Core business speeds up while top AI customer pulls back
- Datadog runs a usage-based SaaS model, which means customers pay as they monitor more hosts or index more data.
- The core story is land and expand: customers start with one tool, then add more products over time.
- Large customers matter most, with about 4,720 customers over $100,000 in ARR making up 91% of ARR as of June 30, 2026.
- Multi-product use is rising fast, with 13% of customers using ten or more products.
- The main debate is whether a usage reduction from their largest AI customer will overshadow growth in the rest of the business.
A dual-track growth story
Datadog is a high-quality cloud software business with a clear expansion engine. Its core non-AI customer business accelerated to high-20s percent year-over-year growth in the second quarter of 2026. The existing customer base is still spending more over time, and large customers now make up a larger share of revenue than ever before.
The strongest sign of stickiness is product depth. Customers are using more of the platform to replace disjointed tools. Early in 2026, 13 percent of customers used ten or more products. That makes Datadog harder to replace because it becomes part of how developers, operations teams, and security teams work each day.
The new twist is AI volatility. Datadog landed deals to monitor AI training workloads, but AI companies also optimize their spending. Management noted that Datadog's largest customer, a leading AI company, will reduce its usage starting in the third quarter of 2026. This adds uncertainty to the growth story and highlights severe concentration risk at the top of the customer base.
The stock still carries a price problem. The core business is strong, but valuation is not cheap. If AI demand wobbles further, if customers cut cloud spending, or if hyperscalers compete more aggressively, investors may not give Datadog much room for mistakes.
Paid by cloud usage
Datadog sells subscriptions to its cloud-based observability and security platform. Subscription terms are mostly monthly or annual. Revenue is tied to usage, mainly the number of hosts monitored or the amount of data indexed.
This model can grow quickly when customer workloads grow. It can also slow quickly when customers look for ways to reduce data volume, monitored hosts, or cloud costs. That is why Datadog's usage trends matter as much as total customer count.
The sales motion is simple: land with one product, prove value fast, then expand into more products. This is working best in large accounts. As of June 30, 2026, customers with more than $100,000 in ARR represented 91 percent of total ARR, up from 89 percent a year earlier.
Datadog also sells through a direct sales force and cloud-provider marketplaces. That helps reach cloud buyers, but it keeps the company tied to the same hyperscalers that can also compete with it. New features like Bring Your Own Cloud allow customers to keep data on their own infrastructure, which could change how Datadog makes money on data ingestion.
One platform, many hooks
Infrastructure monitoring
This tracks servers, containers, databases, and cloud resources. It is often an easy first Datadog product for engineering teams.
Application performance monitoring
APM helps teams find slow or broken parts of an app. It is core to Datadog's value because it links app health to the rest of the tech stack.
Log management
Log tools collect and search machine data from apps and systems. This can scale with usage, but it is also an area where customers may optimize data volumes.
User experience monitoring
These tools show how real users or test users experience an app. They help Datadog reach teams that care about uptime, speed, and customer impact.
Cloud security
Security products extend Datadog from monitoring into finding risks and threats. This supports the multi-product expansion story.
GPU monitoring and LLM observability
These products target AI workloads, including training runs and large language model apps. The open question is how much they can add to total growth.
Bring Your Own Cloud (BYOC)
These offerings let Datadog run on customer infrastructure for logs, metrics, and traces. They are meant to serve data residency and sovereign AI needs.
Mostly North America
Datadog reports one operating segment. The mix below uses customer billing geography for the three months ended March 31, 2026, when revenue from outside North America was about 28% of total revenue.
What could break
AI usage swings
High impact · High oddsAI-native customers have shown a pattern of fast usage growth followed by optimization. Starting in Q3 2026, Datadog's largest customer will reduce usage despite renewing its contract, creating a near-term revenue headwind.
Hyperscaler bundling
High impact · Medium oddsDatadog competes with AWS, Azure, and Google Cloud, plus their native monitoring tools. Those cloud providers can bundle tools into broader contracts. Datadog also relies on cloud ecosystems for distribution and infrastructure, which makes the relationship both helpful and risky.
Expansion slows
High impact · Medium oddsThe model depends on customers using more Datadog products over time. If dollar-based net retention falls out of the 120 percent range, the core thesis weakens. A slowdown in six-plus, eight-plus, or ten-plus product adoption would be an early warning.
Cloud cost and margin pressure
Medium impact · Medium oddsDatadog hosts its platform on third-party cloud infrastructure. Higher cloud infrastructure costs have already been called out as a margin pressure in prior filings. New AI products may require more investment before they reach full margin scale.
Security or privacy failure
High impact · Low oddsDatadog handles sensitive operational and security data for customers. A major breach could hurt trust, create legal costs, and slow sales. Global privacy rules also add compliance risk as the company grows outside North America.
In one breath
What does Datadog do?
Datadog sells software that helps companies watch their cloud apps and systems in real time. It covers infrastructure, app performance, logs, user experience, and cloud security.
How does Datadog make money?
Datadog sells subscriptions to its SaaS platform. Many subscriptions are usage based, so revenue rises when customers monitor more hosts or index more data.
Why does AI matter for Datadog?
AI workloads create complex systems that need monitoring, especially GPUs and training runs. Management says AI training is a real market for Datadog, but AI companies also go through intense spending optimization cycles.
What is the biggest risk for Datadog investors?
The biggest risk is that usage growth slows while the stock still prices in strong growth. AI-native customers can grow fast and then optimize spending, which can make revenue less predictable.
