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Elastic MapReduce
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    Billing Overview

    Last updated: 2020-08-06 16:24:20
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      Billing Mode

      Pay-as-you-go: the billing mode of all nodes in a cluster is pay-as-you-go. For more information on node types, please see Node Type Description.

      When you purchase an EMR cluster, the unit price of the cluster will be displayed by hour. During bill settlement, the price will be calculated based on the number of seconds of actual usage, and the fees will be rounded to 2 decimal places. The billing starts at the time point when the cluster is created and ends at the time point when termination of the cluster is completed.

      When you purchase a pay-as-you-go cluster, the amount of 2 hours' usage fees for the current configuration will be frozen in advance. Bills will be settled on the hour (Beijing time), and fees will be deducted based on the actual usage time of the cluster in the previous hour.

      When the configuration of a pay-as-you-go node is adjusted, the amount frozen during purchase will be unfrozen, and a new amount of 2 hour's usage fees will be frozen at the latest unit price. When you terminate a pay-as-you-go cluster, the system will unfreeze the frozen amount.

      Price Description

      EMR provides elastic computing cluster capabilities, so that you can select and combine multiple EMR specifications in a customized manner. EMR fees will be charged for all nodes in each cluster.

      The prices of nodes in Beijing, Shanghai, and Guangzhou regions are as listed below:

      1. The prices on our official website are subject to change. Please check periodically for the latest prices.
      2. For the prices of disks, please see CBS Price Overview.
      3. The following prices are for configuration fees of CPU and memory, excluding fees of images, system cloud disks, data cloud disks, and bandwidth.
      4. Some models may be sold out in the actual situation. For more information, please see the model list on the purchase page.

      Tier-1 pay-as-you-go fees (USD/hour)

      Model CPU Memory (GB) Beijing, Shanghai, and Guangzhou
      Standard S2 2 4 0.12
      Standard S2 4 8 0.23
      Standard S2 4 16 0.34
      Standard S2 8 16 0.45
      Standard S2 8 32 0.67
      Standard S2 12 24 0.67
      Standard S2 16 32 0.89
      Standard S2 16 64 1.33
      Standard S2 24 48 1.33
      Standard S2 24 96 1.99
      Standard S2 32 64 1.77
      Standard S2 32 128 2.65
      Standard S2 48 125 3.05
      High-IO I2 8 16 0.45
      High-IO I2 8 32 0.67
      High-IO I2 12 24 0.67
      High-IO I2 16 32 0.89
      High-IO I2 16 64 1.33
      High-IO I2 24 96 1.99
      High-IO I2 32 64 1.77
      High-IO I2 32 128 2.65
      MEM Optimized M2 2 16 0.23
      MEM Optimized M2 8 64 0.91
      MEM Optimized M2 12 96 1.36
      MEM Optimized M2 16 128 1.81
      MEM Optimized M2 24 192 2.71
      MEM Optimized M2 32 256 3.61
      Computing C2 4 8 0.33
      Computing C2 4 16 0.5
      Computing C2 16 32 1.31
      Computing C2 16 60 1.89
      Computing C2 32 120 3.78
      Big Data D1 8 32 0.72
      Big Data D1 16 64 1.44
      Big Data D1 24 96 2.16
      Big Data D1 32 128 2.88
      Big Data D1 56 224 5.03
      Big Data D2 8 32 0.68
      Big Data D2 16 64 1.36
      Big Data D2 24 96 2.04
      Big Data D2 32 128 2.72
      Big Data D2 64 256 5.44
      Big Data D2 76 320 6.68
      MEM Optimized M4 2 4 0.11
      MEM Optimized M4 4 32 0.46
      MEM Optimized M4 8 64 0.91
      MEM Optimized M4 12 96 1.36
      MEM Optimized M4 12 144 1.85
      MEM Optimized M4 16 128 1.81
      MEM Optimized M4 16 192 2.47
      MEM Optimized M4 32 256 3.61
      MEM Optimized M4 32 384 4.94
      MEM Optimized M4 64 512 7.22
      MEM Optimized M4 72 648 8.86
      Standard S3 2 4 0.12
      Standard S3 2 8 0.17
      Standard S3 4 8 0.23
      Standard S3 4 16 0.34
      Standard S3 8 16 0.45
      Standard S3 8 32 0.67
      Standard S3 16 32 0.89
      Standard S3 16 64 1.33
      Standard S3 24 48 1.33
      Standard S3 24 96 1.99
      Standard S3 32 64 1.77
      Standard S3 32 128 2.65
      Standard S3 48 96 2.65
      Standard S3 48 192 3.98
      Standard S3 64 128 3.54
      Standard S3 64 256 5.3
      High-IO I3 8 32 0.68
      High-IO I3 16 64 1.35
      High-IO I3 24 96 2.02
      High-IO I3 32 128 2.69
      High-IO I3 48 192 4.03
      High-IO I3 64 256 5.38
      High-IO I3 80 320 6.72
      Standard S4 1 4 0.09
      Standard S4 2 4 0.12
      Standard S4 2 8 0.17
      Standard S4 4 8 0.23
      Standard S4 4 16 0.34
      Standard S4 8 16 0.46
      Standard S4 8 32 0.68
      Standard S4 16 32 0.91
      Standard S4 16 64 1.35
      Standard S4 24 48 1.36
      Standard S4 24 96 2.02
      Standard S4 32 64 1.81
      Standard S4 32 128 2.69
      Standard S4 48 96 2.71
      Standard S4 48 192 4.03
      Standard S4 64 128 3.61
      Standard S4 64 256 5.38
      Standard S4 72 288 6.05
      Big Data DS2 8 32 0.68
      Big Data DS2 16 64 1.36
      Big Data DS2 24 96 2.04
      Big Data DS2 32 128 2.72
      Standard Network Optimized SN3ne 2 4 0.12
      Standard Network Optimized SN3ne 4 8 0.23
      Standard Network Optimized SN3ne 4 16 0.34
      Standard Network Optimized SN3ne 8 16 0.46
      Standard Network Optimized SN3ne 8 32 0.68
      Standard Network Optimized SN3ne 12 24 0.68
      Standard Network Optimized SN3ne 16 32 0.91
      Standard Network Optimized SN3ne 16 64 1.35
      Standard Network Optimized SN3ne 24 48 1.36
      Standard Network Optimized SN3ne 24 96 2.02
      Standard Network Optimized SN3ne 32 64 1.81
      Standard Network Optimized SN3ne 32 128 2.69
      Standard Network Optimized SN3ne 48 96 2.71
      Standard Network Optimized SN3ne 64 128 3.61
      Standard Network Optimized SN3ne 64 192 4.49
      Standard Network Optimized SN3ne 64 256 5.38
      Standard Network Optimized SN3ne 72 288 6.05
      Standard Network Optimized SN3ne 76 192 4.84
      Standard Network Optimized SN3ne 76 256 5.72
      MEM Optimized M5 2 16 0.23
      MEM Optimized M5 4 32 0.46
      MEM Optimized M5 8 64 0.91
      MEM Optimized M5 12 96 1.36
      MEM Optimized M5 32 256 3.61
      Standard S5 2 4 0.07
      Standard S5 2 8 0.11
      Standard S5 4 8 0.14
      Standard S5 4 16 0.21
      Standard S5 8 16 0.28
      Standard S5 8 32 0.41
      Standard S5 16 32 0.55
      Standard S5 16 64 0.82
      Standard S5 24 48 0.82
      Standard S5 24 64 0.95
      Standard S5 24 96 1.23
      Standard S5 32 64 1.09
      Standard S5 32 128 1.63
      Standard S5 48 96 1.63
      Standard S5 48 192 2.45
      Standard S5 64 192 2.72
      Standard S5 64 256 3.26
      Standard S5 76 256 3.46
      High-IO IT3 16 64 1.44
      High-IO IT3 32 128 2.88
      High-IO IT3 64 256 5.75
      High-IO IT3 76 160 4.67
      Computing Network Enhanced CN3 4 8 0.28
      Computing Network Enhanced CN3 4 16 0.42
      Computing Network Enhanced CN3 8 16 0.56
      Computing Network Enhanced CN3 8 32 0.83
      Computing Network Enhanced CN3 16 32 1.11
      Computing Network Enhanced CN3 16 64 1.66
      Computing Network Enhanced CN3 32 64 2.21
      Computing Network Enhanced CN3 32 128 3.32
      Standard SA2 2 4 0.14
      Standard SA2 4 4 0.07
      Standard SA2 4 8 0.09
      Standard SA2 8 8 0.14
      Standard SA2 8 16 0.18
      Standard SA2 16 32 0.36
      Standard SA2 16 64 0.53
      Standard SA2 32 64 0.71
      Standard SA2 64 128 1.41
      Standard SA2 64 192 1.77
      Standard SA2 80 160 1.77
      Standard SA2 128 256 2.82
      Standard SA2 160 320 3.53
      Computing C3 4 8 0.35
      Computing C3 4 16 0.52
      Computing C3 8 16 0.69
      Computing C3 8 32 1.04
      Computing C3 16 32 1.38
      Computing C3 16 64 2.08
      Computing C3 32 64 2.76
      Computing C3 32 128 4.16

      Other billable items that may incur fees when you use an EMR cluster include network traffic, metadata storage, and COS.

      • Network traffic
        Public network access is enabled for the Master.1 node of the cluster by default, so that WebUIs of various Hadoop components can be accessed from outside the cluster. Traffic fees will be incurred by the data interaction when these pages are accessed, which are very low in most cases; therefore, bill-by-traffic is used by default, which is more cost-effective than bill-by-bandwidth.
      • Metadata storage
        TencentDB for MySQL is used for metadata storage such as Hive in EMR 2.x and below. The storage fees are included in the total fees on the purchase page.
      • COS
        If you use COS to isolate storage and computation of your cluster, data storage and request fees will be incurred (for more information, see COS Product Pricing) when your cluster makes a computation request and pulls data from COS. In addition, new data may be generated in COS during computation for the purposes of result data storage or backup.

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