Strong AI demand drives upward revision of growth expectations! Bernstein upgrades Datadog (DDOG.US) to "Outperform" with price target raised to $350.

In its latest research report published on October 19, Bernstein upgraded cloud monitoring and security software company Datadog (DDOG.US) to "Outperform" and sharply raised its price target from $237 to $350. The firm believes that the growth in computing power usage demand from artificial intelligence (AI) companies has far exceeded previous expectations and is expected to drive sustained high-speed revenue growth for Datadog over the coming years. Based on the October 16 closing price of $247.11, the new price target implies approximately 42% potential upside for the stock. Bernstein analyst Peter Weed noted that the upgrade mainly stems from a reassessment of the demand outlook from AI-native customers. Although Datadog's traditional customer business is showing weakness, and its largest AI customer previously reduced a contract worth approximately $100 million in annualized recurring revenue (ARR), the rapid growth in AI model usage may be sufficient to offset these negative effects. Previously, Bernstein had downgraded Datadog on concerns that its largest AI customer would reduce contract scale and that demand from traditional customers was slowing. The firm believes this largest AI customer may be OpenAI, but this identification is the analyst's judgment rather than official company confirmation. The report noted that approximately 85% of Datadog's revenue still comes from traditional customer groups, and third-quarter related demand indicators were actually weaker than Bernstein had previously expected. According to alternative data tracked by the firm, this trend could create approximately $10 million in additional pressure on fourth-quarter revenue. However, changes in demand from AI-native customers significantly exceeded expectations, prompting Bernstein to reassess the company's growth potential. The firm said that at the end of the second quarter this year, Token usage growth at major AI labs temporarily slowed markedly, raising market concerns that AI spending might gradually approach saturation. But entering the third quarter, with the launch of new-generation AI models, declines in inference service prices, and enterprises accelerating deployment of AI coding tools, Token consumption accelerated again. According to data from platforms such as OpenRouter, enterprise-side Token consumption by Datadog's major AI lab customers grew at least 50% quarter over quarter in the third quarter, with some smaller AI-native customers potentially growing even faster. Among them, Bernstein estimates that Token usage of OpenAI models on the OpenRouter platform surged approximately 300% quarter over quarter in the third quarter. The firm believes that OpenAI's launch of the new Codex model and the sharp decline in pricing for some model services in August were important factors driving usage growth. Bernstein noted that although model price cuts may affect AI companies' own revenue growth, for Datadog what matters more is customers' actual usage, because higher computing and application activity typically means more demand for monitoring, log management, and system performance analytics. At the same time, AI coding tools are becoming the main driver of enterprise Token consumption growth. As developers use AI to handle more complex software development, data analysis, and business tasks, Token consumption per user may continue to increase, creating new growth opportunities for cloud infrastructure software vendors such as Datadog. Accordingly, Bernstein sharply raised its growth forecast for Datadog's AI-native customer business. The firm expects that after accounting for the impact of the largest AI customer reducing contract scale, revenue from the AI-native customer group is still expected to achieve approximately 25% quarter-over-quarter growth in the third quarter, compared with a previous forecast of essentially zero. For the fourth quarter, the firm expects revenue from this customer group to continue growing approximately 25% quarter over quarter, above the previous forecast of approximately 10%. The firm believes that AI demand growth is not only coming from large model developers, but the proliferation of consumer AI agents may also create new opportunities. For example, after AI agent products such as Muse attract a large number of users, demand for monitoring, system stability, and performance optimization from related applications is expected to increase in tandem. However, the firm specifically noted that there is currently no evidence that Muse has already used Datadog's AI monitoring services, so this potential opportunity has not yet been included in the base forecast. In addition, Datadog's traditional customer business may also benefit from the proliferation of AI agents. As consumers increasingly use AI agents to access internet services, shopping platforms, and other applications, the cloud computing load of related enterprises may increase, thereby driving monitoring and data analytics spending by Datadog's existing customers. Bernstein is also optimistic about Datadog's development opportunities in cybersecurity and AI-driven IT operations. The firm believes that as AI coding tools significantly increase code generation speed, the number of software and systems that enterprise cloud operations teams need to manage will also increase, creating more demand for Datadog's Scout AI operations agent and cybersecurity products. It is worth noting that Bernstein has not fully incorporated the aforementioned recovery in traditional customer demand and the potential incremental contribution from new products into its forecast model, but instead views them as additional support for future performance that may exceed expectations. On financial forecasts, Bernstein raised its near-term revenue growth expectations for Datadog by approximately 7 percentage points and extended its judgment on the company's high-growth period. According to the latest model, the firm expects Datadog's 2026 revenue to reach $4.698 billion, further increase to $6.450 billion in 2027, and potentially reach $8.599 billion in 2028. Compared with $3.427 billion in 2025, revenue in 2027 is expected to grow by nearly 88%. On earnings, Bernstein raised its 2026 adjusted earnings per share forecast for Datadog from $2.70 to $2.82, and its 2027 forecast from $3.45 to $4.00. At the same time, the firm expects Datadog's 2027 non-GAAP operating profit to reach approximately $1.730 billion, with the operating margin rising from 24.4% in 2026 to 26.8%, reflecting the operating leverage effect brought by revenue growth. Bernstein noted that its latest revenue growth forecasts are significantly above Wall Street consensus expectations. The forecast comparison chart on page 9 of the research report shows that the firm expects Datadog's revenue growth rate to remain above market expectations for the next three years, with the growth gap reaching nearly 10 percentage points in some quarters. On valuation, Bernstein raised the valuation multiple applied to Datadog's next-twelve-month expected revenue from approximately 16x to 22x and, combined with a discounted cash flow model, raised its price target to $350. Its discounted cash flow model still uses assumptions of an 11% weighted average cost of capital and a 3% terminal growth rate. However, Bernstein also acknowledged that the current forecasts carry certain risks. As large AI companies expand in scale, some customers may shift to developing monitoring tools in-house or adopt lower-cost open-source alternatives. In addition, the proliferation of open-weight models and self-hosted deployment models may also weaken some customers' demand for third-party monitoring services. The firm also cautioned that Token usage growth may not necessarily translate fully into Datadog revenue growth. Customers' continued optimization of software usage efficiency, efforts to obtain larger discounts, and changes in the macroeconomic or regulatory environment could all affect the company's actual performance. Nevertheless, Bernstein believes that the reacceleration of AI-native customer demand, combined with the potential recovery in traditional customer demand and new product commercialization opportunities, is already sufficient to change Datadog's investment outlook. As AI applications gradually expand from model training to enterprise coding, consumer agents, and broader business scenarios, Datadog is expected to become an important beneficiary of the scaling development of AI applications.