Welcome to EC2 versus Azure virtual machines. In this lecture, we're going to take a look at virtual machines in AWS and in Microsoft Azure.

Now, if you've worked in either cloud before, chances are good that you've already launched one or two virtual machines. However, when you're designing for a multicloud environment, it's not just about knowing how each service works in isolation. Instead, it's about understanding how to align them so you can build, deploy, and manage workloads consistently across both platforms.

In AWS, EC2 is the solution to deploy when you need full control over the virtual server. You begin the deployment by choosing an Amazon Machine Image or AMI which acts as a template that includes the OS and any installed software. You can use AWS-provided images, community-maintained images, or you can build your own for consistency across regions or accounts.

You then have the instance type selection which includes options like general purpose, compute optimized, memory optimized, and others. Each is designed to align with different workload patterns, so when you're working in a multicloud setup, this flexibility can help you match AWS instance types to their closest equivalents in Azure, even if the naming conventions differ.

Now, as far as storage goes, EC2 typically uses EBS volumes, which are block-level volumes that persist independently from the VM itself. So for example, you can attach multiple volumes, you can snapshot them for backup or cloning, and you can choose performance levels that align with your workload. This is no different from the process in Azure, it's just that the terminology is a bit different.

Networking in AWS is handled through VPCs, which give you private, logically isolated networks. Within a VPC, you configure subnets, you configure routing, security groups, and network interfaces. For multicloud architects, this model maps well to Azure's virtual network architecture, which we'll touch on in a moment.

Azure VMs work in pretty much the same way. You start by choosing an image from the Azure marketplace, like Linux or Windows, or some other third-party software image. And just like with AMIs, you can also build your own custom images and manage them through the Azure Compute Gallery, which is helpful if you're standardizing VM configurations across both clouds.

And just like in the case of EC2 instances, Azure VM sizes are grouped into series. For example, you have A, B, and D series for general purpose, E series for memory, F series for compute, and so on.

You'll notice that while the naming differs from AWS, the underlying structure is comparable. From a multicloud perspective, this means you can define a performance baseline in one cloud and map it to the other fairly easily during migration or failover planning.

For storage, Azure uses managed disks. These are block-level volumes managed by the platform with performance tiers similar to EBS, options like standard HDD, standard SSD, premium SSD, and so on. Azure abstracts much of the backend complexity, which makes it easier to ensure consistency when deploying to multiple regions or building repeatable infrastructure pipelines.

Azure Virtual Networks, or vNets, are conceptually similar to AWS VPCs. In both platforms you define subnets, route tables, network security rules, and network interfaces to control how resources communicate within and outside the network.

If you're designing a network topology that spans both clouds, this one-to-one conceptual match between VPCs and vNets makes hybrid and multicloud network architectures more manageable. So from a high level, both platforms let you do the same core things. You choose an image, select a VM size, attach storage, configure networking, and secure the environment. But as a multicloud architect, the key is understanding not just how these services compare, but how to abstract the differences in your design process.

Let's walk through a practical example. Suppose you're building a batch processing system for overnight data crunching. It needs to be fast, but only for a few hours a day. In AWS, you might create a custom AMI with your processing tools baked in, spin up some C5 instances with high performance EBS volumes, and then manage everything through an autoscaling group.

A cloud watch alarm might watch queue depth and trigger scale out events during high load. In Azure you take a similar approach. You'd start with a custom image or a marketplace VM image, use some F series instances for compute, attach premium SSDs, and deploy everything as part of a virtual machine scale set. Azure Monitor handles the metrics and triggers scaling based on queue depth.

This is where multicloud thinking starts to matter. The services themselves are different, but the deployment patterns, the monitoring goals, and scaling strategies are functionally equivalent. This means that if you design your automation and configuration management tools around high-level objectives like desired CPU utilization or queue length thresholds, you can reuse much of that logic across both platforms.

It also means you can test resilience by shifting noncritical workloads between clouds or design failover procedures that launch VMs from standardized images using common configuration frameworks like Terraform or Bicep.

Of course, there are differences that can affect your design decisions. As you'd expect, Azure tends to make it easier to work with Microsoft licensing. For example, if you're running Windows Server or SQL Server, you can take advantage of things like Azure Hybrid Benefit to reduce your costs.

AWS on the other hand, gives you more granular control over instance options and storage tiers. This might be useful if you need to tune performance for specific use cases. From an operation standpoint, things like startup time, scale-set behavior, disk throughput, and monitoring APIs vary slightly, so your observability and automation tools need to accommodate these differences.

But the foundational concepts like compute, storage, network, identity, monitoring, and scaling are all consistent enough that you can build patterns that work across both clouds.

So if you're building in just one cloud, you're mainly focused on depth. But in multicloud you're looking for alignment or ways to build with confidence in either environment without reinventing everything from scratch.

AWS EC2 vs Azure Virtual Machies

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