How Cloud Computing Works

In 2006, Amazon launched a service called EC2 (Elastic Compute Cloud). It let anyone rent a virtual computer by the hour. At the time, building software meant buying physical servers, finding space for them in a data center, and hoping you’d guessed your capacity needs correctly. Amazon’s idea was radical: why buy hardware at all when you could just pay for computing power as you need it?

That idea became cloud computing. Today, it’s how almost all software is built and run.

The short answer

Cloud computing means renting computing resources (servers, storage, databases, networking) over the internet instead of owning and maintaining them physically. Companies like Amazon (AWS), Microsoft (Azure), and Google (Google Cloud) own massive data centers full of computers. You rent access to those computers, paying only for what you use. This eliminates the need to buy, configure, and maintain your own hardware.

The full picture

What “the cloud” actually means

“The cloud” sounds ethereal, but it’s grounded in reality. Every “cloud” service is running on physical hardware somewhere: thousands of computers in giant data centers, connected by networking equipment, cooled by industrial cooling systems.

When you “store files in the cloud,” they’re actually sitting on someone else’s hard drives. When you “run an app in the cloud,” it’s actually executing on someone else’s processors. These servers communicate over the internet and are often fronted by CDNs to serve content quickly to users around the world. The “cloud” is just a marketing term for computing infrastructure that you access over the internet rather than own yourself.

The key insight is that cloud providers operate at enormous scale. A company like AWS has millions of customers. By spreading the cost of massive data centers across all those customers, they achieve efficiencies no individual company could match. They also handle all the messy infrastructure work: replacing failed hard drives, upgrading networking equipment, managing security patches, so you don’t have to.

The three main cloud service models

Cloud services are typically categorized into three models, depending on how much responsibility the customer takes on:

Infrastructure as a Service (IaaS): You rent virtual machines and storage. You’re responsible for the operating system, the software, and how you configure everything. AWS EC2 is the classic example. This is the closest to owning your own servers, just without the physical hardware.

Platform as a Service (PaaS): You rent a platform where you just deploy your code. The cloud provider handles the operating system, runtime environment, and underlying infrastructure. Heroku and Google App Engine are examples. You focus on writing code; the provider handles everything else.

Software as a Service (SaaS): You use a complete application hosted in the cloud. You just log in and use it. Gmail, Salesforce, and Slack are all SaaS. You have zero responsibility for infrastructure.

Most companies use a mix. They might host their core application on AWS (IaaS), use a managed database service (PaaS), and have their marketing team use a SaaS CRM.

Why cloud computing changed everything

Before cloud computing, launching a new web service required:

• Buying servers (capital expense, weeks of lead time)
• Racking them in a data center
• Configuring networking and security
• Hiring people to maintain the hardware
• Planning capacity for peak traffic, even if most of the time the servers sat idle

Cloud computing eliminated all of this. Need a server? Click a button and have one in 60 seconds. Need 100 servers for a product launch? Scale up in minutes, then scale back down after. Pay only for what you use.

This shift fundamentally changed the economics of starting a company. In 2005, launching a startup required thousands of dollars in hardware. In 2015, you could launch a global web service for almost nothing. This is why the startup boom of the 2010s happened when it did: cloud computing removed the biggest barrier to entry.

The major providers and what they offer

The cloud market is dominated by three companies:

Amazon Web Services (AWS): The pioneer and the largest. AWS offers over 200 different services: compute, storage, databases, machine learning, analytics, robotics, and more. If you can name an IT need, AWS probably has a service for it.

Microsoft Azure: Strong in the enterprise space, especially for companies already using Microsoft products. Azure integrates well with Windows Server, Office 365, and enterprise software. Many large corporations prefer Azure because of its enterprise features and existing relationships.

Google Cloud: Known for strengths in data analytics, machine learning, and containerized computing (Kubernetes, which Google invented). Often the choice for data-heavy applications and companies with strong engineering teams.

Other providers exist, like IBM Cloud, Oracle Cloud, and Alibaba Cloud, but these three control about two-thirds of the market.

Key cloud services explained

Most cloud computing breaks down into a handful of core services:

• Compute: Virtual machines (AWS EC2, Azure VMs, Google Compute Engine). Think of these as computers you can rent.
• Storage: Object storage for files (AWS S3 is the standard). Think of this as an infinitely scalable hard drive.
• Databases: Managed MySQL, PostgreSQL, MongoDB, and others. You don’t need to maintain the database software. The provider handles that.
• Networking: Virtual networks, CDNs, DNS services. The glue that connects everything.
• Serverless: Functions (AWS Lambda, Azure Functions) that run code in response to events without you managing any servers at all. You only pay when the code actually runs.
• Containers and Kubernetes: The modern way to package and run software. A container bundles an application with everything it needs to run (code, libraries, settings) into a single portable unit, like a shipping container that works the same whether it’s on a boat, a train, or a truck. Kubernetes (invented by Google, now the industry standard) is the system that manages and coordinates containers at scale: starting them, stopping them, distributing traffic between them, and restarting them when they crash. The CNCF’s 2024 survey found that 93% of organizations now use or are evaluating Kubernetes. If you’ve ever wondered how a company can deploy 100 copies of the same app and have them all work together seamlessly, Kubernetes is the answer.

The hidden costs and challenges

Cloud computing isn’t free, and it isn’t always cheaper than owning your own infrastructure, especially for very large companies with consistent, predictable workloads.

The main challenge is cost management. Because it’s so easy to spin up resources, it’s also easy to forget about them. Thousands of companies have horror stories about forgetting a development server running for months and receiving a bill for thousands of dollars. Cloud cost optimization has become an entire industry, a field now called FinOps (short for Financial Operations). FinOps teams are dedicated to tracking cloud spending, identifying waste, and negotiating better rates. A 2025 Harness report projected $44.5 billion in cloud infrastructure waste that year alone. The cloud makes it easy to spend; FinOps makes sure you spend wisely.

Security is another consideration. Cloud providers offer robust security, but configuration errors are common. A misconfigured database accessible to the internet has caused multiple major data breaches. Using the cloud securely requires expertise.

There’s also vendor lock-in. Each provider has its own services and APIs. Migrating from AWS to Azure, or from Azure to Google Cloud, is expensive and time-consuming. Many companies stay with their provider simply because switching is hard.

AI cloud services: the new frontier

If cloud computing’s first era was about renting servers, the current era is about renting AI brains. Since 2023, all three major cloud providers have built dedicated platforms for running AI models, and this is now the fastest-growing part of the cloud business. GenAI cloud services grew 140–180% year-over-year in Q2 2025, a pace that far outstrips traditional cloud growth.

The three main players:

AWS Bedrock: Amazon’s managed AI platform gives you access to dozens of AI models (including Anthropic’s Claude, Meta’s Llama, Mistral, and others) through a single API, without managing any infrastructure. You pick the model, send it text, get results back. Bedrock also handles compliance, security, and data privacy, which matters enormously for enterprises. Think of it as a library of AI models you can call on-demand.

Azure OpenAI Service: Microsoft’s partnership with OpenAI gives Azure customers access to GPT-4 and other OpenAI models with enterprise-grade security and compliance baked in. This is why companies that were nervous about sending data to OpenAI’s consumer product use Azure instead: the same models, but inside your own compliant cloud environment.

Google Vertex AI: Google’s unified platform for building and deploying AI models. It integrates tightly with Google’s own Gemini models, but also supports open models. Google’s edge is in data: Vertex AI connects naturally to BigQuery (Google’s data warehouse) and other analytics tools, making it easier to train models on data you already have in Google Cloud.

The reason this matters beyond the hype: AI services are fundamentally changing what it means to “build software.” Features that once required entire data science teams (sentiment analysis, image recognition, text generation) are now API calls available to any developer with a cloud account and a credit card.

GPU clouds: the hardware behind AI

Training and running AI models requires a different kind of computing than traditional web apps. Where normal servers use CPUs (great at a few complex tasks in sequence), AI workloads need GPUs (great at millions of simple calculations in parallel). NVIDIA’s H100 GPU has become the gold standard for AI training, and you can rent access to one from any major cloud provider.

AWS, Azure, and Google Cloud all offer GPU-powered instances specifically for AI workloads. Pricing varies: AWS charges around $3.90 per H100 GPU-hour on-demand, while Google Cloud’s comparable instance runs around $3.00. Spot instances (spare GPU capacity that can be interrupted) cost significantly less but aren’t guaranteed to stay available, a trade-off that works fine for training runs but not for production inference.

Smaller, specialized “GPU cloud” providers like Lambda Labs, CoreWeave, and RunPod have also emerged, often offering H100s at lower rates than the big three. They’ve attracted AI startups who need serious compute but don’t want to pay the hyperscaler premium.

Why it matters

Cloud computing is the foundation of modern software. Whether you’re launching a startup, building an internal tool, or evaluating a tech stack, understanding cloud basics helps you make better decisions.

For businesses, cloud computing turned IT from a capital expense (buy hardware) into an operational expense (pay as you go). This flexibility lets companies experiment without huge upfront risk and scale dramatically when they find product-market fit.

For individuals, almost every app you use runs in the cloud. Your photos, your email, your documents, your streaming services: they’re all stored and served from data centers owned by cloud providers. Understanding what that means (the tradeoffs, the benefits, the privacy implications) helps you evaluate the services you use.

Common misconceptions

“The cloud is more secure than my own servers.” Not inherently. Cloud providers offer powerful security tools, but misconfiguration is common. Many breaches happen because someone left a database publicly accessible. Both self-hosted and cloud-hosted systems can be secure or insecure. It depends on how well they’re configured.

“Cloud computing is always cheaper.” It usually is for startups and companies with variable workloads. But for massive, stable workloads, owning your own infrastructure can be cheaper. The cloud’s advantage is flexibility and reduced upfront cost, not always lower total cost.

“My data is stored in ‘the cloud’, I don’t know where it is.” Actually, you can usually choose. Most providers let you specify which regions your data is stored in, for both performance and regulatory reasons. Data in Germany, for example, is subject to German privacy laws, which are strict.