Microsoft Azure is a public cloud platform that rents compute, storage, databases, and hundreds of managed services on demand. Microsoft announced it as Windows Azure on 27 October 2008 and made it generally available on 1 February 2010. It grew from a Windows-centric experiment into one of the three dominant global clouds, and it now hosts a large share of the world’s enterprise AI workloads.

Aerial view of a dark printed circuit board with a network of glowing red traces fanning out across the surface, evoking the interconnected data centers and services that make up the Azure cloud.
Azure turns racks of physical hardware into a single programmable surface, much like the interconnected traces on this board.

What it was

Before public clouds, building a serious application meant buying servers, racking them in a room, cooling them, and maintaining them for years. You paid for peak capacity even when traffic was low. A startup could wait weeks for hardware to arrive.

Azure removed that burden. Microsoft built enormous data centers around the world and rented their capacity over the internet. You ask for a virtual machine, a database, or a storage account, and it appears in minutes. You pay for what you use and release it when you are done.

Think of it like renting an apartment instead of building a house. You do not pour the foundation, wire the electricity, or fix the plumbing. You move in, use the space, and pay monthly. The landlord handles the building. Azure is the landlord for computing.

At launch, Azure focused on platform services. You deployed code into managed roles and let Microsoft handle the underlying machines. Over time it added raw virtual machines, container services, serverless functions, managed databases, and a wide catalog of higher-level tools. It also integrated tightly with the Windows and Active Directory tools that enterprises already ran.

Apps and AI
Azure OpenAIAzure MLCognitive ServicesManaged AI and application services
Data
SQL DatabaseCosmos DBBlob Storage
Compute
Virtual MachinesFunctionsKubernetes Service
Foundation
Global regionsNetworkingEntra ID identity

Why it mattered

Azure mattered because it brought Microsoft, the company that ran most of the world’s business desktops and servers, into the cloud era. Amazon Web Services had a head start from 2006. Azure gave large enterprises a credible second option from a vendor they already trusted.

That trust was the key advantage. Banks, hospitals, governments, and manufacturers already ran Windows Server, SQL Server, and Active Directory. Azure let them extend those systems into the cloud without abandoning their existing tools and contracts. Hybrid setups, part on-premises and part in Azure, became a practical migration path.

Azure also helped turn computing into a utility. Capacity became something you turn on and off like electricity, billed by the hour or the second. This shifted technology spending from large upfront purchases to ongoing operating costs, and it let small teams rent the same scale of infrastructure that once belonged only to giants.

The platform reshaped Microsoft itself. Under Satya Nadella, who became chief executive in 2014, the company reorganized around the cloud. Azure and its subscription services grew into a major pillar of Microsoft’s revenue and pulled the wider industry further toward the cloud model.

How it connects to AI today

Azure is one of the central places where modern AI gets built and run. Microsoft’s partnership with OpenAI, backed by a multi-year, multi-billion-euro investment, made Azure the cloud home for much of OpenAI’s training and inference. The two companies are deeply intertwined, and that link runs through Azure’s infrastructure.

A builder meets this directly through Azure OpenAI Service. It exposes models such as GPT inside a company’s own Azure boundary, with enterprise controls for identity, data residency, and compliance. A bank that cannot send customer data to a public endpoint can still call a powerful language model through Azure, governed by its existing security policies. That governed access is why many regulated organizations adopt AI through Azure first.

The connection goes deeper than one service. Azure Machine Learning trains, tracks, and deploys custom models. Azure AI Search powers retrieval-augmented generation, the pattern where a model answers using your private documents. Azure builds and rents GPU clusters for the heavy compute that training large models demands. Microsoft also weaves these capabilities into its products, so Copilot features across GitHub, Office, and Windows run on Azure underneath.

For a developer in 2026, the practical path often looks like this. You provision a model deployment in Azure OpenAI, store your documents in Azure storage, index them with Azure AI Search, and wire it together with Azure Functions. The cloud that started as a place to host Windows applications is now a full stack for shipping AI products, from raw GPUs to packaged model APIs.

Still in use today

Azure is active and growing. It is one of the three dominant public clouds alongside Amazon Web Services and Google Cloud, and Microsoft expands its data center footprint and service catalog continuously. Far from legacy, it sits at the center of the company’s strategy.

Nothing replaced Azure because the public cloud model it helped popularize is still ascending. Organizations keep moving workloads off their own hardware, and AI has accelerated that shift by demanding GPU capacity that few companies can build themselves. Renting that capacity from a hyperscale cloud is often the only realistic option.

Azure persists for the same reasons it succeeded. It meets large enterprises where they already are, inside the Microsoft ecosystem of identity, productivity, and developer tools. The original Windows Azure branding is gone, and many early platform services have evolved or retired, but the platform itself is more important now than at any point in its history. It is the place where a great deal of the world’s business software, and increasingly its AI, runs every day.

Further reading