Meet the AI Stock Running Rings Around Nvidia in 2026. It Could Just Be Getting Started

The Shift from AI Hardware to AI Operations

For the past few years, the narrative surrounding artificial intelligence has been dominated by the “picks and shovels” providers. Nvidia has been the gold standard, supplying the semiconductors that power the AI revolution. However, as the technology matures, a critical shift is occurring. Investors are moving downstream, looking beyond the hardware to the software and platforms that make AI functional, scalable, and profitable for the enterprise.

This transition is evident in the performance of AI-powered observability and security platforms. While hardware giants continue to grow, companies that help businesses manage their AI infrastructure are seeing explosive gains. For instance, while Nvidia has seen a 21% increase in stock value in early 2026, observability leader Datadog has soared 51% in the same period, signaling a growing appetite for the tools that operationalize AI.

Why GPU Monitoring is the New Gold Mine

The biggest challenge for modern enterprises isn’t just deploying AI—it’s proving the return on investment (ROI). Many companies are investing substantial sums into AI, but they often struggle to optimize spending and performance as they scale. This is where GPU monitoring becomes indispensable.

By monitoring the health, cost, and performance of GPUs, businesses can stop treating AI as a “black box” and start treating it as a transparent asset. This capability allows for:

• Faster Troubleshooting: Identifying bottlenecks in hyperscale AI training workloads more quickly.
• Cost Optimization: Reducing wasteful spending on expensive compute resources.
• Performance Scaling: Ensuring that as AI models grow, the underlying infrastructure remains stable.

The market demand for these tools is immense. Recent data shows that the largest technology companies in the world—those building the most advanced AI models—are already utilizing these observability tools to accelerate their pace of innovation.

Analyzing the Numbers: Growth and Scale

The financial trajectory of the observability sector reveals a robust trend. Datadog’s recent first-quarter results outperformed analyst expectations across the board. While consensus estimates predicted revenue of $932 million, the company delivered $1 billion, a 32% year-over-year increase. Adjusted earnings per share (EPS) also climbed 30% to $0.60, beating the $0.51 estimate.

This growth is driven by a surge in high-value enterprise adoption. The number of customers providing annual recurring revenue (ARR) of $100,000 or more rose to 4,550, a 21% increase. This indicates that the platform is not just attracting little trials, but is becoming a core part of the budget for large-scale enterprises.

the financial health of these platforms is strong. With operating cash flow at $335 million and free cash flow rising 18% to $289 million, these companies are generating the liquidity necessary to continue innovating in a fast-paced market.

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The Valuation Dilemma: Growth vs. Value

Despite the impressive growth, the “AI premium” is now being baked into stock prices. For example, Datadog has recently traded at 72 times next year’s expected earnings. For value investors, this multiple may seem prohibitively high, especially after the stock surged 95% in a single month.

However, growth investors argue that the “disruption fear”—the idea that AI would make traditional SaaS companies obsolete—has been debunked. Instead, AI is acting as a catalyst. By helping customers join the AI revolution and achieve a greater ROI, these platforms have shifted from being “at risk” to being “essential.”

Wall Street remains overwhelmingly bullish, with 92% of analysts rating Datadog as a buy or strong buy as of May 2026. The key for investors is determining whether the accelerating growth can justify the premium valuation.

Frequently Asked Questions

What is AI observability?
AI observability refers to the use of tools to monitor the health, performance, and cost of AI infrastructure, specifically the GPUs and workloads used to train and deploy AI models.

How does GPU monitoring improve AI ROI?
It allows businesses to optimize their compute spending and troubleshoot performance issues faster, ensuring that expensive AI investments translate into actual productivity and revenue.

Is Datadog a better investment than Nvidia?
It is not a direct comparison. Nvidia provides the hardware (semiconductors), while Datadog provides the operational software. While Nvidia has higher overall revenue growth, Datadog represents the “downstream” opportunity of AI adoption.