Day 3 of 90 Days of DevOps: What is Cloud Computing? Understanding Its Models, Benefits & Its Role in DevOps

In my previous blog, I explored the Software Development Life Cycle (SDLC) and its various models that guide software development from planning to deployment. As I continue my #90DaysOfDevOps journey, today’s focus is on a key pillar that powers modern DevOps practices, Cloud Computing.

Let’s explore cloud computing in a simple way, what it is, the different types of models it offers, the benefits it brings, and how it plays a key role in making DevOps practices smoother and more efficient.

What is Cloud Computing?

Cloud computing is the on-demand delivery of IT resources including servers, storage, databases, networking, software, analytics, and intelligence, over the internet with a pay-as-you-go pricing model. It enables faster innovation, flexible resource management, and significant cost-efficiency by eliminating the need for owning and maintaining physical infrastructure.

Instead of buying and maintaining physical servers or data centers, organizations can rent computing power and storage from cloud providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).

Advantages of Cloud Computing

1. Pay-as-You-Go Pricing

No need to invest in heavy infrastructure. You only pay for what you use. nothing more, nothing less. This helps startups and large enterprises alike to manage budgets efficiently.

2. Cost Efficiency

Cloud providers eliminate the need for hardware, data centers, and manual maintenance. This drastically reduces upfront and operational costs, allowing teams to focus more on innovation.

3. Scalability

Cloud resources scale automatically during peak and off-peak times, ensuring optimal performance while avoiding unnecessary costs.

4. High Availability

Cloud providers host services across multiple regions and availability zones. This ensures that applications are always up and running, even in case of local outages or failures.

5. Reliability & Backup

Built-in backup, disaster recovery, and redundancy mechanisms make sure your data is safe and recoverable at all times. You don’t need to worry about accidental deletions or system failures.

6. Security

Leading cloud platforms invest heavily in robust security features like identity management, encryption, and threat detection. They also comply with international standards and certifications (like ISO, SOC, GDPR, HIPAA).

7. Unlimited Storage

Whether you're storing small text files or massive datasets, cloud platforms provide virtually unlimited storage that scales as your data grows, without manual configuration.

8. Automation

Cloud supports automation tools like scripts, APIs, and services such as AWS CloudFormation, Terraform, or CI/CD pipelines. This enables teams to automate deployments, backups, monitoring, and scaling key practices in DevOps.

Cloud Deployment Models

Cloud deployment models define how cloud services are made available to users. Based on the level of control, security, and management needed, organizations can choose from the following common deployment models:

1. Public Cloud

In a public cloud, cloud resources are owned and managed by a third-party cloud provider (like AWS, Microsoft Azure, or Google Cloud) and are delivered over the internet.
This is a multi-tenant environment, meaning multiple users share the same infrastructure.

Advantages:

• Cost-effective (pay-as-you-go)

• No infrastructure maintenance

• Scalable and highly available

Disadvantages:

• Less control and security

• Not ideal for highly sensitive workloads

Example: Hosting a static website on AWS S3.

2. Private Cloud

A private cloud is exclusively used by a single organization. The infrastructure can be located either on-premises or hosted by a third-party provider, but it offers more control and security.

Advantages:

• High security and privacy

• More control over data and resources

• Customizable infrastructure

Disadvantages:

• Expensive to build and maintain

• Needs skilled professionals.

Example: Banking applications with strict regulatory requirements.

3. Hybrid Cloud

The hybrid model combines both public and private clouds, allowing data and applications to be shared between them. It offers the flexibility to keep sensitive workloads private while using the public cloud for less-critical operations.

Advantages:

• Balanced cost and control

• Ideal for disaster recovery and burst workloads

• Supports gradual cloud adoption

Disadvantages:

• Complex setup and management

• Requires compatibility between environments

Example: A healthcare system using private cloud for patient data and public cloud for mobile app hosting.

4. Community Cloud

A community cloud is shared among several organizations with similar requirements or goals, such as compliance, security, or industry standards. The infrastructure can be managed internally or by a third party.

Advantages:

• Shared cost among community members

• Collaboration-focused

• Meets specific regulatory needs

Disadvantages:

• Limited scalability compared to the public cloud

• Governance and security responsibilities are shared

Example: Universities sharing research infrastructure.

Cloud Service Models

Cloud Service Models describe what level of service the cloud provider offers to the user, ranging from raw infrastructure to fully managed applications. Understanding these models is essential for DevOps, as they influence how applications are built, deployed, and managed.

1. Infrastructure as a Service (IaaS)

This is the most flexible model. With IaaS, the cloud provider gives you virtual machines, storage, and networking, kind of like renting a computer in the cloud. You get full control over the operating system, configurations, and software you want to run. IaaS is super useful when we want to create our environments for testing, staging, or production, especially when custom configurations are needed.

You manage: The OS, your apps, and everything above
The cloud provider manages: The hardware and core infrastructure

Examples: AWS EC2, Azure Virtual Machines, Google Compute Engine

2. Platform as a Service (PaaS)

PaaS feels like a step up in convenience. You don’t need to manage the servers or operating systems anymore. The platform is already set up, you just focus on writing and deploying your code. It simplifies the CI/CD process. We can focus on building features while the platform handles scaling, deployment, and patching.

You manage: Your application and data
The cloud provider manages: The OS, servers, and runtime

Examples: AWS Elastic Beanstalk, Google App Engine, Heroku

3. Software as a Service (SaaS)

This one’s the easiest to use. SaaS is when the cloud provider delivers a complete software application that you can access through a web browser. No need to install or manage anything. Even though we don’t usually build SaaS apps ourselves during DevOps learning, we do use a lot of SaaS-based tools for communication, version control, and monitoring.

You manage: Just using the service
The cloud provider manages: Everything else

Examples: Google Docs, Dropbox, Salesforce, Microsoft 365

Cloud Computing in DevOps

Cloud and DevOps are a perfect match. Here’s how cloud computing powers DevOps at every stage:

DevOps Lifecycle Support

Final Thoughts

Today, we explored the core concepts of cloud computing, including its service models (IaaS, PaaS, SaaS) and how they align with DevOps practices. We learned that cloud computing provides flexibility, scalability, and cost-efficiency, making it a perfect fit for modern development workflows. As we move forward in our #90DaysOfDevOps challenge, the integration of cloud technologies will continue to play a pivotal role in enhancing automation, collaboration, and overall system performance.

Thanks for reading! If you found this helpful, feel free to drop a comment, share it with others, or follow along for tomorrow’s update!