China (Ningxia) Region
Pricing for Amazon EC2 and Amazon EMR
Pricing for Amazon EC2 and Amazon EMR
With Amazon EMR you only pay for what you use. Amazon EMR pricing is in addition to pricing for EC2 and S3. We charge less where our costs are less, and your cost will depend on the number and type of Amazon EC2 Instances in your job flow and the amount of time it is running.
Instance Type |
Amazon EC2 Price (per hour) |
Amazon EMR Price (per hour) |
General Purpose - Current Generation |
||
m5.xlarge |
¥1.356 |
¥0.318 |
m5.2xlarge |
¥2.711 |
¥0.637 |
m5.4xlarge |
¥5.422 |
¥1.275 |
m5.8xlarge |
¥10.844 |
¥1.793 |
m5.12xlarge |
¥16.266 |
¥1.793 |
m5.16xlarge |
¥21.688 |
¥1.793 |
m5.24xlarge |
¥32.532 |
¥1.793 |
m5a.xlarge |
¥1.219 |
¥0.286 |
m5a.2xlarge |
¥2.437 |
¥0.572 |
m5a.4xlarge |
¥4.874 |
¥1.143 |
m5a.8xlarge |
¥9.749 |
¥1.793 |
m5a.12xlarge |
¥14.623 |
¥1.793 |
m5a.16xlarge |
¥19.497 |
¥1.793 |
m5a.24xlarge |
¥29.246 |
¥1.793 |
m5d.xlarge |
¥1.657 |
¥0.378 |
m5d.2xlarge |
¥3.313 |
¥0.75 |
m5d.4xlarge |
¥6.627 |
¥1.5 |
m5d.8xlarge |
¥13.254 |
¥1.793 |
m5d.12xlarge |
¥19.881 |
¥1.793 |
m5d.16xlarge |
¥26.508 |
¥1.793 |
m5d.24xlarge |
¥39.762 |
¥1.793 |
m6g.xlarge |
¥1.0762 |
¥0.259 |
m6g.2xlarge |
¥2.1524 |
¥0.511 |
m6g.4xlarge |
¥4.3048 |
¥1.023 |
m6g.8xlarge |
¥8.6095 |
¥2.045 |
m6g.12xlarge |
¥12.9143 |
¥3.068 |
m6g.16xlarge |
¥17.2191 |
¥4.09 |
m7g.xlarge |
¥1.0762 |
¥0.270912 |
m7g.2xlarge |
¥2.1524 |
¥0.541824 |
m7g.4xlarge |
¥4.3048 |
¥1.083648 |
m7g.8xlarge |
¥8.6095 |
¥2.167296 |
m7g.12xlarge |
¥12.9143 |
¥3.250944 |
m7g.16xlarge |
¥17.2191 |
¥4.334592 |
m8g.xlarge |
¥1.07619 |
¥0.2980032 |
m8g.2xlarge |
¥2.15238 |
¥0.5960064 |
m8g.4xlarge |
¥4.30476 |
¥1.1920128 |
m8g.8xlarge |
¥8.60953 |
¥2.3840256 |
m8g.12xlarge |
¥12.91429 |
¥3.5760384 |
m8g.16xlarge |
¥17.21906 |
¥4.7680512 |
m8g.24xlarge |
¥25.82859 |
¥7.1520768 |
m8g.48xlarge |
¥51.65718 |
¥14.3041536 |
General Purpose - Previous Generation |
||
m4.large |
¥0.8078 |
¥0.187 |
m4.xlarge |
¥1.6157 |
¥0.372 |
m4.2xlarge |
¥3.2313 |
¥0.745 |
m4.4xlarge |
¥6.4626 |
¥1.49 |
m4.10xlarge |
¥16.1566 |
¥1.68 |
m4.16xlarge |
¥25.8505 |
¥1.68 |
m6i.xlarge |
¥1.35551 |
¥0.31872 |
m6i.2xlarge |
¥2.71102 |
¥0.63744 |
m6i.4xlarge |
¥5.42203 |
¥1.27488 |
m6i.8xlarge |
¥10.84406 |
¥2.54976 |
m6i.12xlarge |
¥16.2661 |
¥3.82464 |
m6i.16xlarge |
¥21.68813 |
¥5.09952 |
m6i.24xlarge |
¥32.53219 |
¥7.64928 |
m6i.32xlarge |
¥43.37626 |
¥10.19904 |
Compute Optimized - Current Generation |
||
c5.xlarge |
¥0.986 |
¥0.283 |
c5.2xlarge |
¥1.972 |
¥0.565 |
c5.4xlarge |
¥3.943 |
¥1.129 |
c5.9xlarge |
¥8.872 |
¥1.793 |
c5.12xlarge |
¥11.83 |
¥1.793 |
c5.18xlarge |
¥17.745 |
¥1.793 |
c5.24xlarge |
¥23.66 |
¥1.793 |
c5a.xlarge |
¥0.89 |
¥0.25564 |
c5a.2xlarge |
¥1.78 |
¥0.51128 |
c5a.4xlarge |
¥3.56 |
¥1.02256 |
c5a.8xlarge |
¥7.12 |
¥1.793 |
c5a.12xlarge |
¥10.68 |
¥1.793 |
c5a.16xlarge |
¥14.24 |
¥1.793 |
c5a.24xlarge |
¥21.36 |
¥1.793 |
c5d.xlarge |
¥1.191 |
¥0.319 |
c5d.2xlarge |
¥2.382 |
¥0.638 |
c5d.4xlarge |
¥4.765 |
¥1.275 |
c5d.9xlarge |
¥10.721 |
¥1.793 |
c5d.18xlarge |
¥21.442 |
¥1.793 |
c6g.xlarge |
¥0.7832 |
¥0.226 |
c6g.2xlarge |
¥1.5664 |
¥0.452 |
c6g.4xlarge |
¥3.1327 |
¥0.903 |
c6g.8xlarge |
¥6.2655 |
¥1.806 |
c6g.12xlarge |
¥9.3982 |
¥2.709 |
c6g.16xlarge |
¥12.5309 |
¥3.612 |
c6gd.xlarge |
¥0.953 |
¥0.254976 |
c6gd.2xlarge |
¥1.9059 |
¥0.509952 |
c6gd.4xlarge |
¥3.8119 |
¥1.019904 |
c6gd.8xlarge |
¥7.6237 |
¥2.039808 |
c6gd.12xlarge |
¥11.4356 |
¥3.059712 |
c6gd.16xlarge |
¥15.2474 |
¥4.079616 |
c6i.xlarge |
¥0.98582 |
¥0.2822 |
c6i.2xlarge |
¥1.97165 |
¥0.5644 |
c6i.4xlarge |
¥3.9433 |
¥1.1288 |
c6i.8xlarge |
¥7.88659 |
¥2.2576 |
c6i.12xlarge |
¥11.82989 |
¥3.3864 |
c6i.16xlarge |
¥15.77318 |
¥4.5152 |
c6i.24xlarge |
¥23.65978 |
¥6.7728 |
c6i.32xlarge |
¥31.54637 |
¥9.0304 |
c6in.xlarge |
¥1.77448 |
¥0.376488 |
c6in.2xlarge |
¥3.54897 |
¥0.752976 |
c6in.4xlarge |
¥7.09793 |
¥1.505952 |
c6in.8xlarge |
¥14.19587 |
¥3.011904 |
c6in.12xlarge |
¥21.2938 |
¥4.517856 |
c6in.16xlarge |
¥28.39173 |
¥6.023808 |
c6in.24xlarge |
¥42.5876 |
¥9.035712 |
c6in.32xlarge |
¥56.78346 |
¥12.047616 |
c7g.xlarge |
¥0.7832 |
¥0.2407 |
c7g.2xlarge |
¥1.5664 |
¥0.4814 |
c7g.4xlarge |
¥3.1327 |
¥0.9628 |
c7g.8xlarge |
¥6.2655 |
¥1.9256 |
c7g.12xlarge |
¥9.3982 |
¥2.8884 |
c7g.16xlarge |
¥12.5309 |
¥3.8512 |
c8g.xlarge |
¥0.78318 |
¥0.2648032 |
c8g.2xlarge |
¥1.56636 |
¥0.5296064 |
c8g.4xlarge |
¥3.13273 |
¥1.0592128 |
c8g.8xlarge |
¥6.26546 |
¥2.1184256 |
c8g.12xlarge |
¥9.39819 |
¥3.1776384 |
c8g.16xlarge |
¥12.53092 |
¥4.2368512 |
c8g.24xlarge |
¥18.79638 |
¥6.3552768 |
c8g.48xlarge |
¥37.59276 |
¥12.7105536 |
Compute Optimized - Previous Generation |
||
c4.large |
¥0.678 |
¥0.164 |
c4.xlarge |
¥1.356 |
¥0.325 |
c4.2xlarge |
¥2.711 |
¥0.652 |
c4.4xlarge |
¥5.422 |
¥1.304 |
c4.8xlarge |
¥10.844 |
¥1.68 |
c6gn.xlarge |
¥0.99513 |
¥0.286848 |
c6gn.2xlarge |
¥1.99027 |
¥0.573696 |
c6gn.4xlarge |
¥3.98054 |
¥1.147392 |
c6gn.8xlarge |
¥7.96108 |
¥2.294784 |
c6gn.12xlarge |
¥11.94161 |
¥3.442176 |
c6gn.16xlarge |
¥15.92215 |
¥4.589568 |
Memory Optimized - Current Generation |
||
r5.xlarge |
¥1.766 |
¥0.419 |
r5.2xlarge |
¥3.533 |
¥0.837 |
r5.4xlarge |
¥7.065 |
¥1.674 |
r5.8xlarge |
¥14.13 |
¥1.793 |
r5.12xlarge |
¥21.195 |
¥1.793 |
r5.16xlarge |
¥28.26 |
¥1.793 |
r5.24xlarge |
¥42.39 |
¥1.793 |
r5a.xlarge |
¥1.59 |
¥0.378 |
r5a.2xlarge |
¥3.179 |
¥0.75 |
r5a.4xlarge |
¥6.359 |
¥1.501 |
r5a.8xlarge |
¥12.717 |
¥1.793 |
r5a.12xlarge |
¥19.076 |
¥1.793 |
r5a.16xlarge |
¥25.434 |
¥1.793 |
r5a.24xlarge |
¥38.151 |
¥1.793 |
r5d.xlarge |
¥2.067 |
¥0.479 |
r5d.2xlarge |
¥4.135 |
¥0.957 |
r5d.4xlarge |
¥8.27 |
¥1.793 |
r5d.8xlarge |
¥16.54 |
¥1.793 |
r5d.12xlarge |
¥24.81 |
¥1.793 |
r5d.16xlarge |
¥33.08 |
¥1.793 |
r5d.24xlarge |
¥49.62 |
¥1.793 |
r6g.xlarge |
¥1.4021 |
¥0.335 |
r6g.2xlarge |
¥2.8041 |
¥0.669 |
r6g.4xlarge |
¥5.6082 |
¥1.339 |
r6g.8xlarge |
¥11.2165 |
¥2.677 |
r6g.12xlarge |
¥16.8247 |
¥4.016 |
r6g.16xlarge |
¥22.433 |
¥5.354 |
r6gd.xlarge |
¥1.654 |
¥0.382464 |
r6gd.2xlarge |
¥3.308 |
¥0.764928 |
r6gd.4xlarge |
¥6.616 |
¥1.529856 |
r6gd.8xlarge |
¥13.2319 |
¥3.059712 |
r6gd.12xlarge |
¥19.8479 |
¥4.589568 |
r6gd.16xlarge |
¥26.4639 |
¥6.119424 |
r7g.xlarge |
¥1.4021 |
¥0.355572 |
r7g.2xlarge |
¥2.8041 |
¥0.711144 |
r7g.4xlarge |
¥5.6082 |
¥1.422288 |
r7g.8xlarge |
¥11.2165 |
¥2.844576 |
r7g.12xlarge |
¥16.8247 |
¥4.266864 |
r7g.16xlarge |
¥22.433 |
¥5.689152 |
r8g.xlarge |
¥1.40206 |
¥0.3911624 |
r8g.2xlarge |
¥2.80412 |
¥0.7823248 |
r8g.4xlarge |
¥5.60824 |
¥1.5646496 |
r8g.8xlarge |
¥11.21649 |
¥3.1292992 |
r8g.12xlarge |
¥16.82473 |
¥4.6939488 |
r8g.16xlarge |
¥22.43297 |
¥6.2585984 |
r8g.24xlarge |
¥33.64946 |
¥9.3878976 |
r8g.48xlarge |
¥67.29892 |
¥18.7757952 |
x1.16xlarge |
¥46.147 |
¥11.07054 |
x1.32xlarge |
¥92.294 |
¥22.14108 |
x2idn.16xlarge |
¥46.14204 |
¥11.07054 |
x2idn.24xlarge |
¥69.21306 |
¥16.60581 |
x2idn.32xlarge |
¥92.28408 |
¥22.14108 |
x2iedn.xlarge |
¥5.76776 |
¥1.3838258 |
x2iedn.2xlarge |
¥11.53551 |
¥2.767635 |
x2iedn.4xlarge |
¥23.07102 |
¥5.53527 |
x2iedn.8xlarge |
¥46.14204 |
¥11.07054 |
x2iedn.16xlarge |
¥92.28408 |
¥22.14108 |
x2iedn.24xlarge |
¥138.42612 |
¥33.21162 |
x2iedn.32xlarge |
¥184.56816 |
¥44.28216 |
z1d.xlarge |
¥2.684 |
¥0.618 |
z1d.2xlarge |
¥5.367 |
¥1.235 |
z1d.3xlarge |
¥8.051 |
¥1.793 |
z1d.6xlarge |
¥16.102 |
¥1.793 |
z1d.12xlarge |
¥32.204 |
¥1.793 |
Memory Optimized - Previous Generation |
||
r4.xlarge |
¥2.6289 |
¥0.417 |
r4.2xlarge |
¥5.2577 |
¥0.828 |
r4.4xlarge |
¥10.5155 |
¥1.655 |
r4.8xlarge |
¥21.0309 |
¥1.68 |
r4.16xlarge |
¥42.0618 |
¥1.68 |
r6i.xlarge |
¥1.76627 |
¥0.41832 |
r6i.2xlarge |
¥3.53254 |
¥0.83664 |
r6i.4xlarge |
¥7.06507 |
¥1.67328 |
r6i.8xlarge |
¥14.13014 |
¥3.34656 |
r6i.12xlarge |
¥21.19522 |
¥5.01984 |
r6i.16xlarge |
¥28.26029 |
¥6.69312 |
r6i.24xlarge |
¥42.39043 |
¥10.03968 |
r6i.32xlarge |
¥56.52058 |
¥13.38624 |
Storage Optimized - Current Generation |
||
i3.xlarge |
¥2.341 |
¥0.518 |
i3.2xlarge |
¥4.683 |
¥1.036 |
i3.4xlarge |
¥9.365 |
¥1.793 |
i3.8xlarge |
¥18.731 |
¥1.793 |
i3.16xlarge |
¥37.461 |
¥1.793 |
i3en.xlarge |
¥3.382 |
¥0.75032 |
i3en.2xlarge |
¥6.764 |
¥1.50064 |
i3en.3xlarge |
¥10.146 |
¥1.793 |
i3en.6xlarge |
¥20.292 |
¥1.793 |
i3en.12xlarge |
¥40.583 |
¥1.793 |
i3en.24xlarge |
¥81.166 |
¥1.793 |
i4i.xlarge |
¥2.728 |
¥0.56938 |
i4i.2xlarge |
¥5.457 |
¥1.13876 |
i4i.4xlarge |
¥10.914 |
¥2.27918 |
i4i.8xlarge |
¥21.827 |
¥4.55836 |
i4i.12xlarge |
¥32.741 |
¥6.83588 |
i4i.16xlarge |
¥43.654 |
¥9.11506 |
i4i.24xlarge |
¥65.48172 |
¥13.673088 |
i4i.32xlarge |
¥87.30895 |
¥18.230784 |
Storage Optimized - Previous Generation |
||
d2.xlarge |
¥5.401 |
¥1.077 |
d2.2xlarge |
¥10.803 |
¥1.68 |
d2.4xlarge |
¥21.606 |
¥1.68 |
d2.8xlarge |
¥43.212 |
¥1.68 |
You are charged per-second from the time your cluster starts until it is terminated, with a one-minute minimum.
Amazon EMR Additional Pricing Details
Amazon S3 is billed separately. (Many customers store their input and output data in S3; others store all of the data locally on HDFS.) The more data you store, the lower the monthly price per GB.
China (Beijing) Region
Pricing for Amazon EC2 and Amazon EMR
With Amazon EMR you only pay for what you use. Amazon EMR pricing is in addition to pricing for EC2 and S3. We charge less where our costs are less, and your cost will depend on the number and type of Amazon EC2 Instances in your job flow and the amount of time it is running.
Instance Type |
Amazon EC2 Price (per hour) |
Amazon EMR Price (per hour) |
General Purpose - Current Generation |
||
m5.xlarge |
¥2.026 |
¥0.318 |
m5.2xlarge |
¥4.053 |
¥0.637 |
m5.4xlarge |
¥8.106 |
¥1.275 |
m5.8xlarge |
¥16.211 |
¥1.793 |
m5.12xlarge |
¥24.317 |
¥1.793 |
m5.16xlarge |
¥32.423 |
¥1.793 |
m5.24xlarge |
¥48.634 |
¥1.793 |
m5a.xlarge |
¥1.821 |
¥0.286 |
m5a.2xlarge |
¥3.642 |
¥0.572 |
m5a.4xlarge |
¥7.284 |
¥1.143 |
m5a.8xlarge |
¥14.568 |
¥1.793 |
m5a.12xlarge |
¥21.852 |
¥1.793 |
m5a.16xlarge |
¥29.137 |
¥1.793 |
m5a.24xlarge |
¥43.705 |
¥1.793 |
m5d.xlarge |
¥2.547 |
¥0.378 |
m5d.2xlarge |
¥5.093 |
¥0.75 |
m5d.4xlarge |
¥10.187 |
¥1.5 |
m5d.8xlarge |
¥20.374 |
¥1.793 |
m5d.12xlarge |
¥30.561 |
¥1.793 |
m5d.16xlarge |
¥40.747 |
¥1.793 |
m5d.24xlarge |
¥61.121 |
¥1.793 |
m6g.xlarge |
¥1.6074 |
¥0.259 |
m6g.2xlarge |
¥3.2149 |
¥0.511 |
m6g.4xlarge |
¥6.4298 |
¥1.023 |
m6g.8xlarge |
¥12.8595 |
¥2.045 |
m6g.12xlarge |
¥19.2893 |
¥3.068 |
m6g.16xlarge |
¥25.7191 |
¥4.09 |
m7g.xlarge |
¥1.7225 |
¥0.270912 |
m7g.2xlarge |
¥3.4449 |
¥0.541824 |
m7g.4xlarge |
¥6.8898 |
¥1.083648 |
m7g.8xlarge |
¥13.7796 |
¥2.167296 |
m7g.12xlarge |
¥20.6694 |
¥3.250944 |
m7g.16xlarge |
¥27.5593 |
¥4.334592 |
m8g.xlarge |
¥1.72245 |
¥0.2980032 |
m8g.2xlarge |
¥3.44491 |
¥0.5960064 |
m8g.4xlarge |
¥6.88981 |
¥1.1920128 |
m8g.8xlarge |
¥13.77963 |
¥2.3840256 |
m8g.12xlarge |
¥20.66944 |
¥3.5760384 |
m8g.16xlarge |
¥27.55926 |
¥4.7680512 |
m8g.24xlarge |
¥41.33889 |
¥7.1520768 |
m8g.48xlarge |
¥82.67777 |
¥14.3041536 |
General Purpose - Previous Generation |
||
m1.small |
¥0.442 |
¥0.069 |
m3.xlarge |
¥3.471 |
¥0.436 |
m3.2xlarge |
¥6.942 |
¥0.871 |
m4.large |
¥1.405 |
¥0.187 |
m4.xlarge |
¥2.815 |
¥0.372 |
m4.2xlarge |
¥5.624 |
¥0.745 |
m4.4xlarge |
¥11.248 |
¥1.49 |
m4.10xlarge |
¥28.121 |
¥1.68 |
m4.16xlarge |
¥44.995 |
¥1.793 |
m6i.xlarge |
¥2.02642 |
¥0.31872 |
m6i.2xlarge |
¥4.05283 |
¥0.63744 |
m6i.4xlarge |
¥8.10566 |
¥1.27488 |
m6i.8xlarge |
¥16.21133 |
¥2.54976 |
m6i.12xlarge |
¥24.31699 |
¥3.82464 |
m6i.16xlarge |
¥32.42266 |
¥5.09952 |
m6i.24xlarge |
¥48.63398 |
¥7.64928 |
m6i.32xlarge |
¥64.84531 |
¥10.19904 |
Compute Optimized - Current Generation |
||
c5.xlarge |
¥1.479 |
¥0.283 |
c5.2xlarge |
¥2.957 |
¥0.565 |
c5.4xlarge |
¥5.915 |
¥1.129 |
c5.9xlarge |
¥13.309 |
¥1.793 |
c5.12xlarge |
¥17.745 |
¥1.793 |
c5.18xlarge |
¥26.617 |
¥1.793 |
c5.24xlarge |
¥35.49 |
¥1.793 |
c5a.xlarge |
¥1.328 |
¥0.25564 |
c5a.2xlarge |
¥2.656 |
¥0.51128 |
c5a.4xlarge |
¥5.312 |
¥1.02256 |
c5a.8xlarge |
¥10.625 |
¥1.793 |
c5a.12xlarge |
¥15.937 |
¥1.793 |
c5a.16xlarge |
¥21.25 |
¥1.793 |
c5a.24xlarge |
¥31.875 |
¥1.793 |
c5d.xlarge |
¥1.821 |
¥0.319 |
c5d.2xlarge |
¥3.642 |
¥0.638 |
c5d.4xlarge |
¥7.284 |
¥1.275 |
c5d.9xlarge |
¥16.389 |
¥1.793 |
c5d.12xlarge |
¥21.852 |
¥1.793 |
c5d.18xlarge |
¥32.779 |
¥1.793 |
c5d.24xlarge |
¥43.705 |
¥1.793 |
c6g.xlarge |
¥1.172 |
¥0.226 |
c6g.2xlarge |
¥2.3441 |
¥0.452 |
c6g.4xlarge |
¥4.6881 |
¥0.903 |
c6g.8xlarge |
¥9.3763 |
¥1.806 |
c6g.12xlarge |
¥14.0644 |
¥2.709 |
c6g.16xlarge |
¥18.7526 |
¥3.612 |
c6i.xlarge |
¥1.47874 |
¥0.2822 |
c6i.2xlarge |
¥2.95747 |
¥0.5644 |
c6i.4xlarge |
¥5.91494 |
¥1.1288 |
c6i.8xlarge |
¥11.82989 |
¥2.2576 |
c6i.12xlarge |
¥17.74483 |
¥3.3864 |
c6i.16xlarge |
¥23.65978 |
¥4.5152 |
c6i.24xlarge |
¥35.48966 |
¥6.7728 |
c6i.32xlarge |
¥47.31955 |
¥9.0304 |
c7g.xlarge |
¥1.2569 |
¥0.2407 |
c7g.2xlarge |
¥2.5139 |
¥0.4814 |
c7g.4xlarge |
¥5.0277 |
¥0.9628 |
c7g.8xlarge |
¥10.0554 |
¥1.9256 |
c7g.12xlarge |
¥15.0831 |
¥2.8884 |
c7g.16xlarge |
¥20.1108 |
¥3.8512 |
c8g.xlarge |
¥1.25693 |
¥0.2648032 |
c8g.2xlarge |
¥2.51385 |
¥0.5296064 |
c8g.4xlarge |
¥5.0277 |
¥1.0592128 |
c8g.8xlarge |
¥10.0554 |
¥2.1184256 |
c8g.12xlarge |
¥15.08311 |
¥3.1776384 |
c8g.16xlarge |
¥20.11081 |
¥4.2368512 |
c8g.24xlarge |
¥30.16621 |
¥6.3552768 |
c8g.48xlarge |
¥60.33243 |
¥12.7105536 |
Compute Optimized - Previous Generation |
||
c3.xlarge |
¥2.109 |
¥0.33 |
c3.2xlarge |
¥4.217 |
¥0.654 |
c3.4xlarge |
¥8.434 |
¥1.307 |
c3.8xlarge |
¥16.869 |
¥1.68 |
c4.large |
¥1.134 |
¥0.164 |
c4.xlarge |
¥2.268 |
¥0.325 |
c4.2xlarge |
¥4.535 |
¥0.652 |
c4.4xlarge |
¥9.071 |
¥1.304 |
c4.8xlarge |
¥18.141 |
¥1.68 |
c6gn.xlarge |
¥1.48914 |
¥0.286848 |
c6gn.2xlarge |
¥2.97828 |
¥0.573696 |
c6gn.4xlarge |
¥5.95657 |
¥1.147392 |
c6gn.8xlarge |
¥11.91314 |
¥2.294784 |
c6gn.12xlarge |
¥17.8697 |
¥3.442176 |
c6gn.16xlarge |
¥23.82627 |
¥4.589568 |
Memory Optimized - Current Generation |
||
r5.xlarge |
¥2.437 |
¥0.419 |
r5.2xlarge |
¥4.874 |
¥0.837 |
r5.4xlarge |
¥9.749 |
¥1.674 |
r5.8xlarge |
¥19.497 |
¥1.793 |
r5.12xlarge |
¥29.246 |
¥1.793 |
r5.16xlarge |
¥38.995 |
¥1.793 |
r5.24xlarge |
¥58.492 |
¥1.793 |
r5a.xlarge |
¥2.193 |
¥0.378 |
r5a.2xlarge |
¥4.387 |
¥0.75 |
r5a.4xlarge |
¥8.774 |
¥1.501 |
r5a.8xlarge |
¥17.548 |
¥1.793 |
r5a.12xlarge |
¥26.322 |
¥1.793 |
r5a.16xlarge |
¥35.095 |
¥1.793 |
r5a.24xlarge |
¥52.643 |
¥1.793 |
r5d.xlarge |
¥2.957 |
¥0.479 |
r5d.2xlarge |
¥5.915 |
¥0.957 |
r5d.4xlarge |
¥11.83 |
¥1.793 |
r5d.8xlarge |
¥23.66 |
¥1.793 |
r5d.12xlarge |
¥35.49 |
¥1.793 |
r5d.16xlarge |
¥47.32 |
¥1.793 |
r5d.24xlarge |
¥70.979 |
¥1.793 |
r6g.xlarge |
¥1.9333 |
¥0.335 |
r6g.2xlarge |
¥3.8666 |
¥0.669 |
r6g.4xlarge |
¥7.7332 |
¥1.339 |
r6g.8xlarge |
¥15.4665 |
¥2.677 |
r6g.12xlarge |
¥23.1997 |
¥4.016 |
r6g.16xlarge |
¥30.933 |
¥5.354 |
r6gd.xlarge |
¥2.3413 |
¥0.382464 |
r6gd.2xlarge |
¥4.6827 |
¥0.764928 |
r6gd.4xlarge |
¥9.3653 |
¥1.529856 |
r6gd.8xlarge |
¥18.7307 |
¥3.059712 |
r6gd.12xlarge |
¥28.096 |
¥4.589568 |
r6gd.16xlarge |
¥37.4613 |
¥6.119424 |
r7g.xlarge |
¥2.073 |
¥0.355572 |
r7g.2xlarge |
¥4.1459 |
¥0.711144 |
r7g.4xlarge |
¥8.2919 |
¥1.422288 |
r7g.8xlarge |
¥16.5838 |
¥2.844576 |
r7g.12xlarge |
¥24.8756 |
¥4.266864 |
r7g.16xlarge |
¥33.1675 |
¥5.689152 |
r8g.xlarge |
¥2.07297 |
¥0.3911624 |
r8g.2xlarge |
¥4.14594 |
¥0.7823248 |
r8g.4xlarge |
¥8.29188 |
¥1.5646496 |
r8g.8xlarge |
¥16.58375 |
¥3.1292992 |
r8g.12xlarge |
¥24.87563 |
¥4.6939488 |
r8g.16xlarge |
¥33.1675 |
¥6.2585984 |
r8g.24xlarge |
¥49.75125 |
¥9.3878976 |
r8g.48xlarge |
¥99.5025 |
¥18.7757952 |
x1.16xlarge |
¥68.876 |
¥11.07054 |
x1.32xlarge |
¥137.752 |
¥22.14108 |
x2idn.16xlarge |
¥68.87076 |
¥11.07054 |
x2idn.24xlarge |
¥103.30614 |
¥16.60581 |
x2idn.32xlarge |
¥137.74152 |
¥22.14108 |
x2iedn.xlarge |
¥8.60885 |
¥1.3838258 |
x2iedn.2xlarge |
¥17.21769 |
¥2.767635 |
x2iedn.4xlarge |
¥34.43538 |
¥5.53527 |
x2iedn.8xlarge |
¥68.87076 |
¥11.07054 |
x2iedn.16xlarge |
¥137.74152 |
¥22.14108 |
x2iedn.24xlarge |
¥206.61228 |
¥33.21162 |
x2iedn.32xlarge |
¥275.48304 |
¥44.28216 |
Memory Optimized - Previous Generation |
||
r3.xlarge |
¥4.9018 |
¥0.56 |
r3.2xlarge |
¥9.8036 |
¥1.12 |
r3.4xlarge |
¥19.6073 |
¥1.68 |
r3.8xlarge |
¥39.2147 |
¥1.68 |
r4.xlarge |
¥3.924 |
¥0.417 |
r4.2xlarge |
¥7.842 |
¥0.828 |
r4.4xlarge |
¥15.683 |
¥1.655 |
r4.8xlarge |
¥31.373 |
¥1.68 |
r4.16xlarge |
¥62.746 |
¥1.68 |
r6i.xlarge |
¥2.43718 |
¥0.41832 |
r6i.2xlarge |
¥4.87435 |
¥0.83664 |
r6i.4xlarge |
¥9.7487 |
¥1.67328 |
r6i.8xlarge |
¥19.49741 |
¥3.34656 |
r6i.12xlarge |
¥29.24611 |
¥5.01984 |
r6i.16xlarge |
¥38.99482 |
¥6.69312 |
r6i.24xlarge |
¥58.49222 |
¥10.03968 |
r6i.32xlarge |
¥77.98963 |
¥13.38624 |
Storage Optimized - Current Generation |
||
i3.xlarge |
¥3.122 |
¥0.51792 |
i3.2xlarge |
¥6.244 |
¥0.517 |
i3.4xlarge |
¥12.487 |
¥1.035 |
i3.8xlarge |
¥24.974 |
¥1.793 |
i3.16xlarge |
¥49.948 |
¥1.793 |
i3en.xlarge |
¥4.525 |
¥0.75032 |
i3en.2xlarge |
¥9.05 |
¥1.50064 |
i3en.3xlarge |
¥13.576 |
¥1.793 |
i3en.6xlarge |
¥27.151 |
¥1.793 |
i3en.12xlarge |
¥54.302 |
¥1.793 |
i3en.24xlarge |
¥108.605 |
¥1.793 |
i4i.xlarge |
¥3.639 |
¥0.56938 |
i4i.2xlarge |
¥7.278 |
¥1.13876 |
i4i.4xlarge |
¥14.555 |
¥2.27918 |
i4i.8xlarge |
¥29.11 |
¥4.55836 |
i4i.12xlarge |
¥43.665 |
¥6.83588 |
i4i.16xlarge |
¥58.221 |
¥9.11506 |
i4i.24xlarge |
¥87.33086 |
¥13.673088 |
i4i.32xlarge |
¥116.44115 |
¥18.230784 |
Storage Optimized - Previous Generation |
||
d2.xlarge |
¥6.673 |
¥1.077 |
d2.2xlarge |
¥13.345 |
¥1.68 |
d2.4xlarge |
¥26.69 |
¥1.68 |
d2.8xlarge |
¥53.38 |
¥1.68 |
i2.xlarge |
¥10.204 |
¥1.325 |
i2.2xlarge |
¥20.407 |
¥1.68 |
i2.4xlarge |
¥40.815 |
¥1.68 |
i2.8xlarge |
¥81.63 |
¥1.68 |
You are charged per-second from the time your cluster starts until it is terminated, with a one-minute minimum.
Amazon EMR Additional Pricing Details
Amazon S3 is billed separately. (Many customers store their input and output data in S3; others store all of the data locally on HDFS.) The more data you store, the lower the monthly price per GB.
Pricing for Amazon EMR on Amazon EKS
This pricing is for Amazon EMR on Amazon EKS clusters.
The Amazon EMR price is in addition to the Amazon EKS pricing or any other services used with Amazon EKS. You can run EKS on Amazon Web Services using either EC2 or Amazon Fargate. If you are using Amazon EC2 (including with EKS managed node groups), you pay for Amazon Web Services resources (e.g., EC2 instances or Amazon EBS volumes) you create to run your Kubernetes worker nodes. See detailed pricing information on the Amazon EC2 pricing page. If you are using Amazon 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 1 minute applies. See detailed pricing information on the Amazon Fargate pricing page.
Amazon EMR pricing on EKS is calculated based on the vCPU and memory resources used from the time you start to download your EMR application image until the Amazon EKS Pod terminates, rounded up to the nearest second. Pricing is based on requested vCPU and memory resources for the Task or Pod.
| Type | China (Ningxia) Region | China (Beijing) Region |
| Per vCPU per Hour | ¥ 0.0902975 | ¥ 0.0902975 |
| Per GB per Hour | ¥ 0.00995675 | ¥ 0.00995675 |
Pricing Example
Pricing based on China (Beijing) Region pricing.
Suppose you are running an 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 6400GB of total memory. Let’s assume that that application utilizes 100 VCPUs and 300GB of memory for 30 minutes.
Total EMR uplift charges for the job:
- Total Uplift on vCPU = (100 * ¥0.0902975 * 0.5) = (number of vCPU * per vCPU-hours rate * job runtime in hour) = ¥4.51
- Total Uplift on memory = (300 * ¥0.00995675 * 0.5) = (amount of memory used * per GB-hours rate * job runtime in hour) = ¥1.49
- Total EMR Uplift for the EMR job = ¥6.01
Additional Costs
You pay ¥0.688 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 Amazon Web Services using either Amazon EC2 or Amazon Fargate.
If you are using EC2 (including with EKS managed node groups), you pay for Amazon Web Services resources (e.g., EC2 instances or 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 Amazon EC2 pricing page.
If you are using Amazon 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 1 minute applies. See detailed pricing information on the Amazon Fargate pricing page.
Pricing for Amazon EMR Serverless
This pricing is for EMR Serverless.
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.
Note: When using custom images, you are charged for aggregate vCPU, memory, and storage resources used from the time EMR Serverless starts downloading the image until the workers are stopped, rounded up to the nearest second with a 1-minute minimum.
Pricing details (compute and memory)
Pricing is based on vCPU, memory, and storage resources used by workers, aggregated across all workers.
Linux/x86
| Dimension | China (Ningxia) Region | China (Beijing) Region |
| Per vCPU per Hour | ¥ 0.324935 | ¥ 0.469547 |
| Per GB per Hour | ¥ 0.0357071 | ¥ 0.0517751 |
Linux/ARM
| Dimension | China (Ningxia) Region | China (Beijing) Region |
| Per vCPU per Hour | ¥ 0.276422 | ¥ 0.38859 |
| Per GB per Hour | ¥ 0.030353 | ¥ 0.04265 |
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.
| Dimension | China (Ningxia) Region | China (Beijing) Region |
| Per storage GB per Hour | ¥ 0.000738 | ¥ 0.000829 |
Supported worker configurations
| CPU | Memory Values | Ephemeral Storage |
| 1 vCPU | Min. 2 GB and Max. 8 GB, in 1 GB increments | 20 GB - 200 GB |
| 2 vCPU | Min. 4 GB and Max. 16 GB, in 1 GB increments | 20 GB - 200 GB |
| 4 vCPU | Min. 8 GB and Max. 30 GB, in 1 GB increments | 20 GB - 200 GB |
| 8 vCPU | Min. 16 GB and Max. 60 GB, in 4 GB increments | 20 GB - 200 GB |
| 16 vCPU | Min. 32 GB and Max. 120 GB, in 8 GB increments | 20 GB - 200 GB |
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 services of Amazon Web 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 Examples
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 * ¥ 0.0902975 * 0.5) + (200 * ¥ 0.0902975 * 0.25) = (number of vCPU * per vCPU-hours rate * job runtime in hour) = ¥ 9.02975
Total GB-hours = (750 * ¥ 0.00995675 * 0.5) + (1500 * ¥ 0.00995675 * 0.25) = (Total GB of memory configured * per GB-hours rate * job runtime in hour) = ¥ 7.4675625
Total EMR Serverless Charges = ¥ 9.02975 + ¥ 7.4675625 = ¥ 16.4973125
Additional Charges: If your application uses other services of Amazon Web Services such as Amazon S3, you are charged standard S3 rates.