Datadog ($DDOG) – Earnings Review – August 08, 2024
Datadog 101:
Datadog is a dominant player in the data observability space. Observability simply refers to the practice of monitoring an entire software ecosystem to track issues, vulnerabilities and performance. Other players within this area include the hyper-scalers, Splunk, Elastic, CrowdStrike (through its Humio acquisition) 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.
Log (or record of event) management: manages “timestamped records of events” occurring across the entire infrastructure. This also facilitates faster issue remediation and optimization of performance. These logs are organized and utilized within infrastructure monitoring and other use cases to identify things like customer service issues. Log management encompasses the collecting, maintaining, and leveraging of log data. This product routinely supports infrastructure monitoring, BUT there’s a key difference between the two. Log management handles event-based data, 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.
These three product categories closely tie together to form its “unified platform.”
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 here, 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.”
“At Datadog, we’re focused on helping our customers observe, secure, and take action on their complex systems.”
Co-founder/CEO Oliver Pomel
Datadog Demand
- Beat revenue estimates by 3.4%.
- Beat billings estimates by 4.6%.
- Total customer count rose 10% Y/Y.
- $100,000+ annual recurring revenue (ARR) customers now represent 87% of total revenue and that continues to climb.
- 11% of its clients are now using 8+ products vs. 7% Y/Y.
Source: Brad Freeman – SEC Filings, Company Presentations, and Company Press Releases
Profits & Margins
- Beat EBIT estimates by 14%. Operating expenses (OpEx) rose by 21% Y/Y, which includes $11 million in costs from its DASH user conference.
- Beat EPS estimates by 16%.
- Missed FCF estimates by 6%. FCF is a lumpy metric based on timing of collections, tax levels and other payments. I think annual FCF is what to focus on. Trailing 12 month FCF is up 58% Y/Y to $670M.
Source: Brad Freeman – SEC Filings, Company Presentations, and Company Press Releases
Balance Sheet
- $3B in cash & equivalents.
- No traditional debt.
- $744M in convertible senior notes.
- Diluted share count rose by 2.3% Y/Y.
Guidance & Valuation
“We base our guidance on trends observed in recent months and apply conservatism on these growth trends.”
CFO David Obstler
- Raised annual revenue guidance by 1%, which beat by 0.5%.
- Raised annual EBIT guidance by 5%, which beat by 4.2%.
- Raised annual EPS guidance from $1.54 to $1.64, which beat by $0.08.
- Headcount growth in 2024 will be faster than during 2023.
- It sees CapEx at a modest 3.5% of 2024 revenue. It’s not playing the “build the best model on your own” contest.
- There’s no change to Datadog’s long term prospects.
Next quarter guidance was slightly light on revenue, ahead on EBIT and ahead on EPS.
Datadog trades for 69x 2024 earnings. EPS is expected to grow by nearly 20% this year and by 22% next year.
Source: Brad Freeman – SEC Filings, Company Presentations, and Company Press Releases
Call & Release
Product expansion was the centerpiece of this call. Within GenAI and non-AI products, Datadog is quickly expanding its suite of use cases within various observability buckets, cloud and data security, cloud service management and GenAI.
Non-AI Product Expansion:
Datadog Flex logs were fully rolled out during the quarter. As a reminder, this is its cost effective means to store and retain large batches of logs. They’re priced at $0.60 per 1 million annually and allow for separation of storage and query costs. This makes it ideal for long term data storage and regulatory compliance. With Flex Logs, storage and computation can scale in a parallel, independent manner. This separation for Datadog’s clients unleashes far more data scalability, customization and cost optimization. Conversely, querying from a flex log is slower than for Datadog’s standard log tier. That makes Flex Logs better suited for lower priority data.
Datadog is pushing further into digital experience monitoring. Datadog Synthetics is its way of testing interactions and processes in a zero stakes environment. It allows companies to predict and simulate usage patterns to test interface and product resilience. It can uncover issues before those issues ever are deployed as part of a software package. This is somewhat similar to real-time user monitoring (RUM), except RUM is tracking actual user actions, rather than a simulated user. This can scrape findings from how a user experience is progressing and if it needs to be tweaked. Datadog can provide churn analysis, engagement metrics and more. Each product now has $100 million in annual recurring revenue (ARR), which now gives the firm 5 products with that amount of business.
- Other newer innovations within digital experience monitoring include mobile app testing, feature flag testing (test a piece of a product without disrupting the whole thing), user journey visualization etc.
Within cloud security, there were several new product announcements. It added agentless cloud scanning, which allows Datadog’s security tools to run in a client’s client environment without needing to actually install any separate software (or agents). Some customers prefer this agentless approach, some don’t. Datadog now provides both. It added code security to allow “customers to detect and prioritize code-level vulnerabilities in their products and apps.” This also pushes Datadog “further left” in the DevOps space towards source code creation and maintenance.
Its new data security tool (only for AWS for now) can uncover sensitive data, which is a perfect complement to its asset, app and infrastructure observability cores. Data is the engine driving every single asset and product a modern enterprise provides. Datadog now not only monitors the hygiene and usage of this data, but flags vulnerabilities too. Finally, Live Debuggger frees developers to “step through code in production environments & find the root cause of coding errors.”
The firm also added an OpenTelemetry integration with its agent. OpenTelemetry is a popular open-sourced means of collecting various kinds of data. Datadog now allows this to happen from within its environment without the need for a separate vendor.
Log Workspaces is its new collaborative environment for cross-department engineers to collect and combine assets. From there, they can build complex, “multi-stage” queries that can pick and pull needed data from different parts of an enterprise to combine for new use cases.
Its new 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. Another big piece of this push is the new Datadog App Builder. This allows developers to easily build applications with native integrations into the rest of their Datadog products.
AI Product Expansion & Monetization:
Toto is its first foundational large language model (FLLM). It utilizes Datadog’s enormous supply of data to augment customer workflows. It is “state of the art” in terms of performance across all major FLLM benchmarks.
Bits AI is the firm’s copilot. It can summarize incidents and conversationally field questions. The chatbot can also now be installed as an agent within a customer’s architecture to automate performance or security investigations.
Finally, Datadog debuted LLM observability. This allows developers to watch, upgrade and maintain their LLMs to accelerate the deployment of GenAI apps. And that’s a very important idea. As I’ve said many times, the first wave of GenAI monetization has been within chips and servers… all hardware. That foundation is being laid to ensure the proper amount of capacity is in place to support decades of high performance compute (HPC) applications. That wave is where Datadog will make its GenAI financial mark. LLM Observability sets the table for expediting and strengthening the GenAI app wave.
Sticking with GenAI monetization for a moment, Datadog now has 2,500 AI integrations vs. 2,000 Q/Q. AI integrations within the Datadog platform allow customers to place their first party data right into its ecosystem to accelerate model and app work. These integrations seem to be delivering momentum, as 4% of its ARR is now from AI customers vs. 3.5% Q/Q. It also continues to add observability support for data players like Snowflake, with data being the salt to any GenAI model’s pepper.
Platform Play Effect & The Business Environment:
If all of this product news is the cause, more cross-selling, vendor consolidation and platformization are the effects. During the quarter, DDOG signed its largest contract ever with a South American Bank. They were struggling with overarching visibility, and now with Datadog, they aren’t. It highlighted three large 7 figure deals with a travel management firm and security firm to drive point solution displacement, better outcomes and $500,000 in annual savings for one of the clients. Another one of them is saving $1 million per year in OpEx. The European Central Bank (ECB) cut two more vendors in favor of DDOG and is now using 17 of its products.
Point solution elimination routinely allows customers to pocket efficiency gains and do “more with less.” Especially in today’s environment, that is working. It’s allowing Datadog to cut through continued customer hesitancy to deliver strong results. Gross revenue retention stayed in the mid-to-high 90% range and it continued to see usage growth from existing customers accelerate. That was most pronounced within its large enterprise segment, with smaller customers yielding stable Q/Q usage growth.
Take
This was a good quarter. The guidance raises weren’t as explosive as we’ve seen with a small handful of enterprise software names, but this was still better than most. DDOG is successfully expanding beyond its core observability niche and laying the GenAI product foundation needed to support app monetization down the road. It continues to guide very prudently, continues to find steady growth and keeps delivering more EBIT leverage. Rock-solid quarter from a rock-solid company. I don’t see 70x earnings as compelling for a 20%-25% EPS compounder, but the actual quarter was quite good.
