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Datadog (DDOG) delivered strong 1Q25 results, surpassing EPS and revenue expectations, staying true to the company's historical trend of beating analysts' forecasts. In fact, DDOG has exceeded top and bottom-line estimates in every quarter going back at least five years. The Q1 outperformance was driven by strong usage growth among existing customers -- particularly large accounts with $100K+ in annual recurring revenue (ARR) -- and continued adoption of its multi-product platform.

However, DDOG's guidance for 2Q25 and FY25 was mixed, with the midpoint of both EPS ranges coming in slightly below expectations, while revenue came in above estimates. As the company prioritizes growth and innovation, R&D spending is likely to remain elevated (+27% in Q1), as is its Sales and Marketing expense (+21% in Q1) due to customer acquisition efforts. Additionally, some enterprises are optimizing cloud workloads amid rising macroeconomic uncertainty, further pressuring operating leverage and EPS.

• DDOG's growth was propelled by a 13% increase in large customers ($100K+ in ARR), reaching 3,770 and accounting for 87% of total ARR despite comprising only 12% of the 28,700 total customer base. Growth in large customers stems from increased cloud adoption and demand for the company's observability and security products, especially among enterprises undergoing digital transformations. Furthermore, DDOG's land-and-expand strategy is proving to be effective, reflected by a strong dollar-based net retention rate of 115%, indicating robust expansion within existing accounts.
• Like competitors Snowflake (SNOW) and MongoDB (MDB), DDOG is benefitting from secular tailwinds such as cloud migration, digital transformation, and rising AI adoption, fueling demand for monitoring and data analytics platforms. Also, like SNOW, the company's usage-based business model, which charges customers based on the volume of hosts, containers, or data processed, was an asset in Q1. The flexibility of the usage-based model allowed DDOG to capture additional revenue from customers, while the model's low friction for adoption enabled rapid onboarding of new customers, which grew by 10%.
• On the downside, DDOG's conservative EPS reflects the challenges in forecasting usage patterns during volatile macro conditions and cloud optimization trends. DDOG's model makes revenue projections less predictable than traditional subscription-based SaaS, but DDOG does have a tendency of guiding conservatively. 

DDOG's Q1 results highlight its robust growth and healthy free cash flow generation of $244 mln, reinforcing its strong market positioning in cloud observability. However, its mixed guidance, driven by macroeconomic caution, increasing expenses, and usage-based model variability, is overshadowing the upside Q1 results.