As your personal cloud infrastructure advisor, I‘m here to make sure you select the optimal AWS EC2 instance to power your applications. With over 600 options to assess, this comprehensive yet practical guide examines the 5 main Amazon EC2 instance families along with my instance sizing recommendations for different workloads.
Let‘s start with a quick primer on EC2 and instances first.
What is an EC2 Instance?
An EC2 (Elastic Compute Cloud) instance is essentially a virtual machine configured to run applications in the AWS cloud. Each instance provides:
- Compute power (CPU, cores, memory)
- Storage capacity (disks, SSD)
- Network connectivity (bandwidth, ports)
Instances come in different sizes to fit various application requirements. By combining and scaling multiple instances, you can build hugely scalable applications on AWS.
Benefits of using EC2 instances:
- Agility – Launch new instances in minutes
- Cost Efficiency – Pay-as-you-go pricing
- Flexibility – Tailor instances to your app needs
- Reliability – Leverage AWS global infrastructure
Now let‘s examine the 5 main instance categories and see what makes them unique.
Overview: 5 Instance Types
AWS offers 5 major instance types purpose-built for different workload requirements:
| Type | Description | Use Cases |
|---|---|---|
| General Purpose | Balance of compute, memory and networking resources | Web servers, gaming fleets, code repos, mid-size databases |
| Compute Optimized | Ideal for high CPU and network bandwidth applications | Batch processing, web/gaming backend, data analytics, ML training, HPC |
| Memory Optimized | Fast performance for workloads requiring huge memory | High speed databases (Redis, Memcached), real-time big data apps |
| Storage Optimized | High disk throughput and IOPS capabilities | Transactional databases, data warehousing and reporting, distributed file systems |
| Accelerated Computing | Hardware accelerators like GPUs instead of only CPUs | Machine learning, speech recognition, computational fluid dynamics, graphics rendering |
This table summarizes the primary differences between each instance type. Now let‘s examine some popular use cases and top instance recommendations for each category in greater detail.
General Purpose Instances
General purpose provides a balanced profile making it suitable for many mainstream enterprise workloads.
When Should You Use General Purpose Instances?
- Web & Application Servers – Ideal for hosting websites, ecommerce apps, SaaS apps, and microservices. Deliver dynamic content fast.
- Gaming Backends – Create high performance gaming backends connecting players.
- Code Repositories – Store and collaborate on code effectively.
- Medium-sized Databases – Reliably run database engines like PostgreSQL and MySQL.
For example, WordPress sites use general purpose instances like M5.Large to host millions of dynamic web pages reliably while serving content quickly.
Here‘s a comparison of popular balanced instance types:
| Instance | vCPU | Memory (GiB) | Network (Gbps) | Use Case Fit |
|---|---|---|---|---|
| M5.Large | 2 | 8 | Up to 10 | Ideal low cost web server |
| M5a.XLarge | 4 | 16 | Up to 10 | Good for microservices |
| T3.Medium | 2 | 4 | Low to moderate | Dev, test environments |
Pay close attention to network capacity for high traffic web apps and memory for cache or database servers.
Compute Optimized Instances
Compute optimized instances deliver maximum CPU muscle and network capacity for complex workloads.
Let‘s explore some common use cases where they excel:
Batch Processing
Finance, insurance and IoT companies crunch billions of records daily to perform billing, fraud analysis and extract business insights. Using massively parallel processing, some examples include:
- Daily billing cycles calculating interest, overages for 50M customers
- Processing 200 million sensor readings to determine real-time value-at-risk in trading systems
- Telemetry analysis to identify fraudulent activity across billions of transactions
For these enormous scale workloads, compute optimized C5 instances powered by 3 GHz Intel Xeon Scalable processors provide the raw horsepower needed.
Machine Learning Model Training
Developing cutting-edge AI apps using neural networks, computer vision and NLP requires beefy hardware to train deep learning models rapidly:
- Image recognition models can have over 200 layers and 20+ billion parameters
- Language translation models like Google‘s BERT have 24 transformer layers
- Waymo trains autonomous vehicle models using sensor data from over 25M miles driven!
Compute optimized C5.12xLarge instances with 48 vCPUs and 96 GiB RAM trains complex models dramatically faster.
The extra CPU power and network bandwidth accelerate research iterations – a crucial capability.
Recommended Instance Types
For massive scale processing, here are my top compute optimized instance recommendations:
- C5n – Up to 100 Gbps network bandwidth
- C6i – Newest 96 core beast for extreme HPC apps!
- C5ad – Cost-optimized GPU instance for ML inferencing
Monitor CPU credits, network I/O, and GPU stats to optimize spending.
Let‘s shift gears and talk in-memory processing powerhouses…
