What Is Cloud Computing: IaaS, PaaS, and SaaS Explained

What Is Cloud Computing?

Cloud computing is the delivery of computing services — servers, storage, databases, networking, software, analytics, and intelligence — over the internet ("the cloud") on a pay-as-you-go basis. Instead of buying, owning, and maintaining physical data centers and servers, organizations can rent computing resources from cloud providers and scale them up or down as needed, paying only for what they use.

Before cloud computing, organizations had to purchase and maintain physical servers, often over-provisioning capacity to handle peak demand and wasting resources during normal operation. Cloud computing changed this model fundamentally: infrastructure became a utility, like electricity, that could be consumed on demand without large upfront capital investment. This shift has democratized access to enterprise-grade computing, enabling startups to deploy global infrastructure overnight and established enterprises to move faster and innovate at lower risk.

The National Institute of Standards and Technology (NIST) defines cloud computing through five essential characteristics: on-demand self-service (users can provision resources without human interaction with the provider), broad network access (available over the network from any device), resource pooling (serving multiple customers from shared physical resources), rapid elasticity (scaling up or down quickly), and measured service (pay for what you use). Cloud services are also categorized by deployment model — public cloud, private cloud, hybrid cloud, and multi-cloud — and by service model (IaaS, PaaS, SaaS).

Infrastructure as a Service (IaaS)

Infrastructure as a Service (IaaS) is the most fundamental layer of cloud services, providing virtualized computing resources — servers, storage, networking — over the internet. With IaaS, the cloud provider manages the physical hardware (servers, storage arrays, networking equipment, data centers), while the customer manages everything above: operating systems, middleware, applications, and data. IaaS gives customers the most control and flexibility among cloud service models.

Amazon Web Services (AWS) EC2 (Elastic Compute Cloud), Microsoft Azure Virtual Machines, and Google Compute Engine are the leading IaaS offerings. With EC2, for example, a customer can spin up a virtual server (instance) running their chosen operating system in minutes, select the compute and memory configuration they need, and terminate it when done, paying only for the hours the instance ran. They can add storage volumes, configure networking, and manage security groups — the same capabilities as owning physical servers without the capital expense or management overhead.

IaaS is suited for workloads requiring significant control over the computing environment: complex enterprise applications, specialized software requiring specific OS configurations, high-performance computing (HPC) workloads, big data processing, and disaster recovery. Organizations that have invested in developing infrastructure automation and DevOps capabilities get the most from IaaS, as raw infrastructure requires significant operational expertise to manage effectively.

Platform as a Service (PaaS)

Platform as a Service (PaaS) provides a development and deployment environment in the cloud, with resources that enable developers to build applications without managing the underlying infrastructure. PaaS providers manage servers, storage, networking, operating systems, middleware, and runtime environments; customers manage only their applications and data. This frees developers to focus on writing code rather than managing infrastructure.

AWS Elastic Beanstalk, Google App Engine, Heroku, and Microsoft Azure App Service are popular PaaS offerings. A developer using Heroku, for example, can deploy a web application by pushing code to a Git repository — Heroku automatically handles provisioning servers, installing the runtime, configuring load balancers, and scaling the application based on traffic. The developer never needs to think about the underlying infrastructure.

PaaS is particularly valuable for development teams that want to move fast, for organizations building cloud-native applications that take advantage of managed databases, messaging queues, and caching services, and for projects where operational simplicity is prioritized over fine-grained infrastructure control. Managed database services (AWS RDS, Azure SQL Database, Google Cloud SQL) are an important PaaS category — they handle database installation, patching, backups, and replication, allowing development teams to use databases without database administration expertise.

Software as a Service (SaaS)

Software as a Service (SaaS) delivers complete software applications over the internet, typically on a subscription basis. Users access the software through a web browser without installing anything; the provider manages everything — infrastructure, platform, application, and data storage. SaaS is the cloud service model most familiar to everyday users.

Gmail, Google Workspace, Microsoft 365, Salesforce, Slack, Zoom, Dropbox, and Netflix are all SaaS applications. A company adopting Salesforce for customer relationship management, for example, pays a monthly subscription and accesses the CRM through a web browser — Salesforce handles all servers, software updates, security patching, backups, and scaling. The customer configures the application to their needs but has no access to or responsibility for the underlying infrastructure or software maintenance.

SaaS provides the lowest operational burden for customers and the fastest time-to-value — organizations can start using enterprise-grade software immediately without infrastructure investment or technical expertise. The tradeoff is limited customization and less control. SaaS providers dictate upgrade schedules, and customers must work within the application's constraints. Data portability (the ability to export your data if you switch providers) is a recurring concern with SaaS, as vendor lock-in can be significant.

The Cloud Service Responsibility Model

A useful way to understand the three service models is through the shared responsibility model — which party is responsible for securing and managing each layer of the cloud stack. In IaaS, the provider secures physical infrastructure and hypervisor; the customer is responsible for everything from the OS up: patching operating systems, configuring firewalls, managing identity and access, securing applications, and protecting data. In PaaS, the provider extends their responsibility to include the OS, runtime, and middleware; the customer is responsible for applications and data. In SaaS, the provider manages everything except account access and data configuration; the customer is responsible only for securing their accounts and managing what data they store.

This shared responsibility model is critical for security. A common source of cloud security breaches is the misunderstanding of who is responsible for which security controls. Organizations that assume their SaaS or IaaS provider handles all security — rather than understanding their own security obligations under the model — are vulnerable. The 2019 Capital One data breach, for example, exploited a misconfigured AWS web application firewall — a customer-managed IaaS control, not a failure of AWS's infrastructure security.

Cloud Deployment Models

Public cloud services are delivered over the public internet and shared across multiple customers. AWS, Azure, and Google Cloud are public cloud providers. Resources are shared but logically isolated between customers through virtualization and strong access controls. Public clouds offer the greatest scalability, the widest service catalog, and typically the lowest cost for most workloads, but they require trusting the cloud provider with data and surrendering some control over the infrastructure environment.

Private cloud refers to cloud infrastructure dedicated to a single organization, either operated on-premises or hosted by a provider. Private clouds offer greater control, customization, and data sovereignty but require significant upfront investment in hardware and specialized expertise. Many highly regulated industries (banking, healthcare, government) have initially favored private clouds for sensitive workloads, though public cloud providers have substantially improved their compliance certifications and data sovereignty options.

Hybrid cloud combines public and private cloud environments, connected by technology that allows data and applications to move between them. Organizations use hybrid cloud to run sensitive workloads on private infrastructure while leveraging public cloud for variable or less sensitive workloads, achieving a balance between control and scalability. Multi-cloud — using services from multiple public cloud providers simultaneously — has become common as organizations seek to avoid vendor lock-in, optimize for the best services from each provider, and build resilience against provider outages.

The Business Case for Cloud Computing

Cloud computing's business advantages have driven its widespread adoption. Capital expenditure (CapEx) is replaced by operational expenditure (OpEx) — organizations pay monthly for what they use rather than making large upfront investments in hardware that depreciates over time. Variable cost structures align IT spending with actual usage, allowing organizations to scale efficiently.

Time to market accelerates dramatically: provisioning new infrastructure takes minutes in the cloud rather than weeks or months of hardware procurement, installation, and configuration. Development and testing environments can be created and destroyed on demand, reducing the time and cost of software development cycles. Global deployment — launching applications in multiple regions worldwide — becomes accessible to organizations of any size rather than requiring massive multinational infrastructure investment.

The cloud has also enabled entirely new categories of services. The massive computing power required for machine learning model training, genomics research, financial modeling, and weather simulation is available to any organization that can pay for it per hour — capabilities that would have required hundreds of millions of dollars of dedicated infrastructure a decade ago. This democratization of high-performance computing has accelerated innovation across every industry.