SaaS vs PaaS vs IaaS comes down to one question: how much of the technology stack do you want to manage yourself? The more you hand to a provider, the faster you move and the less you control.
All three are large and growing. Gartner forecast 2025 worldwide spending of $299.1 billion on SaaS, $208.6 billion on PaaS and $211.9 billion on IaaS, with IaaS growing fastest at about 25% a year (Gartner, Nov 2024). Most organizations use all three at once.
This guide explains each model, where containers and serverless fit, who is responsible for what, and how to choose the right model for each workload.
Understanding the Basics
There are three most popular methods of cloud deployment, and they are:
- IaaS (infrastructure as a service): you rent virtual servers, storage and networks, and manage the operating system and everything above it. Example: Amazon EC2.
- PaaS (platform as a service): you deploy your code and data; the provider runs the servers, operating system and runtime. Example: AWS Elastic Beanstalk or Heroku.
- SaaS (software as a service): you use finished software over the internet; the provider runs everything except your data, users and settings. Example: Microsoft 365 or Salesforce.
Let us go through each of these in detail now.
SaaS Deployment
SaaS delivers complete applications through a browser or app, on subscription. The provider runs the infrastructure, platform and application.
Benefits: nothing to install, access from anywhere, automatic updates and predictable per-user pricing.
Watch out for: you're still responsible for who has access, how the product is configured, and your data: sharing settings, retention, export and, in many products, backup. Misconfigured sharing and permissions are a common cause of SaaS data exposure, more often than provider failures. Licence sprawl is also a growing cost problem.
Examples: Microsoft 365, Google Workspace, Salesforce, Slack, Zoom.
Best for: standard business functions such as email, CRM, HR, finance and collaboration, where your needs match what the product does.
Building a SaaS product yourself? See our guide to SaaS tenancy architecture.
PaaS provides a managed environment to build, deploy and run applications. You supply code and data; the provider handles servers, operating systems, runtimes and much of the scaling.
Benefits: faster development, automatic scaling, less operational overhead, and built-in integrations with databases, queues and monitoring.
Watch out for: less control over the runtime, platform limits (supported languages, request timeouts, memory caps), and closer ties to one provider.
Examples: application platforms such as AWS Elastic Beanstalk, Azure App Service, Google App Engine and Heroku, plus managed data services like Amazon RDS, Azure SQL Database and Cloud SQL, which are platform services rather than IaaS.
Best for: web apps, APIs and internal tools, especially for small teams that don't want to run servers.
IaaS (Infrastructure as a Service)
IaaS: infrastructure as a service
IaaS gives you virtual machines, block and object storage, and networking on a pay-as-you-go basis. The provider manages the physical hardware, data centres and virtualization layer; you manage the operating system, middleware, runtime, applications and data. (The original article said users manage the hardware in IaaS, which isn't the case.)
Benefits: maximum flexibility and control, fast scaling, and pay-per-use pricing.
Watch out for: you own OS patching, hardening, backups, scaling configuration and capacity planning. Over-provisioned instances are one of the biggest sources of cloud waste.
Examples: Amazon EC2, Azure Virtual Machines, Google Compute Engine, DigitalOcean Droplets.
Best for: lift-and-shift migrations, software with specific OS or licensing needs, and workloads needing custom networking or hardware such as GPUs.
Key Differences Between SaaS, PaaS and IaaS
Here’s our analysis of SaaS vs PaaS vs IaaS, with the most important comparisons.
| Feature | SaaS | PaaS | IaaS |
| Control Level | Low | Medium | High |
| Primary Focus | Application Usage | Application Development and Deployment | Infrastructure Management |
| Infrastructure Management | Provider | Provider | User |
| Operating System Management | Provider | Provider | User |
| Hardware Management | Provider | Provider | User |
| Data Storage and Backup | Provider | Provider/User | User |
| Security | Provider | Shared Responsibility | User |
| Scalability | Provider-Managed | Provider-Managed | User-Managed |
| Cost Model | Subscription-based | Subscription-based or Usage-based | Usage-based |
| Typical Use Cases | Email, CRM, ERP, HR | Web Applications, Mobile Apps, APIs, Databases | Virtual Servers, Storage, Networking |
| Examples | Microsoft 365, Salesforce, Google Workspace, Zoom | Heroku, AWS Elastic Beanstalk, Google App Engine, Microsoft Azure App Service | AWS EC2, Azure Virtual Machines, Google Compute Engine, DigitalOcean |
| Customization | Limited | Moderate | High |
| Integration | Limited to APIs | More flexible | Highly flexible |
| Security Responsibility | Primarily Provider | Shared between Provider and User | Primarily User |
| Compliance | Often pre-configured | Requires additional configuration | Requires careful configuration and management |
| Cost Predictability | High | Medium | Low |
Also read: Maximize Cloud Efficiency: Complete Guide to Well-Architected Reviews
After understanding the key differences between SaaS, PaaS, and IaaS, it's important to consider which cloud deployment model best aligns with your organization's goals, resources, and technical requirements. Let’s explore how to choose the right model to optimize your cloud strategy.
Beyond the big three: containers and serverless
The original article mentioned serverless only as a future trend. Today, two more models sit between IaaS and PaaS, and they're often the real choice teams make:
- Containers as a service (CaaS): you package applications in containers and the provider runs the orchestration. Examples: Amazon EKS, Google Kubernetes Engine and Azure Kubernetes Service (managed Kubernetes), plus simpler options such as Amazon ECS with AWS Fargate and Google Cloud Run. You get portability and fine control over how applications run, without managing the control plane.
- Functions as a service (FaaS), or serverless: you write functions that run in response to events, and pay only while they run. Examples: AWS Lambda, Azure Functions, Google Cloud Run functions. Ideal for event-driven and spiky workloads; less suitable for long-running processes.
The line between these categories is blurring. AWS Fargate, for example, runs containers with no servers to manage, and EKS Auto Mode now manages Kubernetes worker nodes too. See our managed Kubernetes service and cloud-native architecture guide.
How real companies mix all three
- Early-stage startup: Google Workspace and Slack (SaaS), the product on a platform such as Vercel or Google Cloud Run plus a managed database (PaaS and serverless). No servers to manage.
- Growing SaaS company: Microsoft 365 and HubSpot (SaaS); the product on managed Kubernetes with Amazon RDS (CaaS and PaaS); a few EC2 instances for a legacy service or GPU jobs (IaaS).
- Enterprise: hundreds of SaaS tools, internal apps on PaaS and containers, and large IaaS estates from data centre migrations, often across hybrid and multi-cloud environments.
The pattern: buy SaaS for commodity needs, build differentiating products on the most managed platform that works, and keep IaaS for workloads that genuinely need it. For more on moving existing systems, see cloud migration and our on-premise vs cloud comparison.
Choosing the Right Cloud Deployment Model
As we can see from the table, this is how you would choose each platform:
- SaaS offers the lowest level of control, focusing on application usage. Providers manage the entire infrastructure, making it ideal for businesses with limited technical expertise.
- PaaS provides a middle ground, allowing developers to focus on application development and deployment while the provider manages the underlying infrastructure. It's suitable for organizations that need more flexibility than SaaS but don't want to manage the entire infrastructure.
- IaaS offers the highest level of control, giving users the ability to manage the entire infrastructure stack. It's ideal for organizations with advanced technical expertise and specific infrastructure requirements.
So, which would you pick?
The optimal cloud deployment model depends on various factors, including:
- Level of Control: Consider the degree of control required over the infrastructure and applications.
- Technical Expertise: Assess the organization's technical capabilities and the need for specialized skills.
- Scalability Requirements: Evaluate the need for flexible scaling to accommodate future growth.
As you choose the right cloud deployment model for your business, it's equally important to keep an eye on the evolving landscape of cloud technology. Understanding the future trends in cloud computing will help you stay competitive and prepared for what's to come.
Future Trends In Cloud Computing
Cloud computing is continually evolving, and several emerging trends are reshaping the future of cloud computing. One of these is serverless computing, where developers can build and run applications without managing servers.
This model offers reduced operational overhead and improved scalability. It is particularly well-suited for real-time data processing, IoT applications, and microservices architectures.
Another significant trend is edge computing, which involves processing data closer to the source of generation. This approach reduces latency, improves response times, enhances data privacy, and reduces network traffic. Edge computing is ideal for IoT devices, autonomous vehicles, and real-time analytics.
Additionally, the adoption of multi-cloud and hybrid-cloud strategies is gaining momentum. By 2027, 90% of enterprises are expected to adopt hybrid or multi-cloud strategies to enhance flexibility and scalability. Organizations can optimize performance, cost, and risk by utilizing multiple cloud providers and on-premises infrastructure.
This approach particularly benefits large-scale enterprises with complex IT environments, data-intensive workloads, and regulatory compliance needs.
Conclusion
SaaS, PaaS and IaaS differ in one thing: how much of the stack you manage. IaaS gives the most control and the most work; SaaS gives the least of both; PaaS, containers and serverless sit in between. Security and data are shared responsibilities in every model.
Choose per workload: buy SaaS for standard needs, build on the most managed platform that fits, and use IaaS where you truly need control. Not sure which fits your next project? Talk to SquareOps.!
