I was planning on publishing this piece late tonight alongside Airbnb and DraftKings, but I decided to just publish it now instead of waiting. The rest is coming tonight.

There’s a lot going on within this product suite and I think understanding the basics is important. This recurring section will be review for some. If it’s not for you, let’s learn:

This is a dominant player in the observability space. Observability simply refers to the practice of monitoring an entire asset and data ecosystems to track issues, vulnerabilities and performance. Other players within this area include the hyper-scalers, Splunk/Cisco, Elastic, CrowdStrike and many more. Datadog splits its observability niche into 3 smaller buckets: infrastructure monitoring, log management and Application Performance Monitoring (APM).

Infrastructure monitoring: provides a holistic view of assets like servers and networks. It automates the collection of traffic and overall usage insights. That means it can more expediently fix and uncover infrastructure issues and bottlenecks. For example, this product can help clients and their other vendors uncover where compute capacity is being sub-optimally distributed. Fixing those inefficiencies cuts costs. In a world where chip utilization rates are routinely below 10% for hyperscaler cloud customers, that matters.

Log management: collects and manages logs or “timestamped records of events.” This also facilitates faster issue remediation and optimization of performance. This product routinely supports infrastructure monitoring, BUT there’s a key difference between the two. Log management handles event-based data like customer service interactions, while infrastructure monitoring (as the name indicates) handles infrastructure-based metrics.

Application Performance Monitoring (APM): tracks app performance and uncovers/prioritizes performance issues to be remediated.

There’s also a newer, related form of Datadog monitoring called Digital Experience Monitoring. It’s exactly what it sounds like. This product includes real-time user monitoring (RUM) to track precise, observed interactions, and also Datadog Synthetics, which is similar to RUM, but tracks a simulation of expected interactions. Datadog delivers detailed churn analysis, engagement metrics and more from these tools. It also provides mobile app and feature testing, as well as actionable user journey visualization reports.

These four product categories, which frequently work together, form its “unified platform.” Other products to know within this overarching offering include Flex Logs (part of log management). The product broadly rolled out towards the end of 2024. Flex Logs offer a cost effective means to store and retain large batches of logs by separating storage and query usage. This makes it ideal for long term data storage and regulatory compliance. Separation also unleashes more data scalability, query customization and cost optimization. Conversely, querying from a flex log is slower than standard logs. That makes Flex Logs better suited for lower priority data.

Because Datadog already handles network viability, security is a wonderfully relevant growth adjacency. Products like Cloud Infrastructure Entitlement Management (CIEM) for example, ensure identity controls are strict and minimum access permissibility is in place. There’s a lot of competition with configuration-based cloud tools like this one, but Datadog is no slouch. CIEM diminishes risk of identity attacks in a cloud environment. Its Security Information and Event Management (SIEM) product allows for “long term data log visualization for security investigations.” This can be done without dedicated staff to make cloud migration and usage easier. Most recently, it added agentless environment scanning (no security agent installation needed) to match with its agent-based product.

It offers a host of products within Cloud Service management as well. For example, its Kubernetes Autoscaling tool handles resource usage and expansion optimization. It pulls from extensive usage data to tell customers where they can save on compute capacity and other areas. This is part of its cloud service management push.” It also launched Kubernetes Active Remediation, to help guide clients through optimal cloud issue remediation.

But… this intro would not be complete without its GenAI product work. Toto is the name of its first foundational large language model (FLLM) and Bits AI is its copilot. So far, this can summarize incidents and conversationally field questions. Much more is coming. And unsurprisingly, it also tweaked and configured its core products to cater to LLM observability specifically.

In terms of Q4 growth drivers, usage growth was similar compared to last year. The business environment was called stable, with continued cloud migration momentum and still some cost conscious customers. NRR drove the outperformance for revenue this quarter, as new and large customer growth were about as expected. Enterprise customers delivered the strongest usage growth, but small and medium business (SMB) customers still delivered “solid” usage expansion. Gross revenue retention (GRR) remained in the mid-to-high 90% range. 

Note that DDOG asks investors to focus on revenue rather than billings, bookings and RPO to gauge demand. Timing of invoices, deals and more can throw off those other metrics on a quarterly basis.

OpEx rose by 30% Y/Y vs. 21% Y/Y growth last year. It continues to lean into sales and marketing (S&M) and research and development (R&D), as both deleveraged by about 1 point Y/Y. It accelerated investments in product development and sales capacity throughout 2024. G&A provided the rest of the EBIT margin Y/Y deleveraging.

DDOG issued $1 billion in 0% interest rate convertible notes maturing in 2029. Some of the proceeds were used to enter into capped call agreements to put a ceiling on potential dilution and some were used to repurchase 2025 convertible notes. It will retire the rest of these 2025 notes this year. The net impact is a $736 million boost to its cash pile.

“As a reminder, we base our guidance on trends observed in recent months and apply conservatism on these growth trends.”

CFO David Obstler

For 2025, revenue guidance missed by 1.7%, EBIT guidance missed by 17%, and $1.68 EPS guidance missed by $0.30. Q1 guidance was similarly weak across the board.

Datadog trades for 67x 2025 EPS estimates and 56x 2025 FCF estimates. The chart below is based on next 12 month estimates from Q4-24 through Q3-25. When it moves forward by and quarter and resets, it will likely be around 70× 2025 EPS after more downward pressure on estimates. EPS is expected to grow by 12% this year and by 23% next year. FCF is expected to grow by 21% this year and by 28% next year.

“Operating profit guidance reflects our intent to continue to invest for future growth in 2025… as we did in 2024, we expect to grow our investments in both S&A and R&D.”

CFO David Obstler

More 2024 Product News – Cloud:

A large portion of the prepared remarks were spent working through the product releases that Datadog has deployed over the last four quarters. Some of these launches were included in the Datadog 101 section of this article, but there were more to discuss. The company shipped 400 new updates during the year and pushed its roster of product integrations to 850 to make it even easier for DDOG customer’s to cohesively bring their data and work to its platform.

In cloud security, DDOG now has 7,000 customers and is finding continued momentum. Aside from the agentless scanning launch, it added new code security tools to fix issues with source code configurations. This pushes it “further left” on the DevSecOps scale towards developers (closer to original source code creation). It also added security posture management specifically for Kubernetes-based assets.

Looking to 2025, the focus will be on more software composition analysis launches and nurturing the cross-selling momentum between log management and security work-load use cases. It wants to create better systems (go-to-market and product integrations) for accelerating this progress.

In cloud service management, the Datadog On Call launch from January is garnering “significant customer interest.” It also launched AI-powered event management into general availability.

2024 Product News – Product Platform Integration & Open Telemetry:

It became a “better platform for open telemetry” by fully integrating its infrastructure monitoring and APM suites. Open telemetry standardizes data formats across various parts of the tech stack to make open collaboration and sharing between those pieces easier. It creates an environment for more unified learning and work. In APM specifically, it added an error tracking tool allowing customers to “view and manage errors across user sessions, applications and logs all in one place.” That should provide highly valuable data for infrastructure monitoring use cases thanks to these investments in open telemetry. 

2024 Product News – App Building & Digital Experiences:

It also made great strides on its App Builder product to allow developers to customize existing tools, apps and models with their own data and work.

Within digital experience monitoring, Datadog debuted mobile app testing for both iOS and Android. Now, users can conduct this testing right from their actual mobile phones. This expedites finding app issues and comes with session replays to ensure engineers don’t miss problems with product construction. It’s also beta testing more product analytics tools for experience monitoring, with “encouraging customer interest” early on.

2024 Product News – AI, Data & Log Management:

Within the world of AI, it launched incident management tools for its Bits AI product and soft launched autonomous investigations to remove the manual work from uncovering issues with infrastructure. This is expanding to everything else it monitors — large language models (LLMs), apps, usage patterns etc. As of today 3,500 customers are using one or more Datadog AI integration to funnel their AI and machine learning usage into Datadog’s ecosystem for proper maintenance and monitoring. 3,500 compares to 3,000 Q/Q & 2,500 2 quarters ago.

Data observability, which has use cases spanning all major product buckets launched “Datadog Data Jobs Monitoring.” This offers prioritized alerts to data scientists to uncover issues with Spark and Databricks workloads “anywhere in their product pipelines.” It also added Amazon’s Simple Queue Service (SQC) and MongoDB as data integration partners to deepen the roster of popular data vendors that DDOG can “provide deep insights into.”

In log management, aside from the Flex Logs launch, it added more tools for “advanced analytics and querying” and simplified the creation and implementation of observability pipelines, with templates to guide design and inspire best practices.

Products Creating a Platform Play & Customer Momentum:

Building great products is nice, but it’s even nicer when those products drive growing adoption of and engagement with DDOG’s overarching platform. Cross-selling products across its 3 main pillars does the same thing that it does for any other enterprise software name: more revenue, lower churn, higher margins. Driving towards this monitoring and cloud security platform means vendor consolidation, lower customer costs, better outcomes and a more resilient business model. It’s so important.

Luckily, things are going well here. It crossed $1 billion in quarterly bookings for the first time and signed its “highest number of new logos since early 2023.” Customers delivering $1 million or more in ARR rose 17% Y/Y and multi-product adoption trends continued moving up and to the right. While ARR overall crossed $3 billion this quarter, that was thanks to large contributions from all three major buckets: infrastructure monitoring hit $1.25 billion in ARR; log management hit $750 million in ARR; APR also hit $750 million in ARR. Furthermore, its emerging product business, including things like Bits AI, already has $200 million in ARR. The runway for everything Datadog sells remains miles long and the traction is palpable.

In terms of customer wins during the quarter, it offered the following highlights:

More on AI:

Datadog reminded us that it does not monetize GPUs and training workloads like hyperscalers do. It needs to see these GPUs being used by more than a “few AI native customers” to justify more investments in monitoring those specific assets. For now, most of the usage for these chips is still from those AI native companies.

Regardless, its GenAI-adjacent tools seem to be resonating as support systems for AI models and apps. AI native customers were 6% of ARR vs. 3% Y/Y and added 5 points to revenue growth vs. 3 points Y/Y as DDOG retrofits its offering to cater to GenAI workloads. At the same time, as these AI native customers get much larger, they are able to negotiate better terms for DDOG product usage. That played out somewhat sharply in Q4, but it was anticipating this and guided accordingly. Leadership also reminded us that while these renegotiations may lead to flat usage growth for a month or two, it’s a short-term pause in expansion rather than a permanent ceiling.

It also still has not seen many customers moving from simple chat bots and training to Agentic AI and inference focuses. It thinks that’s coming next, thinks the DeepSeek news is accelerating its own monetization path, and thinks of itself as very well-positioned to benefit from this.

“What's interesting to us is how the rest of the world will start operating AI workloads.”

CEO Olivier Pomel

Homegrown Competition:

DDOG leadership was asked about homegrown competition, or companies building observability tools in-house. It thinks there’s a very small handful of companies with the resources and budget where this makes sense to do. None of those companies are “in the market for selling software and never have been.” The “rest of the word is their core market.”

This quarter was a lot like DDOG’s other quarters. Great results… strong product momentum… strong profitability (despite ramping investments)... and an overly conservative guide. The company is clearly fortifying itself as a leader in not just infrastructure monitoring, but its other product pillars as well. As it continues to integrate these three pieces (and experience monitoring), I expect more cross-selling, higher retention and more DDOG success.

To me, the team just set themselves up for an easy year of beating and raising, which is what an often short-sighted Wall Street cares about. This quarter doesn’t make me think any less fondly of the company. It’s one of my favorite enterprise software names not currently in my portfolio. That didn’t change.