Karl Montevirgen is a professional freelance writer who specializes in the fields of finance, cryptomarkets, content strategy, and the arts. Karl works with several organizations in the equities, futures, physical metals, and blockchain industries. He holds FINRA Series 3 and Series 34 licenses in addition to a dual MFA in critical studies/writing and music composition from the California Institute of the Arts.
Doug is a Chartered Alternative Investment Analyst who spent more than 20 years as a derivatives market maker and asset manager before “reincarnating” as a financial media professional a decade ago.
Before joining Britannica, Doug spent nearly six years managing content marketing projects for a dozen clients, including The Ticker Tape, TD Ameritrade’s market news and financial education site for retail investors. He has been a CAIA charter holder since 2006, and also held a Series 3 license during his years as a derivatives specialist.
Doug previously served as Regional Director for the Chicago region of PRMIA, the Professional Risk Managers’ International Association, and he also served as editor of Intelligent Risk, PRMIA’s quarterly member newsletter. He holds a BS from the University of Illinois at Urbana-Champaign and an MBA from Illinois Institute of Technology, Stuart School of Business.
A hyperscale data center is a massive facility designed to run thousands of servers as part of a distributed computing system. Hyperscalers provide enormous amounts of computing power, data storage, and networking capacity on demand. They underpin much of the cloud computing infrastructure used by businesses and increasingly provide the computing power needed to develop and run artificial intelligence (AI).
How is a hyperscale data center different from a traditional data center?
Traditional data centers typically “scale up” by adding computing power, storage, or other capacity to existing hardware. Hyperscale data centers are designed to “scale out,” adding large numbers of servers that work together as a distributed system. That architecture allows hyperscalers to expand capacity quickly and efficiently as demand for cloud computing, data storage, and AI processing grows.
How do hyperscale data centers power AI?
Artificial intelligence requires enormous computing capacity to train and run large language models and other AI systems. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud operate the largest hyperscale cloud platforms and have invested heavily in AI infrastructure. Their data centers provide the processors, storage, networking, and cloud services that developers and businesses use to build, train, and deploy AI models and applications.
Tech industry insiders have been talking about hyperscale data centers (“hyperscalers”) since the early 2010s, referring to the enormous cloud facilities that deliver computing power at industrial scale. But it wasn’t until the rise of OpenAI’sChatGPT and other large language models (LLMs) in the mid-2020s that the term entered the mainstream. Analysts, company executives, and reporters often point to these data centers as the backbone of tomorrow’s digital economy.
As an investor, you’re probably wondering what hyperscalers are, what services they offer, and how they’re reshaping the future of technology and commerce. Time for a deeper dive.
Key Points
Hyperscale data centers (“hyperscalers”) provide a wide range of cloud computing and data solutions.
Unlike traditional data centers, hyperscalers use distributed computer systems to scale dynamically on demand to handle huge workloads.
Hyperscalers support business services including big data analytics, infrastructure-as-a-service, content delivery networks, and more.
What are hyperscalers?
Hyperscalers are large-scale data centers that provide a wide range of cloud computing and data solutions for businesses that need vast digital infrastructure, processing, and storage.
NVIDIA: The chips that power hyperscalability?
At the heart of NVIDIA’s success are its graphical processing units (GPUs), chips that serve complex, high-end applications including ChatGPT (which was trained on NVIDIA chips). As of 2025, NVIDIA was estimated to hold more than 80% of the market for GPU chips used for AI.
What sets hyperscalers apart from their traditional data center counterparts is not only their size—they’re capable of hosting millions, if not billions, of users with speed and efficiency—but also their capacity to dynamically scale on demand.
What differentiates hyperscalers from traditional data centers?
Hyperscalers use distributed computer systems, teams of independent computers splitting up tasks across multiple machines to process high volumes of data. This is called hyperscale computing. Traditional data centers, on the other hand, prioritize centralized computer systems, which can often mean fewer but more powerful computers.
The big difference is how they adjust their capacity to handle increasing workloads:
Traditional data centers “scale up” by upgrading existing hardware.
Hyperscalers “scale out” by adding more computers.
These methods of scaling make a huge difference when businesses need to grow fast, dramatically increasing their data capacity and workloads. Hyperscalers can usually meet this demand seamlessly, while traditional data centers will often hit a limit due to hardware constraints.
Who are today’s biggest hyperscalers?
The controversial side of hyperscale
Like any technological or industrial advance, hyperscalers face criticism for their resource demands and rapid expansion. These facilities require substantial electricity and water to operate and cool thousands of servers, which some observers say could strain local power grids or raise long-term utility costs for residents and businesses.
Communities have also raised concerns about land use, environmental impacts, and reliance on backup diesel generators. Because cloud infrastructure is concentrated among a few companies, analysts warn of resilience and competition risks, and some question whether AI demand will grow fast enough to justify the record levels of investment pouring into new data center capacity.
The “big three” hyperscalers dominating the cloud infrastructure market are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. These are the companies that provide the most extensive cloud infrastructure solutions worldwide.
What kinds of services do hyperscalers offer?
Among the wide array of services hyperscalers provide, here are a few that stand out:
Infrastructure-as-a-service (IaaS): Virtualized servers, networks, and storage that companies can “rent” from a hyperscaler.
Platform-as-a-service (PaaS): Platforms for developers to build and manage applications without the need for hardware.
Software-as-a-service (SaaS): Some hyperscalers offer computer applications that are accessible via Web browsers.
Content delivery network (CDN): Faster Web content delivery through the use of multiple servers across the globe.
Big data analytics: Tools to process and analyze data to detect patterns, trends, and other data-based insights.
Why do businesses use hyperscale solutions?
Companies are increasingly looking to avoid the burden, costs, and risks of having to set up, manage, maintain, and scale up their own hardware-based infrastructure. This is why cloud computing, in general, has gained traction over the last few decades.
But when it comes to larger operations dealing with immense data volumes and workloads, hyperscalers’ economies of scale dwarf those of traditional data centers, allowing businesses to save on costs and resources while optimizing performance.
AI & your money
Artificial intelligence is changing how we interact online, how we manage our finances, and even how we work. Learn more with Britannica Money.
Hyperscalers may also offer value-added information technology services such as database management, data security, and artificial intelligence (AI) integration, among others. When AI exploded into the mainstream in the early 2020s, hyperscalers became key players in a game that was rapidly changing the nature of work.
How is generative AI affecting hyperscalers?
Hyperscalers like the big three—Amazon, Microsoft, and Alphabet—are ramping up their investment in generative AI technologies:
Amazon is integrating AI into its retail and cloud services for a number of tasks, from customer experience optimization to machine learning and code generation for its business customers.
Microsoft, through its partnership with OpenAI, has integrated ChatGPT into its Bing search engine and Azure ecosystem, allowing users to access a wide suite of AI solutions including workflow automation, coding, customer service, app development, and data analysis.
Alphabet integrated Gemini, its most powerful generative AI model, into business and enterprise-level workspaces. Its multimodal capabilities—processing text, images, audio, and video across multiple platforms—can enable businesses to streamline and automate a wide range of complex tasks.
The widespread adoption of AI offerings from these firms—and other hyperscalers such as IBM (IBM) and Oracle (ORCL)—suggests that companies leveraging this new technology may be gaining a competitive advantage. The assumption among companies—and those who analyze and invest in them—is that the more advanced a company’s AI tools, the more competitive its production potential will be.
Hence, hyperscalers that are able to provide more advanced AI tools might not only attract more commercial users, but may also provide them with a significant technological edge over their competitors.
Britannica interactive: Data centers for AI
Data centers: Putting the “hyper” in hyperscale.
Elijah Tan / Encyclopædia Britannica, Inc.
The bottom line
Hyperscalers are the big players in the global digital infrastructure. Hyperscale data centers are central to the growth of cloud computing and AI, even as their expansion raises questions about energy use, infrastructure planning, and long-term sustainability.
From an investing standpoint, it should be noted that the top hyperscalers (particularly Amazon, Microsoft, and Alphabet) are already some of the largest companies on the planet. Their market capitalizations have seen decades of growth, driven not only by their cloud services (and other products and services), but also by investor expectations of more innovation in the future.
In prior eras, a company or industry would be priced for growth, then slowly fade to value as it matured. Today’s hyperscalers, however, can boast price-to-earnings (P/E) ratios that are double and even triple those of a typical value stock. In other words, continued growth and innovation is already baked into share prices, which makes investing in hyperscalers a double-edged sword of sorts.
Specific companies and funds are mentioned in this article for educational purposes only and not as an endorsement.
This article was most recently revised and updated by Karl MontevirgenThis article was most recently revised and updated by Doug Ashburn
