According to a research report released on October 19, Bernstein upgraded cloud monitoring and security software company Datadog (DDOG.US) to Outperform and significantly raised its price target from $237 to $350.
The firm believes that the computing power usage demand from artificial intelligence (AI) enterprises is growing far faster than previously expected, which is likely to drive sustained high-speed revenue growth for Datadog over the coming years. Based on the closing price of $247.11 on October 16, the new price target implies approximately 42% potential upside for the stock.
Bernstein analyst Peter Weed noted that the upgrade primarily stems from a reassessment of the demand outlook from AI-native customers. Although Datadog's traditional customer business has been weak, 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 factors.
Previously, Bernstein had downgraded Datadog due to concerns about its largest AI customer scaling back contract size and slowing demand from traditional customers. The firm believes this largest AI customer may be OpenAI, though this identification is an analyst judgment rather than officially confirmed by the company.
The research report notes 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 have significantly exceeded expectations, prompting Bernstein to reassess the company's growth potential. The firm stated 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, declining 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 from 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 OpenAI model Token usage 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 significant price reductions for some model services in August were important factors driving usage growth.
Bernstein points out 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.
Meanwhile, AI coding tools are becoming a major 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 providers like Datadog.
Bernstein accordingly significantly raised its growth forecast for Datadog's AI-native customer business. The firm expects that after accounting for the impact of its largest AI customer reducing contract size, revenue from the AI-native customer group is still expected to achieve approximately 25% quarter-over-quarter growth in the third quarter, compared to 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 comes not only from large model developers, but the proliferation of consumer AI agents may also create new opportunities. For example, after AI agent products like Muse attract large numbers of users, demand for monitoring, system stability, and performance optimization from related applications is expected to increase in tandem. However, the firm specifically notes that there is currently no evidence that Muse has used Datadog's AI monitoring services, so this potential opportunity has not yet been incorporated into base forecasts.
Additionally, 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, related enterprises' cloud computing loads may increase, thereby driving monitoring and data analytics spending from Datadog's existing customers.
Bernstein is also bullish on 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.
Notably, Bernstein has not yet fully incorporated the aforementioned potential incremental gains from traditional customer demand recovery and new products into its forecast model, instead viewing them as additional support for future earnings that could exceed expectations.
On the financial forecast front, Bernstein raised Datadog's near-term revenue growth expectations by approximately 7 percentage points and extended its assessment of the company's high-growth period. According to the latest model, the firm expects Datadog's revenue to reach $4.698 billion in 2026, further increase to $6.450 billion in 2027, and potentially reach $8.599 billion in 2028. Compared to $3.427 billion in 2025, revenue in 2027 is projected to grow nearly 88%.
On the earnings side, Bernstein raised Datadog's 2026 adjusted earnings per share forecast from $2.70 to $2.82, and its 2027 forecast from $3.45 to $4.00. Meanwhile, the firm expects Datadog's 2027 non-GAAP operating income to reach approximately $1.730 billion, with operating margin rising from 24.4% in 2026 to 26.8%, reflecting the operating leverage effect from revenue growth.
Bernstein points out 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 consistently exceed market expectations over 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 the price target to $350. Its discounted cash flow model still assumes an 11% weighted average cost of capital and a 3% terminal growth rate.
However, Bernstein also acknowledges that there are certain risks to its current forecasts. As large AI companies scale up, some customers may shift to developing monitoring tools in-house or adopt lower-cost open-source alternatives. Additionally, 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 cautions that Token usage growth does not necessarily translate fully into Datadog revenue growth. Customers continuously optimizing software usage efficiency, negotiating larger discounts, and changes in the macroeconomic or regulatory environment could all affect the company's actual performance.
Nevertheless, Bernstein believes that the re-acceleration of AI-native customer demand, combined with potential demand recovery from traditional customers and new product commercialization opportunities, is sufficient to change Datadog's investment outlook. As AI applications gradually expand from model training to enterprise coding, consumer agents, and broader commercial scenarios, Datadog is positioned to become an important beneficiary of the AI application scaling process.
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