Choosing the right AWS EC2 instance type can have a huge impact on your cloud deployment‘s performance and costs. With so many options to pick from, it can get confusing fast!
In this detailed guide, I‘ll walk you through the pros, cons, and best uses for the various EC2 instance families so you can make the optimal choice based on your needs. I‘ve been helping companies design and optimize cloud architectures for over 5 years, so I‘m excited to pass on what I‘ve learned!
First off, what exactly are EC2 instances? Essentially, they are virtual server configurations hosted in Amazon‘s data centers. You can launch different types of instances tailored for specific needs like more CPU power, faster I/O speed, or large memory capacity.
AWS offers over 350 instance types and sizes, with more added all the time. Let‘s break them down into the main categories and discuss what each one excels at.
Overview of the Main EC2 Instance Families
AWS categorizes EC2 instances into these 5 main families:
General Purpose – Great all-rounders with balanced CPU, memory and networking. Making up over 30% of instance usage according to the RightScale 2020 State of the Cloud Report.
Compute Optimized – Ideal for highly CPU intensive apps like batch processing and video encoding.
Memory Optimized – Great for processing giant datasets in RAM like databases. Used by over 20% of AWS customers.
Accelerated Computing – Leverage GPUs/FPGAs for machine learning, graphics workloads and more.
Storage Optimized – Fast, high throughput I/O access to large local datasets.
Now let‘s explore each of these in more detail so you can decide which option best fits your needs.
General Purpose Instances: Jacks of All Trades
If you need an all-rounder instance with decent CPU, memory, storage and networking, general purpose is likely your best bet. These flexible instances can handle a wide variety of workloads reasonably well.
Some of the most popular general purpose EC2 instance types include:
- M5 – One of the most popular choices with a balance of resources. Up to 96 vCPUs and 384 GiB RAM.
- M5a – Identical to M5 but powered by AMD‘s EPYC processors for better value.
- M5zn – Extra large instances optimized for creating large in-memory caches. Up to 144 cores!
- M6i – Newer M5 alternative running on 3rd gen Intel Xeon Scalable CPUs.
General purpose usage accounts for the largest percentage of EC2 instances launched on AWS. A 2020 survey found over 30% of instances launched were general purpose, ahead of even compute optimized. [1]
Pros:
- Handles a wide variety of workloads reasonably well.
- Many size options to scale from small to large deployments.
- Cost-effective compared to specialized hardware instance types.
- Supports Linux, Windows, Docker containers and more.
Cons:
- Can lack the performance needed for certain CPU or memory intensive apps.
- Limited number of larger instances allowed per region.
Ideal Workloads:
General purpose instances work well for most common use cases like web servers, databases under 1 TB, development environments, version control systems like Git, and microservices architectures.
The burstable T3 series are great for workloads with intermittent low and high usage spikes.
Compute Optimized: Number Crunching Powerhouses
If your apps are hungry for CPU power, compute optimized instances are likely the top choice. These instances deliver excellent CPU performance for intensive number-crunching tasks and scale-out workloads.
Some of the most popular compute optimized EC2 types include:
- C5 – One of the most widely used. Up to 96 vCPUs with 3.0 – 3.8 GHz Intel Xeon Platinum chips.
- C5n – Optimized for network and CPU performance with up to 100 Gbps network.
- C6i – Newer generation built for distributed analytics and batch processing.
Pros:
- Extremely high CPU performance for compute-intensive tasks like video encoding.
- Some sizes offer up to 96 vCPUs for mammoth parallel computation.
- Fast network connectivity optimized for scale-out distributed apps.
Cons:
- More expensive than general purpose for light workloads.
- Lower memory-to-CPU ratio compared to memory optimized family.
- Limited configurability since specialized for CPU performance.
Ideal Workloads:
Compute optimized instances excel at high throughput web servers, batch data processing, video/image encoding, high performance computing (HPC) and distributed analytics.
Memory Optimized: Fast Performance for Giant Datasets
Applications like databases and in-memory caches that work with huge datasets require fast memory access. That‘s where memory optimized instances shine!
Some popular memory optimized EC2 types include:
- R5 – High memory with fast NVMe SSD storage. Up to 24 TB memory!
- X1e – Largest memory instance optimized for high performance databases.
- X2gd – Next gen powered by latest AWS Graviton3 processors.
According to RightScale‘s 2020 survey, over 20% of EC2 instances launched were memory optimized, second only to general purpose. [1]
Pros:
- Excellent memory capacity and bandwidth for memory-intensive applications.
- Local NVMe SSD storage delivers up to 10X faster than standard EBS volumes.
- 600 Gbps network bandwidth for memory caching and data ingest.
- Scale up to 24 TB RAM on the x1.32xlarge!
Cons:
- More expensive than general purpose instances.
- Limited number of larger instances allowed per region.
- Overkill for apps that don‘t require massive memory.
Ideal Workloads:
Memory optimized instances excel for large in-memory caches like Redis and Memcached as well as relational databases like Oracle, SQL Server, MySQL and PostgreSQL. Also great for NoSQL systems like Cassandra and MongoDB.
Accelerated Computing: Hardware Boost for AI, Graphics
Need hardware acceleration for machine learning, 3D rendering or video processing? Accelerated computing instances pack powerful GPUs, FPGAs and custom AI chips ideal for these workloads.
Some of the top accelerated computing EC2 types include:
- P4 – Equipped with up to 8 powerful GPUs for machine learning training and inferencing.
- G4dn – Cost-effective Tesla T4 GPU for ML inferencing and graphics.
- F1 – Leverage field programmable gate arrays (FPGAs) for rapid genomics, financial analysis and more.
Pros:
- Hardware acceleration from GPUs, FPGAs and AWS Inferentia chips.
- Excellent for machine learning, speech recognition, 3D rendering, video encoding.
- Dedicated real-time/batch ML inferencing and training instances.
Cons:
- More complex to configure and optimize for peak GPU/FPGA performance.
- Expertise in GPGPU programming often needed to maximize benefits.
- Can be more expensive than traditional EC2 instances.
Ideal Workloads:
Accelerated computing delivers huge performance gains for machine learning, computational fluid dynamics, real-time financial analysis, seismic imaging, video editing and graphics rendering.
Storage Optimized: Great for High Speed Sequential I/O
If your apps need to access huge datasets with high sequential read/write performance, storage optimized instances are designed just for you!
Some of the top storage optimized EC2 types include:
- I3 – High speed NVMe SSD delivering 3.3M IOPS and up to 64 TB capacity!
- D2 – Dense storage with 48 TB of HDD capacity and 12 Gbps direct attached SSD.
- Is5 – Lowest cost storage instance great for MapReduce distributed analytics.
Pros:
- Excellent disk throughput (16 Gbps) and IOPS performance from NVMe SSDs.
- High capacity local instance storage up to 64 TB.
- Scale to massive storage within a single instance.
Cons:
- Lower random IOPS than Provisioned IOPS EBS volumes.
- Not recommended for memory or compute-intensive apps.
Ideal Workloads:
Storage optimized instances excel for distributed file systems like HDFS, large scale NoSQL databases, data warehousing, log processing with MapReduce and business intelligence pipelines.
How Do You Select the Right EC2 Instance Type?
Now that you understand the pros, cons and ideal use cases for each EC2 instance family, how do you decide?
Here are 5 key tips I recommend to clients:
1. Analyze Performance Requirements
Profile your application‘s actual CPU, memory, storage and networking needs using tools like AWS X-Ray. This quantifiable data is invaluable for right-sizing instances.
2. Consider Data Volume and Throughput
The size and throughput rate of your datasets will indicate if storage optimized or memory optimized instances make more sense.
3. Evaluate Costs Upfront
Calculate hourly instance pricing and consider Reserved Instances for extended deployments. Balance performance with cost.
4. Architect For Elasticity
Design your architecture to leverage EC2 benefits like auto-scaling, spot instances and provisioned IOPS storage.
5. Benchmark and Iterate
Continuously benchmark performance as actual usage evolves, and right-size your instances accordingly.
It often pays to start small with general purpose instances for new apps, and scale up/out over time. Use AWS‘s instance recommendation tools as a starting point too.
Wrapping Up
With so many EC2 instance types and sizes, it can get overwhelming trying to determine the optimal choice! I hope this detailed overview of the 5 main instance families helps you narrow down the best option based on your workload‘s specific resource needs and performance profile.
Remember, allocate instance power wisely according to actual data from profiling tools to maximize value. Feel free to reach out if you need help designing and optimizing your cloud architecture!
