AWS ECS Pricing: 3 Pricing Models and 5 Cost Saving Tips Full Explained

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AWS Elastic Container Service is an orchestration tool that manages and schedules containers. It can be used to run applications on a private or public cloud, or as part of an on-premises data center. AWS ECS allows you to build, deploy and manage applications using Docker containers without having to learn new tools or APIs.

What is AWS ECS

AWS Elastic Container Service (ECS) is a highly scalable, high performance container management service that supports Docker containers. With ECS, you can quickly and easily run applications in containers on AWS, with HTTP integration and scaling functionality built in.

A container is a bundle of code along with all of its dependencies—it has nothing to do with virtualization or the cloud. It’s much lighter than an entire operating system because it includes only what is needed to run one program or application on one server or computer system. Containers are used for many different things including:

  • Deploying applications at scale to Docker hosts
  • Exposing ports from your application so that they’re accessible from outside your host
  • Allowing you to develop locally using the same tools as production

How is AWS ECS Priced?

ECS is priced per container instance hour, regardless of the number of containers running on a single instance. The cost is the same whether you run one or 100 containers on an ECS task definition. The only thing that changes is how many hours you run your application for. To see how much your application will cost to run on ECS, multiply the number of instances with their respective hourly costs by the number of hours your application runs in a month:

  • 1-2 months = 0-1 container instance * $0-$0.10 * 24 hours/day * 30 days = $1-$2 per instance/month
  • 3-4 months = 2-3 container instances * $0-$0.10 * 24 hours/day * 30 days = $17-$28 per instance/month

Amazon ECS Pricing for 3 Deployment Options

ECS is priced based on your chosen deployment option.

  • EC2 launch type model: You pay only for the number of tasks you run, and you pay per container-hour after that. This option is useful if you are running a task that takes more than five minutes to complete and/or has many containers in its service (for example, an application with multiple microservices).
  • On-premises Outposts: With this option, ECS manages your clusters and tasks within Amazon Elastic Compute Cloud (EC2) instances that reside in your own data center or cloud environment. In this case, you don’t pay for any AWS services or infrastructure costs associated with the ECS service itself; instead, those costs are borne by you as part of maintaining the underlying EC2 instance hosting your application components..
  • Fargate launch type model: With this option, there are no additional charges beyond those associated with using EC2 instances for containers orchestrated by ECS managed services—you simply pay for what you use according to standard hourly pricing

1. AWS ECS EC2 Launch Type Model

The EC2 launch type is the most expensive option, but it’s also the most flexible and powerful. EC2 instances are virtual machines that run on bare metal, so your containers can use them directly without running inside a container management system like Kubernetes or Docker Swarm. This means you don’t have to worry about managing large numbers of containers across multiple hosts; instead, you can focus on deploying one container per instance—and if you need more power, just spin up another instance (or two). However, this also means that as your application scales up, so does its cost: each new instance will cost money every hour until you terminate it.

The major benefit of using ECS over other cloud platforms is that there are no extra charges for using multiple availability zones or clusters across regions—you can run as many clusters as needed without paying extra for them.

2. AWS ECS on AWS Outposts

AWS Outposts is a new service that allows you to run a managed AWS ECS cluster on premises. This can be useful for organizations that want to keep their data within physical walls and have security and compliance requirements that restrict the use of public cloud services.

With AWS Outposts, you get all the benefits of running your ECS clusters in public cloud with none of the overhead: no hardware or software maintenance, no patching or upgrades to worry about—just pay for what you use and forget about it! Unlike other solutions such as Mesosphere DC/OS, which require extensive configuration and management, AWS Outposts uses Amazon Elastic Container Service for Kubernetes (EKS) by default with no additional work required by you or your team.

3. AWS ECS Fargate Launch Type Model

Fargate is a new launch type in AWS ECS. Fargate is a serverless container service that offers the flexibility of ECS without the need to manage the underlying infrastructure. This launch type can be used with your existing clusters, but it requires you to have an existing VPC and security groups for your containers to communicate with each other. If you don’t have an existing VPC, then you can use a managed VPC instead – this gives you automatic public IP address allocation and DNS routing from Amazon Route 53 (if needed).

It’s important to note that Fargate does not currently support Kubernetes or Swarm containers, only Docker images hosted on S3 buckets or ECR repositories are supported at this time.

The 7 ECS Cost Optimization Tips

This article presents the 7 AWS ECS cost optimization tips that you should consider:

  • Fargate Spot Instances
  • Fine Tuning Load-Based Auto Scaling
  • Scheduled Auto Scaling
  • Mixing Pricing Models (e.g., F1, T2 and T3)
  • Application Load Balancer

1. Fargate Spot Instances

Fargate offers a unique pricing model in which you pay only for the compute resources that you actually use. This is accomplished through Spot Instances, which are essentially offers made by AWS to provide compute capacity at a discounted rate. To put it simply: If you can find an instance type for less than its on-demand price, Fargate will charge you the lower amount. It’s great news for those looking to save money!

On top of this, Fargate allows you to use spot instances for your container tasks—something we haven’t seen from other ECS services. But beware: these instances don’t have access to storage volumes or persistent disk storage, so your data may be lost if it isn’t copied out of this temporary storage space before it expires (which can be as little as 15 minutes).

2. Fine Tuning Load-Based Auto Scaling

Auto Scaling, which we discussed in a previous section, is a powerful tool for automatic scaling. However, if you need fine-grained control over the instance counts in your cluster, then Auto Scaling may not be the best choice.

Instead of setting a static minimum and maximum number of instances in your cluster (which is what Auto Scaling does), ECS offers another option: load-based scaling. The idea behind this method is that instead of creating or destroying EC2 instances based on fixed values such as CPU usage or memory utilization—as with Auto Scaling—you create or destroy them based on actual load values recorded by API calls against your container instances. This ensures that you get exactly enough capacity during busy periods while reducing costs when demand drops off.

To set up load-based auto scaling in ECS, simply specify the desired minimum and maximum number of running instances at launch time:

3. Scheduled Auto Scaling

You can also schedule Auto Scaling. This is an option that’s enabled by default in an Amazon ECS cluster. With scheduled scaling, you can specify a CRON expression to scale up or down your cluster at regular intervals. If you want to scale your instances based on CPU utilization or memory utilization, you can disable scheduled scaling and use the scale-up policy instead (see above).

However, if you want to customize how many instances you want within certain bounds and have those changes occur at specific times during the day or week, then this feature would be very helpful for determining when scaling occurs.

4. Mixing Pricing Models

Mixing pricing models is a good way to save money, reduce risk and optimize your infrastructure.

If you’re migrating an existing application to AWS, or building a new application for the cloud, it’s possible that using multiple pricing models will be the best choice for your organization.

5. Application Load Balancer

The Amazon Web Services Elastic Container Service (ECS) allows you to easily run and manage Docker containers on a cluster of EC2 instances. In order to scale your application, you can use an Application Load Balancer (ALB) to distribute traffic across multiple instances in the ECS service. This makes it easy for you to add extra capacity as needed without having to change any other components of your system.

ALBs are also helpful when migrating from one instance type or availability zone (AZ), because they allow you to gradually introduce new services into production by routing requests from the ALB onto them over time.

6. Right-Sizing Containers

You can right-size your containers using the following steps:

  • Understand how many containers you need for your workloads. The goal is to have enough containers running at any given time so that they don’t experience “cold starts,” but not more than necessary.
  • Choose the right container size. A standard instance type provides a relatively easy way to calculate how many instances are needed for your application needs, but it doesn’t work well if your application requires different kinds of instances with different performance characteristics (for example, CPU-intensive versus memory-intensive). In this case, determining which instance types meet those needs may be more difficult and require some trial and error before finding something that works well in production.
  • Understand the difference between memory limits and CPU limits on each instance type offered by Amazon EC2 Auto Scaling groups help automate scaling requests from ECS Tasks according to preconfigured thresholds or rules; these rules define automatic scaling policies such as when new instances are launched or terminated based on current conditions such as average CPU utilization over time

7. Tagging

Tagging is a way to label and organize your resources in ECS. It’s a great tool that can help you understand costs, manage resources, and more.

Use tags to categorize your services in ECS so you can better manage them. This can be helpful when you need to filter costs by tag, or if you want to know how many instances of a certain service are running on the cluster. For example, let’s say you have an application that runs off of two different containers: one container monitors your servers behind an API (which would be tagged as “API”), while another container provides customer support via chat (which would be tagged as “Chat”). If one container fails at 2 AM but not the other, it’s easy enough for us to see which one failed based on their tags:

Optimizing AWS ECS Pricing with Ocean from Spot by NetApp

Optimizing AWS ECS Pricing with Ocean from Spot by NetApp

If you haven’t explored the benefits of Spot instances, now is the time. Spot instances are a great way to save money and gain flexibility on your EC2 instance usage. By using Spot instances, you can bid against other customers who want to use those same resources. If your bid is higher than the current price of that resource, then you’ll win a spot instance and pay only what it costs at that moment—which may be significantly lower than usual prices for that region. The downside? It’s not guaranteed; if no one else bids for that same resource at the current price or above it, then there won’t be any spots available for your use.

1. Automatically provision

The first benefit of using ECS is its ability to automatically provision the right number of resources. This means that your instances are created with just enough memory and CPU power to serve their needs, and no more. It also means that you’re not paying for unused capacity.

Second, ECS can automatically provision the right type of resources for your application. When an instance boots up in AWS, it’s essentially a blank slate—you have to install everything from scratch (or spin up another one). With ECS, you apply your desired operating system image and configure all necessary settings via Dockerfile instructions before launching an instance from it with ECS. This makes things much easier on both sides: you don’t have to worry about keeping track of all those details yourself; and AWS doesn’t have any extra work beyond what’s needed simply by making sure each node has a copy of Docker installed—a task that takes just seconds per instance regardless how many there are!

The third aspect worth noting here is how auto-scaling works under this model: if more containers need running than currently exist on available nodes, some will be spun up immediately as needed until demand decreases again; likewise conversely when there aren’t enough available containers on any given node anymore because too many others were created simultaneously then shut down because they weren’t necessary anymore due ecs-autoscaling flag being set true“

2. Leverage predictive AI

Predictive AI is a powerful tool that uses machine learning to predict how your AWS resources are being used and helps you optimize your AWS ECS pricing. Predictive AI provides insight into the following:

  • Whether you’re overpaying for your AWS ECS services
  • How much you could save by optimizing the number of containers in each auto-scaling group or by adjusting other parameters like CPU or memory limits, etc.
  • Which instances and auto-scaling groups should be shut down first in case of an unexpected event (for example, if a fleet becomes unavailable due to failure).

3. Auto scaling mechanisms

Auto Scaling is used to automatically adjust the number of instances in a group based on user-defined rules. The Auto Scaling feature automatically scales your application based on any rule you define. For example, you can configure an Auto Scaling group to launch additional instances if CPU utilization reaches 80 percent and terminate instances if free memory falls below 10 percent.

The following are the three mechanisms used by ECS for autoscaling:

  • Autoscaling groups – A set of EC2 instances that can be independently scaled according to predefined policies.
  • Autoscaling policies – Define scaling behavior when specific events occur (for example, increase or decrease depending on the load).
  • Autoscaling launch configurations – Specify what needs to be done when scaling up or down (for example, installing software packages or setting up load balancers).

4. Monitor and analyze

To get a better understanding of how your ECS clusters are performing, you can monitor and analyze them with AWS CloudWatch. You can use Amazon X-Ray to collect more detailed information about the performance of individual containers and processes.

Using Amazon ECS metrics will give you a better understanding of the state of your cluster. For example, if an ECS cluster has been running for a while and there are lots of pending tasks in its queue, this might indicate that there aren’t enough EC2 instances available to perform these tasks. By monitoring the various metrics provided by Amazon CloudWatch Logs (described below), you’ll be able to identify any issues that need fixing before they have time to impact production systems.

5. Manage different workloads

The final thing to know about AWS ECS Pricing is that it’s based on the number of containers you run and the amount of memory and CPU they consume.

If you have a workload that uses more than one container, then each container will be counted separately for billing purposes.

For example, if your application uses three containers (one for each component), then this will count as three separate units of usage toward your monthly pricing.

ECS uses many of AWS’s existing services so it is wise to understand each of those costs as well

ECS uses many of AWS’s existing services, so it is wise to understand each of those costs as well.

  • Elastic Load Balancing (ELB) charges for incoming traffic and egress traffic from your container instances
  • A NAT Gateway is required if you are using a private subnet because ECS does not support NAT Gateways on public subnets. If you do use a NAT Gateway, then you will also need to enable port mapping for each container instance that is running in the private subnet through which your NAT Gateway receives traffic. This allows ECS to know which container instance should be receiving incoming requests from ELB
  • You must pay for an Internet gateway if you want to access external services outside of AWS VPCs

Conclusion

The bottom line is that ECS can be a cost-effective way to manage your applications on AWS, but it’s important to understand the full costs so you can optimize your usage.

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