So you‘re looking to launch cloud applications on Amazon EC2, but overwhelmed by the alphabet soup of instance type options? As an experienced cloud architect, I‘ve helped dozens of clients navigate this decision. In this comprehensive guide, I‘ll demystify EC2 instance families to help you pick the optimal one based on performance needs and budget.
What Are EC2 Instance Types Exactly?
First, let‘s quickly level-set on terminology. EC2 stands for Elastic Compute Cloud, which is Amazon‘s infrastructure for running virtual machine instances. Each EC2 instance type offers a different balance of compute, memory, storage, and networking capacity to suit varied workloads.
Think of an EC2 instance as a customizable remote computer you can spin up to host your applications. By choosing different configurations of CPU, RAM, disk space, etc, you effectively get access to an adaptable, virtualized server infrastructure.
Image source: CloudCharts
As this chart highlights, available EC2 instances cater to general purpose, optimized, accelerated, and custom hardware use cases.
Now let’s explore each EC2 instance family so you can select the right fit based on your apps‘ performance needs. I‘ll call out tips along the way on best practices when provisioning instances.
Diving Into General Purpose EC2 Instances
General purpose EC2 instances offer fixed configurations balancing compute, memory, and network resources. This makes them suitable for a diverse range of workloads from web apps to enterprise systems.
For example, an M5.2xlarge instance gives you 8 vCPUs, 32GB RAM, and upto 10 Gbps network bandwidth. The M5 generation also supports storage choices from fast NVMe SSDs to high capacity HDDs.
Compared to specialized instances, general purpose offerings tend to be more cost effective if app needs are moderate. Their predictable resourcing can comfortably run most typical workloads.
💡Pro Tip: General purpose instances are a safe starting point for prototyping and testing new apps with average requirements. Scale up to optimized or accelerated families later if necessary.
Let‘s compare the pros and cons of using general purpose EC2 instances:
Pros
- Cost-efficient for moderate resource needs
- Quick and easy to provision
- Flexible with configurable storage
- Easy to scale vertically and horizontally
- Familiar infrastructure for lifts-and-shifts
Cons
- Not purpose-built for any single workload
- Can get expensive at greater capacities
- Network performance lags dedicated options
- Adding GPUs not directly possible
To summarize, general purpose instances offer the simplest onramp for getting started on EC2 across a spectrum of use cases – from dev/test environments to enterprise apps.
💡Best for: Development, test, and production environments with average resourcing needs. Web and application servers, microservices, containers, code repositories, CMS, ecommerce sites etc.
Now let‘s move on to observing what optimized EC2 families bring to the table.
Going Lean and Mean with Optimized Instance Types
Once apps pass proof-of-concept and usage ramps up, resource requirements usually grow. This is where optimized EC2 instance types come in handy. By doubling down on compute, memory, or storage, they deliver leadership performance for targeted workloads.
You have a choice of going the compute optimized route for extra CPU muscle or choosing memory optimized instances to effortlessly handle fast in-memory processing of large datasets.
Storage optimized offerings focusing on I/O throughput are also available. Purpose-built designs and specialization allow optimized families to hit higher benchmarks.
💡Pro Tip: Profile apps routinely via load tests to pinpoint resource bottlenecks. Optimized instances can then provide cost-efficient scale.
Let‘s inspect the trade-offs of leaning into EC2 specialization:
Pros
- Peak efficiency for designated workloads
- Leverage latest hardware innovations
- Impressive performance at scale
- Surprisingly cost-competitive
Cons
- Lock-in to asset family risks stranding
- Switching instance types breaks continuity
- Capacity planning needs accuracy
- AWS Graviton chips limit OS support
In summary, workload-targeted optimization unlocks order-of-magnitude efficiency gains over generalist configurations – provided your capacity forecasts are sound.
💡Best for: Purpose-built scale-out workloads with known architectural patterns like high performance compute, video encoding, real-time big data analytics, and database caching.
Next up, we‘ll see how hardware accelerators supercharge EC2 performance further still.
Kicking Into High Gear with Accelerated Computing
For select workloads like machine learning, graphics, and data pattern matching, hardware accelerators achieve blazing speeds. EC2 provides preconfigured accelerated computing instances with GPUs, FPGAs, and AWS Inferentia chips on tap.
These ASICs and vCPU coprocessors offload specialized processing – be it deep learning matrix math or 3D graphics rendering – that is extremely taxing for regular CPUs. Such hardware acceleration makes previously unthinkable breakthroughs possible on cloud infrastructure.
From crunching satellite imagery datasets using GPUs to financial risk modeling with FPGAs, real world use cases abound. By mixing and matching instance sizes to attach the right accelerators, you can massively speed up complex computations.
💡Pro Tip: Validate if accelerator gains offset added costs before committing long term. Favor Spot instances to contain expenses.
How do the pros and cons balance out when harnessing accelerated computing?
Pros
- Order-of-magnitude speedup of heavy workloads
- Future-proof infrastructure for emerging technologies
- Enables bleeding edge innovation like AI productization
- More cost effective than supercomputing resources
Cons
- Steep learning curve around hardware optimization
- Vendor dependencies can yield lock-in
- Exotic hardware failures harder to debug
- Upfront development overheads amortize slowly
In a nutshell, specialized accelerators unlock unprecedented performance for suitable apps, albeit at increased sophistication.
💡Best for: Machine Learning training and inferences, speech and image recognition, computational fluid dynamics, financial risk modeling, medical imaging, graphics rendering.
Finally, let‘s analyze how storage optimized instances complete the EC2 lineup…
Leveling Up Database Performance with Storage Optimized
If your applications need to sustain screaming fast I/O throughput while working with terabytes of data, storage optimized instances are purpose-built for the job.
Delivering upto 30 TB of NVMe SSD storage and 64 vCPUs for parallel processing, these instances can sequentially scan vast datasets far quicker thanks to 100K+ IOPS performance.
From rapidly ingesting web clickstream logs to running complex analytical queries across petabyte scale data lakes, storage optimized EC2 instances turbocharge big data and database workloads.
And thanks to support for high memory configurations, real-time analysis of hot datasets retrieved from storage becomes possible.
💡Pro Tip: Evaluate switching from external EBS volumes to NVMe instance storage for 4x better IOPS and lower latency.
How does specializing for storage augmentation play out?
Pros
- Lightning fast I/O perfect for large databases
- Streamlines big data transformations
- Enables complex analytical workflows
- Cost effective persistence layer
Cons
- Inflated costs for overprovisioning
- Scaling capacity requires planning
*DRAM ratios lag other families - Regional instance limits
In summary, if your apps are held back by slow database performance and lengthy analytical queries, storage optimized instances can accelerate insights generation from vast datasets.
💡Best for: High speed database systems with 10K+ IOPS needs, data warehousing and ETL, distributed file systems.
Now that we‘ve surveyed specialized EC2 families, let‘s wrap up with bare metal customization.
Taking Full Control with EC2 Bare Metal Instances
Lastly, for unbridled visibility and control without virtualization overhead, EC2 offers bare metal instances. Here you run apps directly on physical servers dedicated to your needs.
This hands-on approach allows fine tuning infrastructure to exact specifications without any resource sharing or neighbors to interfere. And you retain root privileges to directly configure networking, storage etc.
Such isolation and precision comes at steeper costs but enables running tier 1 production apps at low latency and consistent throughput. Bare metal is popular for high performance computing clusters, large scale NoSQL databases, and legacy middleware supporting modern cloud-native stacks. Compliance demands also drive adoption.
💡Pro Tip: Validate if bare metal consistency warrants forgoing instance flexibility before committing.
How does the bare metal approach compare?
Pros
- Granular visibility and control
- Eliminates virtualization penalties
- Prevents resource contention with multi-tenancy
- Superior and consistent performance
Cons
- Manual administration overhead
- App migration efforts mandatory
- Costly with zero flexibility
- Locked into provisioned capacity
So in summary, although inflexible and expensive, bare metal instances ensure absolute stability, speed, and control for applications requiring it.
💡Best for: Tier 1 production systems where consistency matters. HPC clusters, NoSQL DBs, legacy Java apps, high frequency trading platforms, streaming video origin serving.
Now over to you! Based on the EC2 exploration above:
- Which instance family aligns closest to your apps – general purpose, optimized, accelerated, or bare metal?
- What are the critical performance factors – CPU cycles, memory access, storage IOPS or raw network throughput?
- Have you identified expected capacity needs through load tests already?
- How might instance choices evolve as usage scales up over time?
Analyzing your workloads first allows intelligently picking EC2 shapes and families for optimal price-performance.
I hope mapping out the EC2 universe helps choose the right instance type for your cloud journey! Do ping me any followup questions.
FAQs About EC2 Instance Types
Here are answers to some commonly asked questions around EC2 instances:
Q: Why are there so many EC2 instance types and sizes? which one should I choose?
A: AWS offers a wide range of instance types to meet varied customer needs flexibly. Analyze your app‘s technical profile first – factors like required CPU, memory, storage, networking resources. This indicates which instance family can best fit the performance envelope. Right size instances to workload capacity so you provision adequately but not overpay either.
Q: Can I change the EC2 instance type for my existing instances?
A: Yes absolutely! One benefit of cloud elasticity is being able to pivot server configurations without affecting applications, thanks to decoupled storage and networking. Use the AWS console or CLI to stop an instance, change its type, and restart it. Certain families may need more migration care.
Q: Why are bare metal instances more expensive than even the largest EC2 instances?
A: You pay a premium for bare metal isolation to guarantee consistent ultra low latency performance unaffected by multi-tenant neighbors. Total control and visibility into underlying physical servers also carries value for certain applications like HFT platforms.
Q: How to decide between provisioning reserved, on-demand or spot instances?
A: Know your workload patterns first. Reserved instances allow upto 75% discounts for steady state production apps. Spot instances help further optimize costs for batch jobs with flexible running time. On-demand works best for spiky, unpredictable workloads where reserving capacity upfront is hard.
I hope these answers help provide more context. Feel free to follow up with any other questions!
