Introduction

Cloud computing has transformed how we build and deploy applications. Understanding cloud fundamentals is essential for modern developers.

The shift to cloud has fundamentally changed the economics of software delivery. Startups can now access enterprise-grade infrastructure on a pay-as-you-go basis, and large organizations can reduce capital expenditure while gaining the ability to scale globally in minutes. For developers, understanding cloud concepts is no longer optional , it's a core competency that influences how you design, deploy, and operate software.

What Is Cloud Computing?

Cloud computing delivers computing services over the internet, including servers, storage, databases, networking, and software on a pay-as-you-go basis.

The "cloud" is essentially a global network of data centers owned and operated by cloud providers. These providers abstract away the physical hardware, allowing you to provision and de-provision resources programmatically through APIs. This elasticity , the ability to scale up during peak traffic and scale down during quiet periods , is one of cloud computing's most powerful economic advantages over traditional on-premises infrastructure.

Resources can be provisioned in seconds through APIs, dashboards, or Infrastructure as Code tools
Pay only for what you use , no upfront capital expenditure on hardware
Cloud providers maintain hardware, security patches, and physical facilities
Global availability zones enable low-latency deployments close to your users worldwide

Service Models

The three main cloud service models are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

Choosing the right service model depends on how much control you need versus how much operational overhead you're willing to manage. IaaS gives you raw virtual machines and networks , maximum control, maximum responsibility. PaaS abstracts the OS and runtime , you focus on your application code. SaaS is fully managed software , you just use it. Most modern cloud architectures mix all three: managed databases (PaaS), containerized workloads (IaaS/PaaS), and third-party APIs (SaaS).

IaaS examples: AWS EC2, Google Compute Engine, Azure VMs
PaaS examples: AWS Elastic Beanstalk, Google App Engine, Heroku, Vercel
SaaS examples: Salesforce, Google Workspace, GitHub, Datadog
FaaS (Function as a Service): AWS Lambda, Google Cloud Functions , serverless compute model

Major Cloud Providers

Leading cloud providers include AWS, Google Cloud Platform, and Microsoft Azure, each offering unique features and services.

AWS holds the largest market share and offers the broadest catalog of services, making it the default choice for most workloads. Google Cloud excels in data analytics, machine learning, and Kubernetes (which Google originally created). Azure is the preferred choice for organizations deeply invested in Microsoft's ecosystem. Multi-cloud strategies are increasingly common for avoiding vendor lock-in and leveraging each provider's strengths.

AWS: Largest service catalog, most mature, strongest community and documentation
GCP: Best-in-class BigQuery, Vertex AI, and Kubernetes Engine (GKE)
Azure: Native integration with Active Directory and Microsoft 365 tools
Smaller providers (DigitalOcean, Hetzner, Fly.io) offer simpler, more affordable options for smaller workloads

Benefits of Cloud Computing

Cloud computing offers cost efficiency, scalability, flexibility, and automatic updates. It eliminates the need for physical infrastructure maintenance.

Beyond the operational benefits, cloud computing enables new architectural patterns that weren't economically feasible with on-premises infrastructure. Serverless functions allow you to run code without managing servers and pay only per invocation. Managed databases handle backups, failover, and scaling automatically. Content delivery networks (CDNs) distribute your assets globally with a few clicks. These capabilities let small teams ship production-grade, globally distributed systems that would have required large operations teams just a decade ago.

Use auto-scaling groups to automatically add/remove capacity based on real-time demand
Leverage spot/preemptible instances for batch workloads to reduce compute costs by up to 90%
Use managed services (RDS, Cloud SQL, DynamoDB) to eliminate database administration overhead
Set up cost alerts and budgets to avoid unexpected cloud bills during traffic spikes