Datadog sees enormous growth opportunities in AI – Nanalyze

If you are a software company with a weak value proposition, AI is a threat because it allows others to build something better and deploy it quickly. But for companies with business models that process a lot of proprietary data in an automated way, AI allows them to do all kinds of exciting things with it. That’s right Datadog $DDOG sits at their observation platform that clearly benefits from the growth of artificial intelligence. The last time we checked in with Datadog, they were forecasting annual revenue of about $3.2 billion, up “only” 18% to 19% year over year. Instead, they achieved 28% revenue growth by 2025.

Now they are again telling us to expect 19% for the coming year, which would still be impressive considering they are starting from a larger base. Perhaps most striking is that a significant portion of that growth surprise would come from artificial intelligence.

Datadog has room for growth

In their latest call, Datadog said they “continue to believe that revenue is a better indicator of our business trends than billing and RPO,” and we couldn’t agree more. For a SaaS company, increasing revenue comes from selling more to existing customers and adding new customers. Despite having 32,700 customers, Datadog still describes the new logo additions as “very strong”, capturing just 7% of its total potential customers. Even more impressive are the increasing revenue commitments from their largest customers.

We don’t get an average contract length, but that’s typically one to three years for SaaS companies. So when Datadog says, “we signed 18 deals over $10 million in TCV this quarter, 2 of which were over $100 million and 1 was an 8-figure country with a leading AI model company,” it implies strong demand for the services they provide, which is a leading indicator of revenue growth.

It also implies that there are many benefits just from existing customers, especially the larger ones. About 90% of Datadog’s revenue comes from customers who spend more than $100,000 a year, meaning they are firmly entrenched in enterprise sales, unlike people who pay for a few seats with a credit card. And get this. While 48% of the Fortune 500 are customers, they still spend less than $500,000 on the median, suggesting a significant advantage over existing large accounts.

While Datadog’s net retention rate is an elusive number to pin down, its latest earnings call notes that “revenue retention rate was approximately 120%, similar to last quarter.” That’s considered average for a quality SaaS company, and it’s likely that a large portion of that new spend will be (or according to management, that will certainly happen soon) of upselling a lot of cool AI stuff to existing customers.

Datadog for AI and AI for Datadog

As we said in our video ‘AI vs. SaaS,” there is no better time to sell AI software to C-level types who want to tell shareholders that they are “doing AI.” So it’s not enough to talk about agentic AI solutions you’re working on (cough, UiPath, cough). You actually have to sell AI functionality to customers. Bonus points if you develop and implement something that most AI companies need. Datadog has managed to do both.

“AI for Datadog” is about the AI-powered features they’re building and deploying, which should result in more revenue from existing customers. They talk about the Bits AI Dev agent, which detects problems at the code level and generates solutions in the production context. Additionally, their Bits AI Security Agent can help release and monitor a solution, and provide complex troubleshooting if necessary. This bot can autonomously conduct investigations and make recommendations based on them Ssafety Iinformation and Edeaerate Mmanagement (SIEM) signals, which are simply warnings that Datadog’s software spits out when it encounters anomalies in a customer’s data outlet.

“Data dog for AI” is the unique product offering they sell to AI companies, specifically “capabilities that provide end-to-end observability and security across the entire AI stack.” Last quarter, they landed “one of the largest AI foundational model companies” with a fragmented observability stack consisting of more than five open source, commercial, and internal observability tools. All these unified solutions have been brought under the unified Datadog platform, meaning the customer is now more productive and can scale with ease.

Datadog says they have 650 “AI-native customers,” which are essentially AI startups that form a cohort that significantly outpaces the rest of their business. About 19 of these customers spend $1 million or more annually with Datadog and “14 of the top 20 AI-native companies are Datadog customers,” implying that most notable AI companies are using their solution as they scale. And guess who has more money than they know what to do with right now? That’s right, AI companies, which now account for 11% of total revenue and are growing rapidly.

Datadog has also developed its own small observability LLM internally called Toto, which is trained on 750 billion data points unique to the company. It cost $750,000 to train, compared to billions for leading AI companies, and trillions of new events are offered every hour.

A Soften-AS-A–Semploy (SaaS) company for which AI is an opportunity and not a threat should command a price premium. Even with all the AI ​​excitement in the air, Datadog has a valuation that is rich but not exaggerated. And lately it has been on the decline, which is great news for people who like to buy high-quality SaaS names at discounted prices.

Datadog’s rating returns to average

Last year we commented on Datadog’s rich valuation, with an average trailing of four quarters Simplement vvaluation Ratio (SVR) from around 16. That average has now dropped to a more modest 14, with their current SVR being just 11.5. This means that despite Datadog’s impressive performance and continued strong revenue growth, shares appear inexpensive compared to past valuations. In fact, they have only become cheaper. Why?

The AI ​​sword has two edges. While Datadog is seeing healthy demand for their own AI tools, they are also facing quite a bit of headwind from competing AI companies. The trio of Great Danes in the room would specifically be the hyperscalers Aman Webb Sservices (AWS), Google Cloud and Microsoft Azure. These three companies have started implementing their own observability and AI monitoring tools on their platforms. For example, AWS offers Amazon CloudWatch, which is built into more than 120 AWS services. CloudWatch allows you to collect and aggregate various data metrics and analyze them for anomalies – essentially exactly what Datadog’s flagship software does. Azure Monitor and Google Cloud Operations Suite offer similar services.

To compete with the big dogs, Datadog must show that its third-party software can add value beyond these built-in tools. Our intern Tidder surveyed the developer community and found that the majority prefer Datadog over CloudWatch thanks to its ease of use and improved features such as custom ‘health checks’. Datadog also has the added benefit of supporting ‘multi-cloud’ operations, meaning it can be used on AWS, Azure and Google Cloud simultaneously. That bodes well as most companies are moving towards using multiple clouds.

There’s also the “AI eats software” narrative that’s been prevalent lately – basically it’s about AI agents slowly replacing humans. This will lead to companies dropping their software subscriptions or abandoning them completely in favor of advanced AI models like Anthropic’s Claude. Although Datadog is largely usage and not seat based, it is not completely shielded from fear of disruption. For example, Datadog uses AI to highlight problems and propose solutions in code. Why can’t any AI agents – not just the ones built by Datadog – proactively solve these problems?

We can speculate until the Datacows come home, but until we see weakness in Datadog’s customer base and revenue growth, what management is saying seems credible. AI is an advantage rather than a threat.

Conclusion

With all the fear-mongering around AI replacing SaaS platforms, Datadog stands out as a company in a strong position. Their highly automated platform pulls data from over 1,000 integrations and compiles it into a single source of truth. Trillions of data points are added every hour and then passed to AI algorithms to make sense of them. Now they go to officers who take action when necessary. The addition of security to their stack provides stickiness and lends itself to vendor consolidation initiatives.

If you’re an AI-native company of any significance, you’re more likely to use Datadog for everything from surveillance to security. As always, the proof is in revenue growth, and Datadog has that in spades. Let’s hope they can blow this year’s numbers out of the water and keep the acceleration going.