Amazon EMR pricing

This pricing is for Amazon EMR applications running on Amazon EMR clusters with Amazon EC2 instances.

The Amazon EMR price is added to the Amazon EC2 price (the price for the underlying servers) and Amazon Elastic Block Store (Amazon EBS) price (if attaching Amazon EBS volumes). These are also billed per-second, with a one-minute minimum. There are a variety of EC2 pricing options you can choose from, including On Demand (shown below), one-year and three-year Reserved Instances, Capacity Savings Plans, and Spot instances. Spot Instances are spare EC2 capacity available at up to a 90% discount compared to On-Demand prices. See Spot Instance price savings vs On Demand by filtering for “Instance types supported by EMR” on the Spot Instance Advisor page.

This pricing is for Amazon EMR on Amazon EKS clusters.

The Amazon EMR price is added to the Amazon EKS pricing or any other services used with EKS. You can run EKS on AWS using either EC2 or AWS Fargate. If you are using EC2 (including with EKS managed node groups), you pay for AWS resources (e.g., EC2 instances or EBS volumes) you create to run your Kubernetes worker nodes. See detailed pricing information on the EC2 pricing page. If you are using AWS Fargate, pricing is calculated based on the vCPU and memory resources used from the time you start to download your container image until the EKS pod terminates, rounded up to the nearest second. A minimum charge of one minute applies. See detailed pricing information on the AWS Fargate pricing page.

Amazon EMR pricing on Amazon EKS is calculated based on the vCPU and memory resources used from the time you start to download your EMR application image until the EKS Pod terminates, rounded up to the nearest second. Pricing is based on requested vCPU and memory resources for the Task or Pod.

Amazon EMR on AWS Outposts pricing is the same as cloud-based instances of EMR. Please refer to the AWS Outposts pricing page for details on AWS Outposts pricing.

With EMR Serverless, there are no upfront costs, and you pay for only the resources you use. You pay for the amount of vCPU, memory, and storage resources consumed by your applications.

With EMR Serverless, you create an application using an open-source framework version and then submit jobs to the application. As part of the job specification, you can provide the minimum and maximum number of concurrent workers, as well as the vCPU, memory, and storage for each worker. EMR automatically adds and removes workers based on what the job requires within your specified limits. The three dimensions of compute, memory, and storage for workers can be independently configured. You can choose from 1 vCPU, 2 vCPU, 4 vCPU, 8 vCPU, to 16 vCPU per worker, memory from 2 GB to 120 GB per worker in 1 GB to 8 GB increments, and storage from 20 GB to 200 GB.

You are charged for aggregate vCPU, memory, and storage resources used from the time workers are ready to run your workload until the time they stop, rounded up to the nearest second with a 1-minute minimum. If you set up your application to start workers at application startup, the requested workers will start when you start your application and end when you stop the application, or when the application remains idle.

Pricing details (compute and memory) – US East (N. Virginia)

Pricing is based on vCPU, memory, and storage resources used by workers, aggregated across all workers.

Pricing details (ephemeral storage)

20 GB of ephemeral storage is available for all workers by default—you pay only for any additional storage that you configure per worker.

Supported worker configurations

Duration

Duration is calculated from the time a worker is ready to run your workload until the time it stops, rounded up to the nearest second with a 1-minute minimum.

Additional charges

You may incur additional charges if your applications use other AWS services. For example, if your application uses Amazon Simple Storage Service (S3) to store and process data, then you will be charged standard Amazon S3 rates. If you move data from sources such as Amazon S3, Amazon Relational Database Service (RDS), or Amazon Redshift, you are charged standard request and data transfer rates. If you use Amazon CloudWatch, you are charged standard rates for CloudWatch logs and CloudWatch events.

Pricing based on US-East-1 pricing.

Suppose you run an Amazon EMR application deployed on Amazon EC2, and that you use one c4.2xlarge EC2 instance as your master node and two c4.2xlarge EC2 instances as core nodes. You will be charged for both EMR and for the EC2 nodes. If you run for one month, with 100% utilization during that month, and use on-demand pricing for EC2, your charges will be:

Master node:

EMR charges = 1 instance x 0.105 USD hourly x (100 / 100 utilized/month) x 730 hours in a month = 76.65 USD (EMR master node cost)EC2 charges = 1 instance x 0.398 USD hourly x 730 hours in a month = 290.54 USD (EC2 master node cost)

Core nodes:

EMR charges = 2 instance x 0.105 USD hourly x (100 / 100 utilized/month) x 730 hours in a month = 153.30 USD (EMR core node cost)

EC2 charges = 2 instance x 0.398 USD hourly x 730 hours in a month = 581.08 USD (EC2 core node cost)

Total charges = 76.65 USD + 290.54 USD + 153.30 USD + 581.08 USD = 1101.57 USD

Pricing based on US-East-1 pricing.

Suppose you are running an Amazon EMR-Spark application deployed on Amazon EKS. In this case, EKS gets its compute capacity using r5.2xlarge EC2 instances (8 vCPU, 64 GB RAM). Let’s assume that the EKS cluster has 100 nodes, totaling 800 vCPU, and 6400 GB of total memory. Let’s assume that that application utilizes 100 VCPUs and 300 GB of memory for 30 minutes.

Total Amazon EMR uplift charges for the job:

Total Uplift on vCPU = (100 * $0.01012 * 0.5) = (number of vCPU * per vCPU-hours rate * job runtime in hour) = $0.506       

Total Uplift on memory = ( 300 * $0.00111125 *0.5) = (amount of memory used * per GB-hours rate * job runtime in hour) = $0.1667       

Total EMR Uplift for the EMR job = $0.6727

Additional Costs

You pay $0.10 per hour for each Amazon EKS cluster that you create. You can use a single Amazon EKS cluster to run multiple applications by taking advantage of Kubernetes namespaces and IAM security policies. You can run EKS on AWS using either Amazon EC2 or AWS Fargate.

If you are using Amazon EC2 (including with Amazon EKS managed node groups), you pay for AWS resources (e.g. EC2 instances or Amazon EBS volumes) you create to run your Kubernetes worker nodes. You only pay for what you use, as you use it. There are no minimum fees and no upfront commitments. See detailed pricing information on the EC2 pricing page.

If you are using AWS Fargate, pricing is calculated based on the vCPU and memory resources used from the time you start to download your container image until the Amazon EKS pod terminates, rounded up to the nearest second. A minimum charge of one minute applies. See detailed pricing information on the AWS Fargate pricing page.

Suppose you submit a Spark job to EMR Serverless. Let’s assume that the job is configured to use a minimum of 25 workers and a maximum of 75 workers, each configured with 4VCPU and 30GB of memory. Consider that no additional ephemeral storage was configured. If your job runs for 30 minutes using 25 workers (or 100 vCPU) and was automatically scaled to add 50 more workers (200 more vCPU) for 15 minutes:

Total vCPU-hours cost = (100 * $0.052624 * 0.5) + (200 * $0.052624* 0.25) = (number of vCPU * per vCPU-hours rate * job runtime in hour) = $5.2624

Total GB-hours = (750 * $0.0057785 * 0.5) + (1500 * $0.0057785 * 0.25) = (Total GB of memory configured * per GB-hours rate * job runtime in hour) = $4.333875

Total EMR Serverless Charges = $9.596275

Additional Charges: If your application uses other AWS services such as Amazon S3, you are charged standard S3 rates.