Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) are two of the most widely adopted cloud computing models. They overlap in important ways but serve fundamentally different purposes. Choosing the wrong model for a workload can mean unnecessary operational overhead, higher costs, or slower delivery cycles.
This article defines each model clearly, explains where the boundary between them blurs, offers guidance on when to choose one over the other, and surveys the primary IaaS and PaaS offerings from AWS, Microsoft Azure, and Google Cloud Platform (GCP).
IaaS vs. PaaS, Defined
Infrastructure as a Service (IaaS)
IaaS delivers on-demand access to virtualized IT infrastructure — compute, networking, and storage — over the internet. The cloud provider owns, operates, and maintains the physical hardware. The customer is responsible for everything above the hypervisor: operating systems, middleware, runtimes, and applications.
Key characteristics of IaaS:
- Provides raw virtualized resources (VMs, block storage, virtual networks).
- Maximum flexibility — customers choose and manage their own OS, runtime, and software stack.
- Requires in-house expertise to configure, secure, patch, and monitor workloads.
- Eliminates capital expenditure on physical servers and data-center facilities.
IaaS was the backbone of the first generation of major public clouds. AWS launched its compute and storage IaaS services in the mid-2000s, enabling enterprises to move workloads off on-premises hardware without fundamentally changing how they managed software.
Platform as a Service (PaaS)
PaaS bundles hosting infrastructure with software tools for developing, testing, and deploying applications. Instead of managing an OS or a web server, a developer uploads code and the platform handles provisioning, scaling, and load balancing.
Key characteristics of PaaS:
- Abstracts infrastructure management away from the developer.
- Includes integrated tools for build, test, deployment, and monitoring pipelines.
- Typically opinionated about supported languages, frameworks, or deployment patterns.
- Trades flexibility for speed and simplicity.
PaaS emerged alongside IaaS in the mid-2000s, with vendors such as Heroku offering opinionated, developer-friendly hosting environments. Early PaaS providers operated largely independently from IaaS vendors and tightly coupled their tooling to their own proprietary hosting platforms.
Where the Lines Blur Between IaaS and PaaS
The boundary between IaaS and PaaS has become less sharp over time for two reasons.
Open standards reduced lock-in. Early PaaS platforms were proprietary end-to-end. The rise of Docker containers and Kubernetes as open, portable orchestration layers made it practical to build PaaS-like workflows on top of generic IaaS. Enterprises gained the ability to switch hosting environments without rewriting their entire deployment pipeline.
IaaS providers added PaaS-style services. AWS, Azure, and GCP all expanded from raw infrastructure into managed developer services — CI/CD pipelines, managed databases, serverless compute, and application hosting platforms. A service such as AWS Elastic Beanstalk, for example, runs on EC2 IaaS underneath but presents a PaaS-style interface to the developer.
A practical rule of thumb: if a cloud service provides only virtualized infrastructure resources, it is IaaS. If it couples software tooling and managed workflows with that infrastructure, it is PaaS. Note that IaaS instances do come with a base OS pre-installed, but that minimal software layer does not constitute a full development or deployment platform.
When to Choose IaaS vs. PaaS
Choose IaaS when:
- Your team has the expertise to manage operating systems, middleware, and security patching.
- You need a specific OS version, runtime, or toolchain not supported by a PaaS offering.
- You are migrating an existing, self-contained application (e.g., an on-premises payroll system) that you simply want hosted in the cloud with minimal architectural change.
- You want maximum control over how software is deployed and how resources are allocated.
Choose PaaS when:
- Developer velocity matters more than low-level infrastructure control.
- Your team is smaller and cannot afford dedicated infrastructure operations staff.
- Your application fits within the languages, frameworks, and deployment models the PaaS supports.
- You want a managed, integrated pipeline from code commit to production deployment.
A note on cost
IaaS is generally priced lower on a per-resource basis than PaaS for equivalent compute and storage consumption. However, IaaS requires you to source, license, and operate software tools separately — development environments, CI/CD platforms, monitoring agents — which adds cost and operational overhead. PaaS bundles many of those tools into a single bill and reduces the staffing overhead for infrastructure management. Total cost of ownership depends heavily on team size, workload complexity, and how efficiently resources are utilized.
AWS IaaS and PaaS Offerings
AWS IaaS: Amazon EC2 and Storage Services
Amazon EC2 is AWS’s primary IaaS compute service. It offers a broad range of instance families, including general-purpose, compute-optimized, memory-optimized, and storage-optimized types. EC2 is typically paired with one or more AWS storage services to host an application and its data:
- Amazon S3 — object storage for unstructured data and static assets.
- Amazon Elastic Block Store (EBS) — persistent block storage attached to EC2 instances.
- Amazon Elastic File System (EFS) — managed NFS-compatible file storage shared across instances.
AWS PaaS: A Composable Approach
AWS does not offer a single, bundled PaaS product. Instead, it provides a portfolio of modular services that teams assemble into a development and deployment pipeline suited to their needs. The key services include:
- AWS Cloud9 — a browser-based integrated development environment (IDE) for writing, running, and debugging code.
- AWS CodePipeline — a continuous delivery service for automating release pipelines. It can deploy to EC2 instances, Lambda functions, or containers on Amazon ECS.
- AWS CodeDeploy — automates application deployments to EC2, on-premises servers, Lambda, and ECS.
- AWS Elastic Beanstalk — a higher-level PaaS-style service where developers upload application code and AWS handles environment provisioning, load balancing, auto-scaling, and monitoring. Underlying infrastructure is still EC2 and related services, but exposed through a simplified interface.
The AWS model appeals to organizations that want granular control over which tools they adopt, but it requires teams to integrate multiple services and understand how they interact. It is the most flexible of the three major providers’ PaaS approaches, and correspondingly requires more configuration effort.
Microsoft Azure IaaS and PaaS Offerings
Azure IaaS: Virtual Machines and Storage
Azure Virtual Machines is Microsoft’s primary IaaS compute offering, with instance families covering general-purpose, compute-optimized, memory-optimized, and burstable workloads. Azure’s storage services include:
- Azure Disk Storage — persistent block storage for VM workloads.
- Azure Blob Storage — object storage for unstructured data.
- Azure File Storage — fully managed file shares accessible via SMB and NFS.
- Azure Queue Storage — message queue storage for decoupling application components.
Azure PaaS: App Service
Azure groups its PaaS offerings under Azure App Service, a managed platform for building and hosting web applications, REST APIs, and mobile back ends. App Service supports multiple languages and frameworks and abstracts the underlying infrastructure entirely. Key sub-services within App Service include:
- Azure Web Apps — fully managed hosting for web applications and APIs.
- Azure Web Apps for Containers — deploys containerized applications without managing the orchestration layer directly.
- Azure API Apps — focused hosting and tooling for building and publishing REST APIs.
Among the three major providers, Azure App Service comes closest to a turnkey, end-to-end PaaS. It is a particularly good fit for organizations already in the Microsoft ecosystem — those developing in .NET, using Azure DevOps for CI/CD, or running Windows Server workloads — because the tooling integrates tightly with those environments.
Google Cloud Platform IaaS and PaaS Offerings
GCP IaaS: Compute Engine and Storage
Google Compute Engine is GCP’s IaaS compute service. It provides both predefined machine types and custom machine types, allowing customers to specify exact vCPU and memory configurations. GCP’s storage services include:
- Google Cloud Storage — scalable object storage.
- Google Persistent Disk — block storage for Compute Engine instances.
- Google Cloud Filestore — managed NFS file storage for applications requiring a shared file system.
GCP PaaS: App Engine
Google App Engine is GCP’s managed PaaS platform. Like Azure App Service, it supports multiple languages and frameworks and handles infrastructure provisioning on behalf of the developer. App Engine offers two deployment environments:
- Standard Environment — runs applications in a language-specific sandbox. Best suited for workloads that need rapid, spiky scaling, low-cost idle operation, and a constrained, well-defined runtime.
- Flexible Environment — runs applications in Docker containers on Compute Engine VMs. Suited for steady traffic patterns, custom runtimes, apps with native code dependencies, and workloads that need access to Compute Engine network resources.
The two environments can be combined within a single App Engine application where appropriate.
App Engine is oriented toward Linux-based development and container-native workflows, making it a natural fit for teams that favor Unix tooling, open source frameworks, and Docker-based deployments. Azure App Service, by contrast, is more oriented toward Windows and .NET workflows, though both platforms support multiple operating systems and languages.
Summary Comparison
| Dimension | IaaS | PaaS |
|---|---|---|
| What you manage | OS, middleware, runtime, application | Application code and data |
| What the provider manages | Physical hardware, virtualization | Hardware, OS, middleware, runtime, scaling |
| Flexibility | High | Moderate to low |
| Operational overhead | High | Low to moderate |
| Best for | Teams with strong ops expertise; custom or legacy workloads | Developer-focused teams; rapid app delivery |
| AWS primary service | Amazon EC2 | Elastic Beanstalk, CodePipeline, CodeDeploy |
| Azure primary service | Azure Virtual Machines | Azure App Service |
| GCP primary service | Google Compute Engine | Google App Engine |
The right choice depends on your team’s operational maturity, the specific workload requirements, and how much control versus convenience you need. In practice, many organizations use both models simultaneously — IaaS for workloads that require low-level control and PaaS for internal tools and new application development where speed matters most.