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Bonus News from MarketBeat SaaS Apocoplyse Survivor? Why Datadog Could Be a Real AI WinnerSubmitted by Leo Miller. Article Posted: 3/26/2026. 
Key Points - The so-called "SaaS Apocalypse" has resulted in somewhat indiscriminate, leading to opportunities and value traps.
- As AI proliferates, Datadog could be a big beneficiary, yet shares remain down almost 40% from their highs.
- Despite the stock’s year-to-date decline, analysts see DDOG rising well above the current share price.
- Special Report: Elon Musk: This Could Turn $100 into $100,000
Over the past few months, many investors have likely encountered the phenomenon known as the “SaaS Apocalypse.” This describes a trend in which software-as-a-service (SaaS) stocks have plunged amid the rise of new artificial intelligence (AI) tools. To some extent, it seems markets are selling off nearly every stock with a SaaS-adjacent business model. However, the effect of AI disruption will not be uniform across all SaaS companies. After nearly five decades on Wall Street, Louis Navellier says a major currency shift is already underway - and the wealthiest Americans, including Musk, Zuckerberg, and Ellison, are quietly moving money out of dollars and into a different type of asset entirely. It's not bitcoin or any other crypto. Navellier has identified 7 companies he believes are positioned at the center of this trend - the last time he spotted a setup like this, Nvidia climbed as high as 10,000%. Watch Navellier's urgent briefing and get all 7 company names That uneven impact creates opportunities in certain SaaS names poised to benefit from AI adoption rather than being replaced by it. One tech stock that may fit this description is Datadog (NASDAQ: DDOG). While shares have recovered from recent lows, the stock is still down roughly 10% in 2026 and nearly 40% from its 52-week high. Some investors believe the market may be misreading what Datadog’s role could look like in an AI-heavy enterprise environment. Understanding the Drivers Behind the “SaaS Apocalypse” A major promise of AI is that AI agents will be able to act autonomously within enterprise workloads. The theory is that agents will let companies cut costs by performing tasks that previously required expensive SaaS products. This expectation is one reason incumbent SaaS companies have seen their shares fall so dramatically. Some proponents also argue that one highly competent employee armed with AI agents could do the work of five people, enabling headcount reductions and lower costs. AI developers such as OpenAI, Anthropic and Google's parent company Alphabet (NASDAQ: GOOGL) pitch this value proposition: pay us to deploy your AI agents, and you’ll save money because you'll need fewer employees. But AI models are far from perfect and can make mistakes. That shows up even with consumer-facing chatbots and can create distrust in AI systems. Inside organizations, errors can have larger consequences—customer impact, revenue leakage, and operational disruption. Businesses are therefore unlikely to adopt AI agents at scale without first building trust and having fast ways to diagnose failures. This is where observability vendors argue they can help. Outsourcing Thinking: AI Agents Increase the Need for Observability Datadog sells observability software that collects data from companies' applications—whether used internally or by customers. That data helps organizations detect problems, identify root causes, and resolve incidents. Part of the bullish case for Datadog is that deploying AI agents could reduce labor costs but also introduce more complexity and generate substantially more data. A video on Datadog’s AI Agent Monitoring tool illustrates this. The speaker describes a fictional personal finance app called Budget Guru, where a user asks the app’s AI agents to perform a simple task: buy $500 of a stock and remind them of an overdraft fee. A human could complete that task in a few clicks, performing the necessary thinking internally. Budget Guru, however, coordinates five separate AI agents to execute the same task—essentially outsourcing the thinking a human would have done. In the process, it generates a large volume of observable data about how the agents reached their conclusion. AI agents create logs, traces, and events that would not exist if a human handled the task. As the number of moving parts grows, so do potential failure points. In that context, AI agents don’t eliminate the need for monitoring; they may raise the bar for it. That dynamic should increase demand for observability platforms like Datadog, turning dispersion risk into opportunity. Datadog: Impressive Growth, Profitability, and Analyst Support In its latest quarter, Datadog’s revenues grew 29% to $953 million. The company also generated free cash flow (FCF) of $291 million, yielding an FCF margin of roughly 31%. The Rule of 40 is a common metric for evaluating SaaS companies, combining revenue growth and profitability. Scores above 40 are generally considered healthy; Datadog’s score sits at about 60. Wall Street analysts see considerable potential in Datadog. The MarketBeat consensus price target is near $180, implying more than 40% upside. Price targets updated after the company’s latest earnings report put the average slightly lower, at about $174. Overall, with strong growth, profitability, analyst backing and potential agentic AI tailwinds, there is reason to believe Datadog could defy the “SaaS Apocalypse.” | 
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