How EC2, S3, VPC, IAM, and Serverless Services Fit Together in Real-World Systems
Cloud technologies have become a standard part of modern software development. Yet many experienced developers still interact with cloud platforms through infrastructure teams, CI/CD pipelines, or managed services without fully understanding what happens behind the scenes.
In this article, we will share a practical overview of AWS architecture, focusing on the services developers encounter most often and, more importantly, how they work together.
With over six years at LeverX, I have helped design and operate cloud environments across numerous enterprise projects. This article is a practical guide for software engineers who want to build a stronger cloud foundation without getting lost in infrastructure minutiae.
One of the biggest misconceptions about cloud computing is that it's fundamentally different from traditional infrastructure. Essentially, it’s just a computer.
Of course, AWS is much more than a single computer. The real innovation is that AWS abstracts away hardware procurement, maintenance, networking, power, cooling, and capacity planning, allowing teams to consume infrastructure on demand.
For developers, this means:
Instead of asking, "Which server should we buy?" teams ask, "Which service best fits this workload?"
AWS offers more than 200 services, but most applications rely on a relatively small set of core components. According to the masterclass, these components fall into five major categories:
| Category | Core Services |
| Compute | EC2, ECS, Lambda, AWS Batch |
| Storage | S3, EBS, Glacier |
| Networking | VPC, Route 53, Internet Gateway |
| Databases | RDS, DynamoDB |
| Security | IAM, KMS, Security Groups |
Understanding these categories is enough to comprehend the architecture of most modern cloud-native applications.
Amazon EC2 (Elastic Compute Cloud) is the most familiar model for many developers.
You provision a virtual machine with a specific amount of CPU and memory, install your software stack, and manage the operating system yourself.
EC2 works particularly well when:
However, EC2 has a downside: you pay for allocated capacity even when your application isn't fully utilizing it.
For most modern applications, LeverX teams increasingly favor container-based architectures.
Amazon ECS (Elastic Container Service) allows developers to deploy containers without managing complex orchestration platforms directly.
Benefits include:
Instead of running one large server, teams can run many smaller containers and scale them dynamically based on demand.
Serverless services remove infrastructure management entirely.
With Lambda, developers upload code and AWS executes it when triggered.
Key advantages:
According to Evgeniy, serverless architectures are particularly attractive for workloads that experience irregular traffic patterns.
A common mistake among developers is treating storage as a single problem. AWS provides different storage technologies optimized for different access patterns.
Amazon S3 (Simple Storage Service) is arguably the most important AWS service. S3 stores objects rather than files on disks.
Typical use cases:
Many modern frontend applications can be deployed entirely from S3 when combined with CloudFront.
Elastic Block Store behaves more like traditional disk storage. It is attached to EC2 instances and used for:
Think of EBS as the cloud equivalent of an SSD attached to a server.
Not every piece of data needs instant access. Glacier provides extremely inexpensive storage for:
The tradeoff is retrieval speed. Accessing archived data may take hours or even days.
Many software engineers avoid networking until production issues force them to learn it. Yet AWS networking concepts are surprisingly intuitive.
Amazon VPC (Virtual Private Cloud) is essentially your isolated network inside AWS. Within a VPC, you can create:
A useful mental model is to think of a VPC as your company's office network, except it's entirely virtual.
A key architectural principle to remember:
Databases should not be publicly accessible.
Typical architecture:
Only the API layer should communicate with databases directly.
Private subnets help enforce this separation.
Route 53 handles domain management and DNS routing. Examples:
It becomes the central naming system for cloud infrastructure.
There are two major AWS database approaches.
RDS provides managed relational databases such as:
Developers get:
without managing database servers manually.
DynamoDB is AWS's managed NoSQL database. Advantages:
As Evgeniy explained, DynamoDB offers flexibility that traditional relational databases cannot easily provide.
A key thing to remember is that security is everyone's responsibility. AWS follows a shared responsibility model:
Identity and Access Management (IAM) controls:
For developers, IAM becomes critical when services need to interact securely. Examples:
The principle of least privilege should guide all IAM policies.
Security Groups function as virtual firewalls. Common rules include:
This simple pattern dramatically reduces attack surfaces.
AWS Key Management Service handles encryption keys. Use cases:
For organizations working in regulated industries, KMS often becomes a foundational security component.
One of the most valuable parts of the session was the architecture walkthrough.
A typical production web application might look like this:
The result is an architecture that is scalable, fault tolerant, and secure by design.
According to the masterclass, several practices consistently separate successful cloud projects from problematic ones.
Use:
instead of relying on a single large server.
Availability Zones are independent data centers within an AWS Region.
Distributing workloads across zones improves resilience against outages and infrastructure failures.
Especially for:
Use HTTPS and managed encryption services whenever possible.
One of the most practical recommendations from the session was to actively use:
Cloud costs rarely become a problem overnight—but they often become expensive surprises when left unmonitored.
During the Q&A, participants asked about AI adoption in enterprise projects.
According to Evgeniy, AI services are increasingly becoming part of production environments, not just experiments.
Within AWS, Bedrock provides access to multiple foundation models while preserving AWS-native governance and security controls. For organizations already operating within AWS ecosystems, this can simplify compliance, networking, and permission management.
For developers, this means future architectures will increasingly include:
all integrated alongside traditional application components.
While we cannot teach you every AWS service within this article, what we attempted to do was to help you understand the fundamental building blocks and how they connect.
Once that mental model is in place, learning advanced topics such as Kubernetes, Infrastructure as Code, CI/CD pipelines, AI services, or serverless architectures becomes significantly easier.
At LeverX, knowledge sharing is an important part of engineering culture. Internal communities, technical masterclasses, and expert-led sessions help developers continuously expand their skills across cloud, DevOps, AI, SAP, and enterprise technologies.
AWS can seem overwhelming when viewed as a catalog of hundreds of services.
But when you reduce it to a few core building blocks—compute, storage, networking, databases, and security—the platform becomes much easier to understand.
For experienced software developers, mastering this mental model is often the fastest path toward designing better systems, collaborating more effectively with DevOps teams, and building cloud-native applications with confidence.
And remember: cloud architecture isn't magic. It's simply a set of well-designed building blocks that, when combined correctly, can power almost any modern application.