Discover the best time to use ECS, EC2, S3, and Lambda in your AWS environment — and the pros and cons of each service.
There are over 200 fully-featured services within the Amazon Web Services (AWS) ecosystem. AWS Elastic Compute Cloud (EC2), Elastic Container Service (ECS), Amazon Lambda, and AWS Simple Storage Service (Amazon S3) are some of the most critical services you should become familiar with.
If you use Amazon Web Services, it is likely that you currently use at least one of these core AWS features.
The benefits they provide may seem to overlap sometimes. But each service has unique capabilities that you may want to use in some cases and not so much in others.
We’ve covered Amazon ECS vs. EKS vs. Fargate for managing and deploying containers before. So this AWS core services guide will explain how Amazon EC2, Lambda, ECS, and S3 compare and when you should use each.
Table Of Contents
Amazon EC2 is a web-based service that provides scalable computing power on Amazon Web Services. AWS refers to servers as EC2 Instances. We will discuss how EC2 instances differ from traditional virtual machines shortly.
Your organization can spin up virtually limitless virtual machines (VMs) in the cloud instead of maintaining, optimizing, and connecting your own computing hardware to an on-premises network. You can spin up an EC2 instance for any purpose, such as creating a SQL database.
It also offers the unique feature of increasing or decreasing the computing capacity you need to run various business processes. This is referred to as scaling EC2 instances.
EC2 instances on AWS can also run a choice of operating systems and AWS services. It supports various Linux variants, Windows and macOS, as well as custom OSes.
Additionally, VM Export/Import enables you to upload on-premises VM images to Amazon EC2. The system converts them to EC2 instances so they can be readily available for cloud computing.
EC2 instances differ in several ways from regular virtual machines (VMs).
There are Auto Scaling Groups built into EC2 instances. Thus, EC2 instances can increase and decrease their computing power as needed.
Whenever your virtual CPU, RAM, and storage requirements approach their limits, EC2 instances add additional computing power to handle the increased demand before users experience any lags or outages.
AWS charges you per hour for the compute capacity you use. Unlike traditional virtual machines, you do not pay a minimum fee. In addition, you don't have to provision a minimum computing capacity in advance.
Because EC2 instances do not share memory between instances, they offer superior performance and availability.
However, EC2 instances only keep data when they are running. Contrary, you can switch VMs on and off without losing data as long as the VM is on the same physical server. For this reason, AWS offers Amazon Elastic Block Store (EBS) to make EC2 instances more durable. You can also backup EC2 data to Amazon S3.
AWS's Elastic Container Service (ECS) is a fully managed container orchestration service. By using ECS, you can focus on building your application rather than maintaining the infrastructure on which it will run.
ECS resources are scalable, just like EC2. However, ECS scales container clusters on-demand, rather than scaling compute resources like EC2.
A major benefit of ECS is that it supports Docker containers. For managing and deploying Kubernetes containers on AWS, you should consider Amazon's Elastic Kubernetes Services (EKS).
ECS orchestrates Docker containers, but the containers run on EC2 instances. EC2 instances provide the virtual machines that Docker containers need in order to run.
AWS released ECS Anywhere in May 2021. ECS Anywhere is the on-premises version of Amazon ECS. By using ECS Anywhere, you can run, secure, and scale Docker containers on customer-managed infrastructure.
The following image shows how ECS Anywhere works:
With ECS Anywhere, AWS engineers manage the backend, so you don't have to worry about operating cluster management software. That's a major advantage if you have wanted to manage containers on your own infrastructure.
However, you can still go serverless by directing the tasks and services to AWS Fargate. Here's our guide to ECS vs EKS vs Fargate for more information.
S3 is a scalable cloud storage service that engineers use in the AWS public cloud for object storage. An object comprises data, metadata, and its assigned name (key). The service hosts these components in S3 buckets.
Using Amazon S3, engineers can store, retrieve, and back up any amount of data from anywhere, anytime, and on any device. Also, you can choose which type of S3 storage to use based on your business needs, just as you can choose which EC2 instances to use.
S3 is highly scalable, available, affordable, secure, durable, and simple to use through a web-based interface, the Amazon S3 Console.
EBS is the high-availability storage volume for EC2 instances at the block level. As mentioned earlier, EC2 instances keep data for as long as they are running. When using EBS, you can keep data after an instance is terminated or shut down — just as you would with a regular VM.
The Amazon Elastic File Service (EFS) option is a scalable file storage service that enables you to store data from multiple EC2 instances. So it's the ideal solution for sharing files between EC2 instances but with high performance and automated features. This differs from EBS, which is high-performance and scalable storage paired with a single EC2 instance.
You can also use EFS to store code and media files across the AWS region boundary with VPC peering. Like EBS, EFS is optimized for EC2 instances, which means it is not ideal for complex querying tasks. That's S3's job.
S3 is a massive, cloud-based hard drive for storing user files and backups long-term. Moreover, S3 storage is not restricted to only serving EC2 instances. Other AWS services rely on it for data storage as well. A service can access S3 storage either programmatically or directly.
As an example, you can use it with AWS Lake Formation as a data lake. You can use this when collecting large quantities of data to feed your Artificial Intelligence system or Machine Learning algorithm.
Engineering teams often use Amazon S3 to archive data they do not wish or have the resources to analyze immediately. Data archives let you store a lightweight version of a file rather than a larger version that takes up more space and costs more to store.
Amazon S3 allows you to store archived data for years at a lower cost than EBS and EFS. The following image illustrates the differences between object storage in S3 and block-level storage in EBS.
Instead, you write code in a supported language, such as Node.js, Go, Java, or Python, to create a Lambda Function. The function concept is similar to a small application written in one of the supported languages. Every function runs in its own container.
The AWS Lambda interface allows you to upload a container image or Zip file which contains the lambda function. In this way, Lambda will allocate your workload with the exact compute power it needs to run an event or incoming request when various triggers occur.
To understand how Lambda works, you have to know what serverless computing is. Serverless computing refers to a cloud-native application development approach in which engineers do not have to manage servers or clusters.
Cloud service providers, such as AWS, manage both the scaling of apps and the cloud infrastructure. Amazon Lambda does not give you access to the infrastructure. Furthermore, when you upload code through Lambda, it is deployed into a container. After that, AWS creates, deploys, and manages the containers on your behalf.
By now, you've probably noticed that all four AWS services have unique use cases.
S3 is also not directly comparable to the rest of these core AWS services. Meanwhile, there has been a growing trend of DevOps engineers comparing EC2, Lambda, and ECS. To compare the three, we'll examine ideal use cases, performance, security, and cost.
Compared to AWS Lambda, EC2 runs a full copy of the operating system and all the necessary hardware to run the OS. Managing and provisioning the EC2 environment is therefore required.
On the other hand, Lambda only needs a few system resources and dependencies to run a specific function. AWS handles everything else.
The EC2 platform, however, gives you a great deal of control over your application and its environment. As an example, you can select what type of EC2 instances (AWS virtual machines) to run the app on, from the CPU to the RAM to the storage options.
Using and optimizing EC2 instances requires advanced skills, time, and even money. It simplifies using EC2 instances by supplying preconfigured instances and by letting engineers control instances with APIs or web interfaces. In addition, EC2 instances automatically scale during peak times and decrease during off-peak times, boosting performance and saving money.
These are some of the ideal uses of EC2 instances:
AWS configuration is a lot of work for some companies, especially for companies with one or two engineers, inadequate AWS skills in-house, or companies that must use the public cloud as quickly as possible. Amazon knows this, which is why it created ECS and Lambda.
AWS Lambda relieves engineers of infrastructure and scalability concerns thanks to its fully managed serverless computing service.
You can use Lambda to:
So, is AWS Lambda better than EC2? You need to take into account your organization's unique needs when determining the right solution.
ECS is not a direct competitor here because you can schedule and deploy Docker containers both in serverless mode and on EC2 servers. Moreover, you can skip the server selection process by using AWS Fargate with ECS.
We have already covered some S3 use cases and why you might prefer S3 over EBS or EFS as a storage option. In the next section, we will compare EC2 with AWS Lambda in more detail.
They both provide computing services, albeit they do so differently. So, is AWS Lambda the better choice for you, or is it EC2?
There are several powerful differences you need to be aware of before choosing EC2 or AWS Lambda.
Lambdas are always available, unlike EC2 instances, which become available on-demand. You will not be charged for Lambdas that you haven't used yet.
You can resize the computing capacity of both Amazon EC2 and AWS Lambda to power up your system during high loads and save money when you are not using it. Lambda instances let you set the maximum number of concurrently executing functions you want to scale up or down, just like EC2 instances.
EC2 requires you to define the minimum, desired, and maximum capacities you need manually. You can ease the process using Auto Scaling Groups.
When your application's load reaches the maximum threshold, Lambda can continue scaling up by 500 instances per minute instead of slowing it down. After the load decreases, Lambda can scale down to zero instances in order to conserve computing resources.
In contrast, EC2 instances require manual adjustment every time your application load reaches maximum utilization. EC2 instances don't automatically scale lower than your pre-set threshold either.
Lambda is not perfect either. If you invoke a Lambda function after the API Gateway timeout of 30 seconds, you will receive a 5XX error from API Gateway as a result. The only way to fix this issue is to keep resubmitting the request until it is approved.
Even so, scalability is still one of the most significant advantages of AWS Lambda.
Lambda’s other advantages include that AWS manages most of the infrastructure backend, such as selecting and maintaining the most suitable operating systems. This and other infrastructure configuration, administration, and optimization tasks will be your job as a DevOps engineer using EC2.
Because AWS Lambda functions are stateless, malicious agents have a hard time growing on them over time. In addition, AWS engineers monitor, patch, and maintain infrastructure security on your behalf. This is a good thing because:
An attack, such as a DDOS attack, would be no match for AWS Lambda, which would just scale up to accommodate the load, allowing your application's workflows to persist through the attack.
The downside is, Lambda instances automatically scale beyond set limits, so something like this can increase your AWS bill quite quickly. Without a cloud cost intelligence platform to detect and alert you to such cost anomalies, you would quickly go over your AWS budget for the month.
EC2 allows you to implement security best practices at the instance level. A single EC2 instance can have several security layers. Still, the security layer determines what traffic to route to in what instance. There may also be multiple connectivity protocols to follow, such as UDP, TCP, and ICMP.
You also need to create valid policies to have the appropriate permissions. Besides, you still need to set up multiple configurations to prevent your workload from deteriorating in performance and availability in the event of a DDOS attack.
You can see that managing security in EC2 instances is not only time-consuming but also opens up much room for human error, opening your applications up to even more attacks or performance degradation. While AWS Shield can ramp up your defenses, you might not have good cost visibility into your infrastructure while this occurs, leading to cost overruns.
As both Lambda and EC2 offer a pay-as-you-go pricing structure, they are cost-effective alternatives to traditional VM environments.
Lambda charges by the number of requests served, and by the length of time it takes to execute code. You can calculate Lambda pricing here to see what you can expect to pay in a month.
EC2 charges by the second. EC2 hourly pricing depends on several variables, including:
It doesn't matter if the running instance executes or not. Having an instance running is what counts.
This is one reason many organizations struggle to control and reduce AWS spend. Cost anomalies occur in the absence of visibility into EC2 instances or Lambda functions. The costs add up undetected and end up eating the organization’s gross margins.
Autoscaling, high-availability, and pay-as-you-go models are all excellent AWS EC2 and Lambda features, but they can also increase your AWS bill if you don't keep an eye on them.
CloudZero makes it easy for engineers to see what their EC2 vs. AWS Lambda decisions mean in terms of costs. CloudZero’s cloud cost intelligence platform enables engineers to see the cost impact of their decisions and even drill into cost data from a high-level down to the individual components that drive their organization’s cloud spend. With this insight, engineering teams can see exactly what AWS services cost you them the most and why.
CloudZero also detects cost anomalies at the instance level and alerts the appropriate team members via Slack instantly. By receiving an early warning, you can reduce the risk of going over budget or eroding gross margins.