AWS EC2 Instance Types Explained: When to Use Each Type

Memory Optimized Instances

Memory-optimized instances are best for workloads that need more memory than CPU power or storage. These instances handle high-performance databases and in-memory applications that process large datasets entirely in memory for ultra-fast response times.

R-series

R-series instances are built for memory-heavy workloads. They offer a higher memory-to-vCPU ratio, which makes them a great fit when your app requires a lot of RAM but not necessarily a large amount of compute power.

We can use them for:

• In-memory caching: Ideal for tools like Redis or Memcached that rely on lightning-fast access to data.
• Real-time big data analytics: Perfect for processing and analyzing data in-memory, with low latency.
• Enterprise apps: Great for large applications that need solid memory allocation to run reliably.

Some of its popular options include r5, r6a, r8g, and r4. Each one comes in different sizes depending on your needs. 

X-series and Z-series

X-series instances provide extreme memory capacity and are specifically designed for memory-intensive enterprise workloads. These are the go-to for:

• SAP HANA
• In-memory databases
• Real-time, large-scale analytics

Some of their standard models are x1e, x2gd, and x8g.

Z-series instances are a bit different. They combine high memory and high CPU frequency, which makes them ideal for:

• Electronic design automation (EDA)
• Financial simulations
• Large relational databases
• Computational lithography

If your workload requires strong single-threaded performance and a substantial amount of RAM, the Z-series (such as the Z1d) offers a balanced punch.

Storage Optimized Instances

Storage-optimized instances use locally attached, high-speed SSDs (NVMe), which are ideal for workloads requiring extremely fast and reliable storage. That’s why we can use them for NoSQL databases, data lakes, or large-scale data processing that requires high IOPS (Input/Output Operations Per Second).

Let’s look at some common storage-optimized instances available: 

I-series, D-series, H-series

The following table shows details of each instance series:

For the complete list of I-series, H-series, and D-series instances, check out the documentation.

Local storage performance

Local storage uses NVMe SSDs, providing blazing-fast performance with very high IOPS (Input/Output Operations Per Second), sub-millisecond latency, and high throughput for your applications. However, it’s ephemeral, which means the data is lost when the instance is stopped or terminated.

That’s why ephemeral storage is used mainly for:

• Temporary scratch space
• Buffer/cache storage
• High-speed temporary databases (like Redis, Cassandra with replicas)

In contrast, EBS-backed volumes (Elastic Block Store) are persistent storage that remains available even if the instance is stopped. EBS is network-attached and is highly durable, offering features like snapshots for easy backup and recovery. This makes it ideal for workloads where data persistence is important, such as databases and critical files.

While ephemeral storage provides incredible performance, it doesn’t provide data durability. On the other hand, EBS-backed storage offers excellent durability and resilience, ideal for workloads that need both performance and data protection.

Accelerated Computing Instances

Accelerated computing instances, also called hardware-accelerated instances, are used when we need specialized hardware like GPUs (Graphics Processing Units), FPGAs (Field Programmable Gate Arrays), or AWS custom chips to offload compute-intensive tasks that would be too slow or inefficient on CPUs alone. 

That’s why they provide improved performance and are suitable for workloads that require heavy parallel processing or complex calculations.

GPU-based instances (P and G families)

GPU-based instances are perfect for tasks that demand powerful parallel computing. With NVIDIA GPUs at their core, these instances are optimized for heavy-duty workloads like ML model training, 3D rendering, high-end gaming, and other graphics-intensive tasks.

Two key families in the GPU-based instance category are:

• P family (e.g., P4, P5): These are equipped with NVIDIA GPUs and are tailored for machine learning (ML) and high-performance computing (HPC) applications.
• G family (e.g., G4, G5): Also powered by NVIDIA GPUs, these are ideal for graphics workloads, ML inference, and game streaming.

Specialized AI/ML instances (Infm and Trn families)

For AI and machine learning workloads, the Inferentia-optimized instance (Inf) and Trainium-optimized instance (Trn) families are built with custom AWS silicon like AWS Inferentia and AWS Trainium chips. They can train and infer deep learning models at scale to deliver top-tier price-performance.

• Inf family (e.g., Inf1, Inf2): These instances are perfect for low-latency, high-throughput inference tasks, backed by AWS Inferentia chips.
• Trn family (e.g., Trn1, Trn2): Optimized for distributed deep learning model training, they provide high-speed interconnects and substantial compute capacity, driven by AWS Trainium chips.

These instances are scalable and support TensorFlow, PyTorch, and Apache MXNet frameworks to increase efficiency and provide seamless performance.

We can use them for:

• Deep learning training at scale: Use Trn1 for cost-effective, high-performance training of large models.
• AI inference at low cost: Use Inf1 for fast, low-cost model inference.
• Large-scale AI deployments: Support environments with millions of inferences per second, such as recommendation engines, speech recognition, and autonomous vehicles.

How To Choose the Right EC2 Instance?

With so many instance types out there, it’s quite challenging to know what to look for so you can make the most of what AWS offers. So, let’s see how you and your team can make confident choices that balance performance, cost, and scalability.

Key decision factors

Start by considering what your workload really needs. Identify the CPU power, memory, storage, and networking requirements that you need, as different workloads require different instance types. 

You’ll also want to factor in how your traffic behaves. Is it steady and predictable, or does it spike at certain times? That can help us decide whether to go for something that can scale on demand.

And mostly, we work within cost constraints, so weigh performance against what we’re willing to spend. If we don’t need persistent storage, using ephemeral storage could save us a fair bit compared to EBS volumes.

AWS tools for selection

Thankfully, AWS gives us several tools to narrow things down. Here are some of these:

• Instance Explorer is a quick way to filter instance types based on the specs you’re after.
• AWS Compute Optimizer looks at how you’re currently using instances and suggests better options if they exist.
• AWS Pricing Calculator estimates costs upfront, which is great if we want to compare options before deployment.

Benchmarking and testing

Even with all these tools, it’s still worthwhile to run some tests yourself. Benchmarking different instances in our environment shows us how they hold up under real workloads. This gives us a better sense of performance, latency, and cost, all based on what actually matters to us.

Cost and Purchasing Options

When it comes to EC2, the way we purchase instances can impact our overall costs. Since AWS offers several purchasing options, each tailored to different workloads, let’s see how to choose the right one for our current project and how to manage our cloud bills effectively.

On-demand instances

On-demand instances give us the most flexibility. We can launch them whenever we need, pay by the second, and shut them down just as easily, no commitments required. They’re perfect for:

• Short-term projects
• Spiky or unpredictable workloads
• Getting started quickly without upfront planning

But that flexibility comes at a higher price. For long-running or predictable workloads, other options can save us a lot more over time.

Reserved and Spot instances

Reserved instances are ideal when we know a workload will run consistently for an extended period. By committing to a one or three-year term, we can save up to 75% compared to on-demand pricing. It’s a smart move for running stable, always-on apps.

Spot instances, on the other hand, are better for a tight budget. AWS offers unused capacity at massive discounts, up to 90% off, but this can be withdrawn with little notice. That means they’re best for jobs that can handle interruptions, like:

• Batch processing
• Data analysis
• CI/CD workloads
• Stateless services

Quick comparison: On-demand vs. reserved vs spot instances

Here’s a quick side-by-side table to help you decide:

For a more detailed comparison, check out the AWS official website.

Cost optimization tips

To stretch our budget even further, here are a few tools and strategies we can lean on:

• Savings plans: These offer flexible, commitment-based pricing across EC2 and other services. We can save up to 72% by committing to consistent usage.
• Auto scaling: Automatically adjusts our capacity to match demand, so we’re never over-provisioning.
• Right-sizing: Regularly review instance types and sizes to make sure they still fit. Small tweaks can mean big savings over time.

Conclusion

There’s no one-size-fits-all when it comes to EC2 instances and that’s a good thing. Whether we’re setting up a quick dev environment or scaling a complex machine learning pipeline, AWS gives us the flexibility to choose exactly what we need.

The key is understanding our workload, knowing our performance goals, and staying mindful of cost. The more intentional we are with our choices backed by tools, testing, and a bit of trial and error, the more we’ll get out of our infrastructure.

If you’re keen to keep building your AWS skills or explore more of what EC2 can do, here are a few resources to check out:

• Introduction to AWS – A great starting point to understand the core AWS concepts and services.
• AWS Cloud Technology and Services – Explore AWS services and how they fit together in the cloud ecosystem.
• AWS Security and Cost Management – Learn about the best practices for securing your AWS environment and managing costs effectively.
• Introduction to AWS Boto in Python – A hands-on course that teaches you how to use Boto3 (AWS SDK for Python) to automate tasks and manage AWS resources.

These resources will help you take your AWS skills to the next level, with practical examples and real-world applications.