Table of Contents

• 1. Datadog (DDOG) – Earnings Review

  • a. Datadog 101

  • b. Key Points

  • c. Demand

  • d. Profits & Margins

  • e. Balance Sheet

  • f. Annual Guidance & Valuation

  • g. Call & Release

  • h. Take

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.

1. Datadog (DDOG) – Earnings Review

a. Datadog 101

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.

b. Key Points

• Great quarter.

• Strong bookings and forward-looking momentum, but a conservative guide.

• To keep accelerating hiring and overall expense growth in 2025.

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