SaaS Apocoplyse Survivor? Why Datadog Could Be a Real AI Winner

Over the past few months, many investors have likely encountered the phenomenon known as the “SaaS Apocalypse." This describes a trend of software-as-a-service (SaaS) stocks seeing their share prices tank as a result of new artificial intelligence (AI) tools.

To an extent, it seems as though markets are selling every stock with even a SaaS-adjacent business model. However, the effect that AI disruption will have on each SaaS company is far from uniform.

This dynamic can lead to opportunities in certain SaaS stocks poised to benefit from AI adoption, rather than having it replace them.

One tech stock that may very well fit this description is Datadog (NASDAQ: DDOG). While shares have recovered from recent lows, the stock is still down around 10% in 2026, and down nearly 40% from its 52-week high.

But 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”

One of the big promises of AI is the idea that AI agents will be able to act autonomously within enterprise workloads.

The theory is that the implementation of agents will allow companies to significantly reduce their costs by performing tasks that previously required expensive SaaS products to complete. This is one of the primary reasons that incumbent SaaS companies have seen their shares fall so dramatically.

Additionally, some argue that one highly competent employee armed with AI agents could perform the work of five people, leading to lower headcount and, in turn, lower costs. This is one of the value propositions that AI developers like OpenAI, Anthropic, and Google's parent company Alphabet (NASDAQ: GOOGL) are touting. Their argument is: pay us to deploy your AI agents, and you’ll save money because you need fewer employees.

However, many know that AI is far from perfect and can make mistakes. This shows up when people simply use consumer-facing chatbots, creating distrust in AI models. Inside an organization, the downside of errors can be larger, including customer impact, revenue leakage, and operational disruption. Thus, businesses are unlikely to adopt AI agents at scale without first building trust over time and being able to quickly diagnose failures when they happen. This is one area where observability vendors argue they can help.

Outsourcing Thinking: AI Agents Increase the Need for Observability

Datadog sells observability software. It collects data via companies' applications, whether used internally or by customers. Through this, companies can detect problems, identify root causes, and resolve incidents.

A large part of the argument for Datadog is that while deploying AI agents could reduce costs compared to humans, they also introduce complexity and generate far more data.

A video on Datadog’s AI Agent Monitoring tool illustrates this well. The speaker discusses a fictional personal finance app called Budget Guru, and a user asks the AI agents powering Budget Guru to perform a simple task: buy $500 of a stock, and remind them of their overdraft fee.

A human could complete this task in just a few clicks. Additionally, they would perform the thinking required to execute it internally. However, Budget Guru had to coordinate with five separate AI agents to execute this task—essentially outsourcing the thinking that a human would have performed. In doing so, they create a mountain of observable data on how they reached their conclusion.

AI agents create data that would not exist if a human performed the same task in the form of logs, traces, and events. As the number of moving parts grows, so do the potential failure points. In that framework, AI agents don’t eliminate the need for monitoring; they may raise the bar for it.

This should create a greater need for observability platforms like Datadog, turning dispersion risk into an opportunity.

Datadog: Impressive Growth, Profitability, and Analyst Support

In its latest quarter, Datadog’s revenues grew by a strong clip of 29% to $953 million. The company also generated free cash flow (FCF) of $291 million, resulting in a FCF margin of approximately 31%.

The Rule of 40 is a key metric for evaluating SaaS companies. It combines revenue growth and profit margins to assess how well a company balances growth and profitability. With scores above 40 considered healthy, Datadog comes in well-positioned at 60.

Notably, Wall Street analysts see considerable potential in Datadog. The MarketBeat consensus price target sits near $180, implying more than 40% upside. When looking at price targets that were updated after the company’s latest earnings report, the avergage is moderately lower at near $174.

Overall, with strong growth, profitability, analyst backing, and potential agentic AI tailwinds, there is real reason to believe that DDOG could defy the "SaaS Apocalypse."

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The article "SaaS Apocoplyse Survivor? Why Datadog Could Be a Real AI Winner" first appeared on MarketBeat.