Datadog upgrade underscores observability role

Wall Street was reacting to a stock call, but the real story sits underneath it: Guggenheim upgraded Datadog to Buy on April 9 and set a $175 price target, arguing the market is underestimating how much artificial intelligence work is feeding demand for monitoring software. (finance.yahoo.com) Datadog sells observability software, which is the dashboard layer companies use to watch apps, servers, databases, and networks in real time. Its platform collects the digital exhaust of software systems: metrics, logs, and traces, which are the machine records that show what ran, where it ran, and what broke. (docs.datadoghq.com) Artificial intelligence pushes that exhaust way up because large language model systems create more requests, more model calls, more failures to inspect, and more cost to track. Datadog now has a product called large language model observability for tracing and securing those model-based applications, which shows how directly the company is aiming at that workload. (docs.datadoghq.com) The company’s own numbers show the scale shift already happening. In its February 10, 2026 earnings call, Datadog said more than 5,500 customers use its artificial intelligence integrations, about 650 customers are “AI-native,” and 14 of the top 20 AI-native companies are customers. (fool.com) That same call showed why investors care about platform breadth, not just one monitoring widget. Datadog said 84% of customers use two or more products, 55% use four or more, and 9% use ten or more, which means a customer that starts with infrastructure monitoring can end up buying logs, security, user monitoring, and incident tools from the same vendor. (fool.com) By the end of 2025, Datadog had about 32,700 customers, and roughly 4,310 of them were spending at least $100,000 a year. Revenue for the fourth quarter reached $953 million, up 29% year over year, while bookings rose 37% to $1.63 billion. (fool.com) The compliance angle is what makes this more than an ordinary “cloud software is back” trade. Datadog’s Audit Trail product records user activity, application programming interface requests, and resource changes so teams can see who changed a dashboard, who altered a log pipeline, or who updated access settings. (docs.datadoghq.com) That turns observability data into something closer to evidence. Datadog says Audit Trail can be used for compliance checks, can archive records to Amazon Simple Storage Service, Google Cloud Storage, or Microsoft Azure Storage, and can support reporting tied to rules such as Health Insurance Portability and Accountability Act, Payment Card Industry Data Security Standard, Sarbanes-Oxley, and General Data Protection Regulation. (docs.datadoghq.com; datadoghq.com) Guggenheim’s note leaned on that widening role indirectly by focusing on Datadog’s backend architecture and multi-product setup. The firm said consensus is underpricing Datadog’s ability to grow beyond its largest customer, OpenAI, and projected 27% revenue growth for 2026 versus the Street at 20%. (finance.yahoo.com) The reason that gap exists is simple: if artificial intelligence systems keep multiplying the amount of telemetry companies generate, the winners may not just be the model makers. The companies that capture the records, store the changes, and show auditors and engineers exactly what happened can end up sitting in the middle of both operations and control. (finance.yahoo.com; docs.datadoghq.com)