Cloud computing didn’t appear overnight. It grew out of earlier ideas about sharing computing power, connecting machines over long distances, and delivering software through the internet. Over time, those ideas evolved into the large-scale cloud platforms we use today.
Before modern cloud computing existed, organizations relied on large mainframe computers and, later, grid computing systems.
Mainframes (from the 1960s onward) allowed many users to connect to a single powerful computer at the same time. Users typically worked on “dumb terminals” that depended entirely on the central machine. This was an early example of shared computing resources.
Grid computing (1990s–early 2000s) connected clusters of computers across multiple locations so they could work together on massive problems. These grids were especially useful for:
The idea of pooling distributed resources in grid computing directly influenced how cloud computing would later be designed.
As the internet matured, companies realized they could provide computing resources over the web instead of forcing every business to buy and maintain its own servers.
In 1999, Salesforce helped popularize the idea of Software-as-a-Service (SaaS) by delivering customer relationship management (CRM) software through a web browser. Customers didn’t have to install software locally or manage their own infrastructure.
In the early 2000s, Amazon began turning its internal infrastructure into products. In 2002, Amazon Web Services (AWS) launched some early tools, and in 2006, AWS released Elastic Compute Cloud (EC2). EC2 allowed users to rent virtual machines on demand, marking the beginning of widely used Infrastructure-as-a-Service (IaaS).
This shift was revolutionary: instead of buying physical servers, businesses could pay only for the computing power and storage they needed.
After AWS proved that cloud computing could work at scale, other major technology companies entered the market.
Google Cloud Platform (GCP) grew out of Google’s experience running one of the world’s largest search engines. Google offered cloud storage, virtual machines, and powerful analytics tools built on its internal technologies.
Microsoft Azure officially launched around 2010. It quickly became popular with organizations already using Windows Server, SQL Server, and Active Directory. Azure made it easier for traditional businesses to move their existing applications into the cloud.
Other companies, such as IBM and Oracle, also introduced cloud platforms focused on large enterprises with specific performance, compliance, and database needs.
As competition increased, cloud providers expanded far beyond simple virtual machines and storage. They introduced new service models and tools to simplify development and operations.
Platform-as-a-Service (PaaS) offerings gave developers ready-made environments for building and deploying applications without managing the underlying servers. This reduced the complexity of setting up databases, runtimes, and web servers.
Function-as-a-Service (FaaS), also known as serverless computing, went even further. Services like AWS Lambda (introduced in 2014) let developers run small pieces of code in response to events, without provisioning or managing any servers.
Cloud providers also launched:
By the late 2010s, many everyday services—such as streaming video, social media, online gaming, and mobile apps—were heavily dependent on cloud infrastructure.
Today, cloud computing is the backbone of much of the internet and modern business.
Many organizations use a multi-cloud strategy, combining services from multiple providers (for example, AWS and Azure together) to increase reliability, avoid vendor lock-in, and take advantage of each platform’s strengths.
Hybrid cloud models are also common. In a hybrid setup, a company might keep some sensitive systems on-premises in its own data centers while using public cloud services for other workloads. This approach balances control, security, and flexibility.
Edge computing has emerged as another important trend. Instead of running all processing in a distant data center, some workloads run closer to users or devices to reduce latency. This is especially important for Internet of Things (IoT) devices, real-time applications, and autonomous systems.
Finally, cloud providers now offer powerful environments for artificial intelligence and machine learning, including access to specialized hardware like GPUs and TPUs. These tools make it possible to train and deploy advanced models at a scale that would be difficult for most organizations to achieve on their own.
From mainframes and grid computing to global-scale cloud platforms, the history of cloud service providers shows a steady move toward more shared, flexible, and on-demand computing. That evolution continues today as new technologies and services are built on top of the cloud.
