During a few of my AWS Cloud infrastructure build outs, client team members tend to wonder and ask why I use both Internet Gateway service, as well as a NAT Gateway service when setting up the network layer of the environment. Some with advanced networking knowledge made a point stating that we could skip the NAT configuration, and use the Internet Gateway service for all Internet access, which technically is true, and can be done. As a matter of fact, for smaller configurations with limited complexity requirements, that is a common practice; a single Internet Gateway service for all ingress/egress Internet traffic.
But why is that in other complex and large environments, especially where security is of a high concern, we tend to use different internet facing Gateway services? and what is the benefit of doing so?
The idea of cloud computing emerged in the early 2000s as businesses began seeking alternatives to the cost and complexity of maintaining on-premise servers and infrastructure. The first major development was Infrastructure as a Service (IaaS), which provided virtualized computing resources such as servers, networking, and storage on-demand. Instead of owning physical hardware, companies could rent these resources from providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud.
This pay-as-you-go model allowed businesses to scale quickly and reduce upfront costs, laying the foundation for the modern cloud era.
As cloud technology matured, the focus expanded beyond just infrastructure. This led to the rise of Platform as a Service (PaaS), which provided developers with a ready-made environment to build, test, and deploy applications. Unlike IaaS, where users still needed to manage operating systems and middleware, PaaS abstracted much of the underlying complexity.
Early examples include Google App Engine and Microsoft Azure App Services. PaaS made it easier for developers to focus on writing code rather than managing infrastructure, accelerating innovation and reducing time-to-market for applications.
Following the growth of IaaS and PaaS, Software as a Service (SaaS) emerged as one of the most consumer-visible layers of cloud computing. SaaS delivers fully functional software applications over the internet, accessible through a browser or app without installation or heavy IT management. Salesforce, launched in 1999, is often credited as the pioneer of SaaS with its customer relationship management (CRM) platform. Since then, SaaS has become ubiquitous in tools like Google Workspace, Slack, Dropbox, and Zoom, providing organizations with scalable, subscription-based software solutions.
The key differences among these three models lie in the level of abstraction and control. With IaaS, organizations maintain the most control, as they are responsible for managing operating systems, applications, and data, while the provider manages hardware. PaaS shifts more responsibility to the provider by managing the runtime, middleware, and development tools, leaving users to focus on application logic.
SaaS abstracts everything, with the provider managing the entire stack while users simply consume the software. This tiered model gives businesses the flexibility to choose how much they want to manage versus outsource.
Below are a few comparison points on various categories between the three services.
Aspect
IaaS (Infrastructure as a Service)
PaaS (Platform as a Service)
SaaS (Software as a Service)
Definition
Provides virtualized computing resources like servers, storage, and networking.
Provides a ready-made platform with tools for developing, testing, and deploying applications.
Provides complete software applications delivered over the internet.
User Responsibility
Manage applications, data, runtime, and OS.
Manage only applications and data.
Only use the software; provider manages everything.
AWS EC2, Google Compute Engine, Microsoft Azure Virtual Machines.
Google App Engine, Heroku, Microsoft Azure App Services.
Salesforce, Google Workspace, Dropbox, Zoom.
To put it in simpler terms, IaaS is ideal for enterprises that want scalable computing power for big data analytics, storage solutions, or hosting virtual machines, who also have enough engineering task force to build the top layers.
PaaS is popular among software development teams that need an efficient environment for building applications without managing infrastructure. SaaS is best suited for end-users and organizations seeking ready-to-use software such as project management tools, HR systems, or communication platforms. Each model caters to different needs, from infrastructure backbone to developer empowerment to end-user functionality.
Today, these three service models coexist and complement each other in the broader cloud ecosystem. For example, a business may host its servers on AWS (IaaS), build custom applications using Google App Engine (PaaS), and run daily operations with tools like Microsoft 365 (SaaS). Together, SaaS, PaaS, and IaaS represent the evolution of cloud computing from infrastructure outsourcing to complete software delivery, fundamentally changing how organizations adopt and scale technology in the digital era.
Containers: A lightweight, portable unit that packages an application and its dependencies into a single standardized executable image. Unlike traditional applications that rely on host operating systems and local dependencies, containers provide isolation at the process level, ensuring consistency regardless of where they run—be it on a developer’s laptop, an on-premises server, or a cloud environment. This makes containers particularly useful in DevOps workflows, continuous integration/continuous deployment (CI/CD) pipelines, and hybrid cloud strategies, where consistency and portability are key.
In the world of technology, three roles have shaped how systems are built, run, and kept alive: the traditional System Administrator, the modern DevOps engineer, and the specialized Site Reliability Engineer. Each emerged from different needs, shaped by the tools and challenges of their time. As businesses shifted from on-premises hardware to cloud-native architectures, these roles adapted, overlapped, and sometimes evolved into one another. This is the story of their journey—where they came from, how they’ve changed, and where they stand today.
In today’s hyper-connected, cloud-dominated tech landscape, it’s easy to forget that some of the most valuable engineering skills aren’t about learning the latest framework or mastering Kubernetes. One of the most underrated yet crucial abilities remains the art of troubleshooting—a process that goes beyond surface-level fixes and delves into the root causes of complex technical issues. Whether in IT, software development, or systems engineering, effective troubleshooting is essential for sustaining reliable infrastructure and ensuring long-term performance.
Log aggregation is the process of collecting and centralizing log data from various sources into a single system where it can be processed, stored, and analyzed. Logs are generated by software systems, applications, services, and infrastructure components, and they provide valuable insights into the behavior and performance of these systems. Aggregating these logs involves gathering them from different servers, applications, and environments to offer a unified view of all system activities. This allows developers, system administrators, and security teams to monitor, troubleshoot, and maintain the system more efficiently.
Automation in the software development industry refers to the use of tools, scripts, and processes to perform repetitive tasks with minimal human intervention. It streamlines the software development lifecycle, including activities like code integration, testing, deployment, and monitoring. Automation helps to increase efficiency, reduce errors, and ensure consistency across all stages of development, ultimately leading to faster delivery of high-quality software. In this context, it becomes an integral part of modern development methodologies like DevOps and Continuous Integration/Continuous Deployment (CI/CD).
Infrastructure as Code (IaC) refers to the practice of managing and provisioning IT infrastructure through code rather than through manual processes. With IaC, infrastructure configurations—such as servers, networks, databases, and security settings—are written in machine-readable code and stored in version-controlled repositories. This approach enables the automation of infrastructure management, which is both repeatable and consistent, eliminating human errors and ensuring that the infrastructure is always in a known, desired state.
Container orchestration refers to the automated management of containerized applications across clusters of machines. It involves processes like deployment, scaling, load balancing, and networking, ensuring that containers run efficiently in distributed environments. The goal is to abstract away the complexities of handling multiple containers and their interdependencies, enabling seamless deployment and operation at scale.
DevOps has become a critical component in the software development lifecycle (SDLC) by bridging the gap between development and operations teams. Traditionally, these two groups operated in silos, which led to inefficiencies, delayed releases, and increased risk of failure. DevOps fosters collaboration and integration, enabling both teams to work together throughout the lifecycle. By automating manual processes, continuous integration (CI) and continuous delivery (CD) pipelines allow for faster and more frequent updates, which is essential for maintaining competitive advantage in today’s fast-paced software development landscape. This shift not only accelerates product development but also helps to ensure higher quality, as bugs are identified and addressed earlier in the process.
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