. If we go back to the 25% FCF CAGR, I can think of this as two parts. I can see a 10 year revenue CAGR around 20%, driven by the massive product portfolio, expanding geographic reach, capturing AI, etc. Couple that with FCF expansion that we talked about above, and 25% doesn’t seem wholly unreasonable. So if I can believe that, then taking the net $6 cash / share today and adding $147 more, I get to $153 which is thankfully more than where the stock last closed. I don’t think anyone ever invests with the long-term expectation you’ll just break even, but I think it’s important to know that if the company is on a cash flow trajectory that can get there, then every subsequent year drives a compounding return on value.²

So then back to multiples: you can apply all sorts of regression analyses, Rule of X, or whatever you’d like to say what you think the multiple should be, but the two easiest things to do are 1) to roll the ‘24 revenue multiple forward a year, and apply it to our ‘25 estimate to say what we think the stock should be worth at the end of the year, and 2) apply a modest bump to the current ‘25 multiple, with expansion coming from higher growth and a larger metric leading to a higher valuation.

1. My approach for most healthy growing SaaS companies is Revenue * 4 * 1.03. Most people I’ve seen just multiply by 4 because it’s easier to be lazy, and scaling everything by 1.03 doesn’t change growth rates. However, when it comes to segmenting by cohorts of existing vs. new or certain verticals, I find it’s helpful to get a bit closer. There’s still noise within this, but I suspect I’m closer to reality.

2. Two quick notes on this: the first is Servicenow’s phenomenal run from post-IPO until now, both in terms of financial and stock performance, illustrates how the market has rewarded companies that have been able to keep up growth and improve cash flow over time.

Second, this sort of analysis is a helpful framework to me to put multiples in context. As an example, if you look at where Zoom traded at its peak in 2020/2021 - you’ll see they needed to deliver triple digit FCF growth for 10%+ years, vastly outweighing likely estimates for how much of the video conferencing market they could capture (or even how big that market was to begin with!). We’re now at a point where most companies will need to re-accelerate growth or hope for a better macro environment to feel good about paying these kind of prices…but that’s an analysis for another time. At the end of the day, however, I’m not sold there’s clear predictive value across the SaaS universe using this methodology, but it at least provides more comfort for me in a floor on the price.