云服务器租用价格表【阿里云篇】
云服务器的租用价格主要就是看配置，而配置主要就是实例规格，cpu,内存,带宽这几个因素,以阿里云服务器租用价格来说，企业级实例规格的价格要高于个人级实例规格，实例规格越高,服务器的性能就越好,但价格就越高。一般来说1核cpu,2G内存,1MB带宽的云服务器在绝大部分的云服务商那里活动价格都是100元左右。一般的云服务商官网都会有产品定价页面，通过产品定价页面即可查询到实时的云服务器价格。附：阿里云服务器的产品定价页面：
https://upload-images.jianshu.io/upload_images/19316870-77d496ab03dab0e0.png
<div class="image-caption">阿里云服务器租用价格表.png
在阿里云服务器定价页面我们可以查询到不同地域、不同实例规格、不同级别、不同操作系统的云服务器租用价格表，另外还可以查询到最新的带宽和云盘等价格表。下面为大家列出阿里云华东1（杭州）地域，企业级，linux操作系统云服务器的最新租用价格表：
福利推荐：购买阿里云服务器之前，推荐先上阿里云官方云小站领取最新代金券，使用代金券可以在购买中抵用现金使用。
https://upload-images.jianshu.io/upload_images/19316870-8491ecc2edeb70de.png
<div class="image-caption">云小站2.png
阿里云服务器杭州地域、企业级、linux操作系统租用价格表（包括按量、标准目录月价、优惠月价、年付月价、3年付月价、5年付月价）
<table>
<thead>
<tr>
<th>实例规格</th>
<th>vCPU</th>
<th>内存(GB)</th>
<th>按量(小时)</th>
<th>标准目录月价</th>
<th>优惠月价</th>
<th>年付月价</th>
<th>3年付月价</th>
<th>5年付月价</th>
</tr>
</thead>
<tbody>
<tr>
<td>通用型 ecs.g6.xlarge</td>
<td>4</td>
<td>16</td>
<td>1</td>
<td>480</td>
<td>480</td>
<td>408</td>
<td>264</td>
<td>182.4</td>
</tr>
<tr>
<td>通用型 ecs.g6.2xlarge</td>
<td>8</td>
<td>32</td>
<td>2</td>
<td>960</td>
<td>960</td>
<td>816</td>
<td>528</td>
<td>364.8</td>
</tr>
<tr>
<td>通用型 ecs.g6.3xlarge</td>
<td>12</td>
<td>48</td>
<td>3</td>
<td>1440</td>
<td>1440</td>
<td>1224</td>
<td>792</td>
<td>547.2</td>
</tr>
<tr>
<td>通用型 ecs.g6.4xlarge</td>
<td>16</td>
<td>64</td>
<td>4</td>
<td>1920</td>
<td>1920</td>
<td>1632</td>
<td>1056</td>
<td>729.6</td>
</tr>
<tr>
<td>通用型 ecs.g6.6xlarge</td>
<td>24</td>
<td>96</td>
<td>6</td>
<td>2880</td>
<td>2880</td>
<td>2448</td>
<td>1570</td>
<td>1080.4</td>
</tr>
<tr>
<td>通用型 ecs.g6.8xlarge</td>
<td>32</td>
<td>128</td>
<td>8</td>
<td>3840</td>
<td>3840</td>
<td>3264</td>
<td>2098</td>
<td>1445.2</td>
</tr>
<tr>
<td>通用型 ecs.g6.13xlarge</td>
<td>52</td>
<td>192</td>
<td>13</td>
<td>6240</td>
<td>6240</td>
<td>5304</td>
<td>3432</td>
<td>2371.2</td>
</tr>
<tr>
<td>通用型 ecs.g6.26xlarge</td>
<td>104</td>
<td>384</td>
<td>26</td>
<td>12480</td>
<td>12480</td>
<td>10608</td>
<td>6864</td>
<td>4742.4</td>
</tr>
<tr>
<td>通用平衡增强型 ecs.g6e.large</td>
<td>2</td>
<td>8</td>
<td>0.55</td>
<td>264</td>
<td>264</td>
<td>224.4</td>
<td>131.2</td>
<td>86.32</td>
</tr>
<tr>
<td>通用平衡增强型 ecs.g6e.xlarge</td>
<td>4</td>
<td>16</td>
<td>1.1</td>
<td>528</td>
<td>528</td>
<td>448.8</td>
<td>290.4</td>
<td>200.64</td>
</tr>
<tr>
<td>通用平衡增强型 ecs.g6e.2xlarge</td>
<td>8</td>
<td>32</td>
<td>2.2</td>
<td>1056</td>
<td>1056</td>
<td>897.6</td>
<td>566.8</td>
<td>387.28</td>
</tr>
<tr>
<td>通用平衡增强型 ecs.g6e.4xlarge</td>
<td>16</td>
<td>64</td>
<td>4.4</td>
<td>2112</td>
<td>2112</td>
<td>1795.2</td>
<td>1161.6</td>
<td>802.56</td>
</tr>
<tr>
<td>通用平衡增强型 ecs.g6e.8xlarge</td>
<td>32</td>
<td>128</td>
<td>8.8</td>
<td>4224</td>
<td>4224</td>
<td>3590.4</td>
<td>2323.2</td>
<td>1605.12</td>
</tr>
<tr>
<td>通用平衡增强型 ecs.g6e.13xlarge</td>
<td>52</td>
<td>192</td>
<td>14.3</td>
<td>6864</td>
<td>6864</td>
<td>5834.4</td>
<td>3775.2</td>
<td>2608.32</td>
</tr>
<tr>
<td>通用型 ecs.g5.large</td>
<td>2</td>
<td>8</td>
<td>0.89</td>
<td>255</td>
<td>242.25</td>
<td>191.25</td>
<td>114.75</td>
<td>76.5</td>
</tr>
<tr>
<td>通用型 ecs.g5.xlarge</td>
<td>4</td>
<td>16</td>
<td>1.77</td>
<td>510</td>
<td>484.5</td>
<td>382.5</td>
<td>229.5</td>
<td>153</td>
</tr>
<tr>
<td>通用型 ecs.g5.2xlarge</td>
<td>8</td>
<td>32</td>
<td>3.54</td>
<td>1020</td>
<td>969</td>
<td>765</td>
<td>459</td>
<td>306</td>
</tr>
<tr>
<td>通用型 ecs.g5.3xlarge</td>
<td>12</td>
<td>48</td>
<td>5.31</td>
<td>1530</td>
<td>1453.5</td>
<td>1147.5</td>
<td>674.5</td>
<td>445</td>
</tr>
<tr>
<td>通用型 ecs.g5.4xlarge</td>
<td>16</td>
<td>64</td>
<td>7.08</td>
<td>2040</td>
<td>1938</td>
<td>1530</td>
<td>918</td>
<td>612</td>
</tr>
<tr>
<td>通用型 ecs.g5.6xlarge</td>
<td>24</td>
<td>96</td>
<td>10.63</td>
<td>3060</td>
<td>2907</td>
<td>2295</td>
<td>1377</td>
<td>918</td>
</tr>
<tr>
<td>通用型 ecs.g5.8xlarge</td>
<td>32</td>
<td>128</td>
<td>14.17</td>
<td>4080</td>
<td>3876</td>
<td>3060</td>
<td>1836</td>
<td>1224</td>
</tr>
<tr>
<td>通用型 ecs.g5.16xlarge</td>
<td>64</td>
<td>256</td>
<td>28.33</td>
<td>8160</td>
<td>7752</td>
<td>6120</td>
<td>3658</td>
<td>2434</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.large</td>
<td>2</td>
<td>8</td>
<td>1.082</td>
<td>311.75</td>
<td>311.75</td>
<td>264.99</td>
<td>278.99</td>
<td>278.99</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.xlarge</td>
<td>4</td>
<td>16</td>
<td>2.165</td>
<td>623.5</td>
<td>623.5</td>
<td>529.98</td>
<td>543.97</td>
<td>543.98</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.2xlarge</td>
<td>8</td>
<td>32</td>
<td>4.33</td>
<td>1247</td>
<td>1247</td>
<td>1059.95</td>
<td>1073.95</td>
<td>1073.95</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.4xlarge</td>
<td>16</td>
<td>64</td>
<td>8.66</td>
<td>2494</td>
<td>2494</td>
<td>2119.9</td>
<td>2133.9</td>
<td>2133.9</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.8xlarge</td>
<td>32</td>
<td>128</td>
<td>17.32</td>
<td>4988</td>
<td>4988</td>
<td>4239.8</td>
<td>4239.8</td>
<td>4239.8</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.16xlarge</td>
<td>64</td>
<td>256</td>
<td>34.64</td>
<td>9976</td>
<td>9976</td>
<td>8479.6</td>
<td>8493.6</td>
<td>8493.6</td>
</tr>
<tr>
<td>网络增强型 ecs.g5ne.18xlarge</td>
<td>72</td>
<td>288</td>
<td>38.97</td>
<td>11223</td>
<td>11223</td>
<td>9539.55</td>
<td>9553.55</td>
<td>9553.55</td>
</tr>
<tr>
<td>密集计算型 ecs.ic5.large</td>
<td>2</td>
<td>2</td>
<td>0.59</td>
<td>170</td>
<td>170</td>
<td>144.5</td>
<td>93.5</td>
<td>64.6</td>
</tr>
<tr>
<td>密集计算型 ecs.ic5.xlarge</td>
<td>4</td>
<td>4</td>
<td>1.18</td>
<td>340</td>
<td>340</td>
<td>289</td>
<td>187</td>
<td>129.2</td>
</tr>
<tr>
<td>密集计算型 ecs.ic5.2xlarge</td>
<td>8</td>
<td>8</td>
<td>2.36</td>
<td>680</td>
<td>680</td>
<td>578</td>
<td>374</td>
<td>258.4</td>
</tr>
<tr>
<td>密集计算型 ecs.ic5.3xlarge</td>
<td>12</td>
<td>12</td>
<td>3.54</td>
<td>1020</td>
<td>1020</td>
<td>867</td>
<td>561</td>
<td>387.6</td>
</tr>
<tr>
<td>密集计算型 ecs.ic5.4xlarge</td>
<td>16</td>
<td>16</td>
<td>4.72</td>
<td>1360</td>
<td>1360</td>
<td>1156</td>
<td>748</td>
<td>516.8</td>
</tr>
<tr>
<td>计算型 ecs.c6.large</td>
<td>2</td>
<td>4</td>
<td>0.39</td>
<td>187</td>
<td>187</td>
<td>158.95</td>
<td>102.85</td>
<td>71.06</td>
</tr>
<tr>
<td>计算型 ecs.c6.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.78</td>
<td>374</td>
<td>374</td>
<td>317.9</td>
<td>205.7</td>
<td>142.12</td>
</tr>
<tr>
<td>计算型 ecs.c6.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.56</td>
<td>748</td>
<td>748</td>
<td>635.8</td>
<td>411.4</td>
<td>284.24</td>
</tr>
<tr>
<td>计算型 ecs.c6.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.34</td>
<td>1122</td>
<td>1122</td>
<td>953.7</td>
<td>603.1</td>
<td>412.36</td>
</tr>
<tr>
<td>计算型 ecs.c6.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.12</td>
<td>1496</td>
<td>1496</td>
<td>1271.6</td>
<td>822.8</td>
<td>568.48</td>
</tr>
<tr>
<td>计算型 ecs.c6.6xlarge</td>
<td>24</td>
<td>48</td>
<td>4.68</td>
<td>2244</td>
<td>2244</td>
<td>1907.4</td>
<td>1234.2</td>
<td>852.72</td>
</tr>
<tr>
<td>计算型 ecs.c6.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.23</td>
<td>2992</td>
<td>2992</td>
<td>2543.2</td>
<td>1645.6</td>
<td>1136.96</td>
</tr>
<tr>
<td>计算型 ecs.c6.13xlarge</td>
<td>52</td>
<td>96</td>
<td>10.13</td>
<td>4862</td>
<td>4862</td>
<td>4132.7</td>
<td>2674.1</td>
<td>1847.56</td>
</tr>
<tr>
<td>计算型 ecs.c6.26xlarge</td>
<td>104</td>
<td>192</td>
<td>20.26</td>
<td>9724</td>
<td>9724</td>
<td>8265.4</td>
<td>5334.2</td>
<td>3681.12</td>
</tr>
<tr>
<td>计算平衡增强型 ecs.c6e.large</td>
<td>2</td>
<td>4</td>
<td>0.41</td>
<td>197</td>
<td>197</td>
<td>167.45</td>
<td>108.35</td>
<td>74.86</td>
</tr>
<tr>
<td>计算平衡增强型 ecs.c6e.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.821</td>
<td>394</td>
<td>394</td>
<td>334.9</td>
<td>202.7</td>
<td>135.72</td>
</tr>
<tr>
<td>计算平衡增强型 ecs.c6e.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.642</td>
<td>788</td>
<td>788</td>
<td>669.8</td>
<td>433.4</td>
<td>299.44</td>
</tr>
<tr>
<td>计算平衡增强型 ecs.c6e.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.283</td>
<td>1576</td>
<td>1576</td>
<td>1339.6</td>
<td>866.8</td>
<td>598.88</td>
</tr>
<tr>
<td>计算平衡增强型 ecs.c6e.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.567</td>
<td>3152</td>
<td>3152</td>
<td>2679.2</td>
<td>1733.6</td>
<td>1197.76</td>
</tr>
<tr>
<td>计算平衡增强型 ecs.c6e.13xlarge</td>
<td>52</td>
<td>96</td>
<td>10.671</td>
<td>5122</td>
<td>5122</td>
<td>4353.7</td>
<td>2803.1</td>
<td>1932.36</td>
</tr>
<tr>
<td>计算型 ecs.c5.large</td>
<td>2</td>
<td>4</td>
<td>0.62</td>
<td>179</td>
<td>179</td>
<td>152.15</td>
<td>98.45</td>
<td>66.23</td>
</tr>
<tr>
<td>计算型 ecs.c5.xlarge</td>
<td>4</td>
<td>8</td>
<td>1.24</td>
<td>358</td>
<td>358</td>
<td>304.3</td>
<td>196.9</td>
<td>132.46</td>
</tr>
<tr>
<td>计算型 ecs.c5.2xlarge</td>
<td>8</td>
<td>16</td>
<td>2.49</td>
<td>716</td>
<td>716</td>
<td>608.6</td>
<td>393.8</td>
<td>264.92</td>
</tr>
<tr>
<td>计算型 ecs.c5.3xlarge</td>
<td>12</td>
<td>24</td>
<td>3.73</td>
<td>1074</td>
<td>1074</td>
<td>912.9</td>
<td>590.7</td>
<td>397.38</td>
</tr>
<tr>
<td>计算型 ecs.c5.4xlarge</td>
<td>16</td>
<td>32</td>
<td>4.97</td>
<td>1432</td>
<td>1432</td>
<td>1217.2</td>
<td>773.6</td>
<td>515.84</td>
</tr>
<tr>
<td>计算型 ecs.c5.6xlarge</td>
<td>24</td>
<td>48</td>
<td>7.46</td>
<td>2148</td>
<td>2148</td>
<td>1825.8</td>
<td>1181.4</td>
<td>794.76</td>
</tr>
<tr>
<td>计算型 ecs.c5.8xlarge</td>
<td>32</td>
<td>64</td>
<td>9.94</td>
<td>2864</td>
<td>2864</td>
<td>2434.4</td>
<td>1575.2</td>
<td>1059.68</td>
</tr>
<tr>
<td>计算型 ecs.c5.16xlarge</td>
<td>64</td>
<td>128</td>
<td>19.89</td>
<td>5728</td>
<td>5728</td>
<td>4868.8</td>
<td>3150.4</td>
<td>2119.36</td>
</tr>
<tr>
<td>内存型 ecs.r6.large</td>
<td>2</td>
<td>16</td>
<td>0.66</td>
<td>318</td>
<td>318</td>
<td>270.3</td>
<td>174.9</td>
<td>120.84</td>
</tr>
<tr>
<td>内存型 ecs.r6.xlarge</td>
<td>4</td>
<td>32</td>
<td>1.33</td>
<td>636</td>
<td>636</td>
<td>540.6</td>
<td>349.8</td>
<td>241.68</td>
</tr>
<tr>
<td>内存型 ecs.r6.2xlarge</td>
<td>8</td>
<td>64</td>
<td>2.65</td>
<td>1272</td>
<td>1272</td>
<td>1081.2</td>
<td>699.6</td>
<td>483.36</td>
</tr>
<tr>
<td>内存型 ecs.r6.3xlarge</td>
<td>12</td>
<td>96</td>
<td>3.98</td>
<td>1908</td>
<td>1908</td>
<td>1621.8</td>
<td>1049.4</td>
<td>725.04</td>
</tr>
<tr>
<td>内存型 ecs.r6.4xlarge</td>
<td>16</td>
<td>128</td>
<td>5.3</td>
<td>2544</td>
<td>2544</td>
<td>2162.4</td>
<td>1399.2</td>
<td>966.72</td>
</tr>
<tr>
<td>内存型 ecs.r6.6xlarge</td>
<td>24</td>
<td>192</td>
<td>7.95</td>
<td>3816</td>
<td>3816</td>
<td>3243.6</td>
<td>2098.8</td>
<td>1450.08</td>
</tr>
<tr>
<td>内存型 ecs.r6.8xlarge</td>
<td>32</td>
<td>256</td>
<td>10.6</td>
<td>5088</td>
<td>5088</td>
<td>4324.8</td>
<td>2798.4</td>
<td>1933.44</td>
</tr>
<tr>
<td>内存型 ecs.r6.13xlarge</td>
<td>52</td>
<td>384</td>
<td>17.23</td>
<td>8268</td>
<td>8268</td>
<td>7027.8</td>
<td>4547.4</td>
<td>3141.84</td>
</tr>
<tr>
<td>内存型 ecs.r6.26xlarge</td>
<td>104</td>
<td>768</td>
<td>34.45</td>
<td>16536</td>
<td>16536</td>
<td>14055.6</td>
<td>9094.8</td>
<td>6283.68</td>
</tr>
<tr>
<td>内存平衡增强型 ecs.r6e.large</td>
<td>2</td>
<td>16</td>
<td>0.729</td>
<td>350</td>
<td>350</td>
<td>297.5</td>
<td>192.5</td>
<td>133</td>
</tr>
<tr>
<td>内存平衡增强型 ecs.r6e.xlarge</td>
<td>4</td>
<td>32</td>
<td>1.458</td>
<td>700</td>
<td>700</td>
<td>595</td>
<td>371</td>
<td>252</td>
</tr>
<tr>
<td>内存平衡增强型 ecs.r6e.2xlarge</td>
<td>8</td>
<td>64</td>
<td>2.917</td>
<td>1400</td>
<td>1400</td>
<td>1190</td>
<td>756</td>
<td>518</td>
</tr>
<tr>
<td>内存型 ecs.r5.large</td>
<td>2</td>
<td>16</td>
<td>1.13</td>
<td>326</td>
<td>309.7</td>
<td>244.5</td>
<td>132.7</td>
<td>83.8</td>
</tr>
<tr>
<td>内存型 ecs.r5.xlarge</td>
<td>4</td>
<td>32</td>
<td>2.26</td>
<td>652</td>
<td>619.4</td>
<td>489</td>
<td>279.4</td>
<td>181.6</td>
</tr>
<tr>
<td>内存型 ecs.r5.2xlarge</td>
<td>8</td>
<td>64</td>
<td>4.53</td>
<td>1304</td>
<td>1238.8</td>
<td>978</td>
<td>586.8</td>
<td>391.2</td>
</tr>
<tr>
<td>内存型 ecs.r5.3xlarge</td>
<td>12</td>
<td>96</td>
<td>6.79</td>
<td>1956</td>
<td>1858.2</td>
<td>1467</td>
<td>880.2</td>
<td>586.8</td>
</tr>
<tr>
<td>内存型 ecs.r5.4xlarge</td>
<td>16</td>
<td>128</td>
<td>9.06</td>
<td>2608</td>
<td>2477.6</td>
<td>1956</td>
<td>1159.6</td>
<td>768.4</td>
</tr>
<tr>
<td>内存型 ecs.r5.6xlarge</td>
<td>24</td>
<td>192</td>
<td>13.58</td>
<td>3912</td>
<td>3716.4</td>
<td>2934</td>
<td>1760.4</td>
<td>1173.6</td>
</tr>
<tr>
<td>内存型 ecs.r5.8xlarge</td>
<td>32</td>
<td>256</td>
<td>18.11</td>
<td>5216</td>
<td>4955.2</td>
<td>3912</td>
<td>2347.2</td>
<td>1564.8</td>
</tr>
<tr>
<td>内存型 ecs.r5.16xlarge</td>
<td>64</td>
<td>512</td>
<td>36.22</td>
<td>10432</td>
<td>9910.4</td>
<td>7824</td>
<td>4694.4</td>
<td>3129.6</td>
</tr>
<tr>
<td>大数据存储密集型 ecs.d2s.5xlarge</td>
<td>20</td>
<td>88</td>
<td>14.73</td>
<td>4242</td>
<td>4242</td>
<td>3605.7</td>
<td>2333.1</td>
<td>1611.96</td>
</tr>
<tr>
<td>大数据存储密集型 ecs.d2s.10xlarge</td>
<td>40</td>
<td>176</td>
<td>29.46</td>
<td>8484</td>
<td>8484</td>
<td>7211.4</td>
<td>4652.2</td>
<td>3209.92</td>
</tr>
<tr>
<td>大数据存储密集型 ecs.d2s.20xlarge</td>
<td>80</td>
<td>352</td>
<td>58.92</td>
<td>16968</td>
<td>16968</td>
<td>14422.8</td>
<td>9332.4</td>
<td>6447.84</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne.2xlarge</td>
<td>8</td>
<td>32</td>
<td>6.68</td>
<td>1925</td>
<td>1828.75</td>
<td>1443.75</td>
<td>866.25</td>
<td>577.5</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne.4xlarge</td>
<td>16</td>
<td>64</td>
<td>13.37</td>
<td>3850</td>
<td>3657.5</td>
<td>2887.5</td>
<td>1732.5</td>
<td>1155</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne.6xlarge</td>
<td>24</td>
<td>96</td>
<td>20.05</td>
<td>5775</td>
<td>5486.25</td>
<td>4331.25</td>
<td>2598.75</td>
<td>1732.5</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne-c8d3.8xlarge</td>
<td>32</td>
<td>128</td>
<td>25.66</td>
<td>7391</td>
<td>7021.45</td>
<td>5543.25</td>
<td>3325.95</td>
<td>2217.3</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne.8xlarge</td>
<td>32</td>
<td>128</td>
<td>26.74</td>
<td>7700</td>
<td>7315</td>
<td>5775</td>
<td>3465</td>
<td>2310</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne-c14d3.14xlarge</td>
<td>56</td>
<td>160</td>
<td>38.92</td>
<td>11209</td>
<td>10648.55</td>
<td>8406.75</td>
<td>5044.05</td>
<td>3362.7</td>
</tr>
<tr>
<td>大数据网络增强型 ecs.d1ne.14xlarge</td>
<td>56</td>
<td>224</td>
<td>46.79</td>
<td>13475</td>
<td>12801.25</td>
<td>10106.25</td>
<td>6063.75</td>
<td>4042.5</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2.xlarge</td>
<td>4</td>
<td>32</td>
<td>1.88</td>
<td>904</td>
<td>904</td>
<td>768.4</td>
<td>497.2</td>
<td>343.52</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2.2xlarge</td>
<td>8</td>
<td>64</td>
<td>3.77</td>
<td>1808</td>
<td>1808</td>
<td>1536.8</td>
<td>994.4</td>
<td>687.04</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2.4xlarge</td>
<td>16</td>
<td>128</td>
<td>7.53</td>
<td>3616</td>
<td>3616</td>
<td>3073.6</td>
<td>1988.8</td>
<td>1374.08</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2.8xlarge</td>
<td>32</td>
<td>256</td>
<td>15.07</td>
<td>7232</td>
<td>7232</td>
<td>6147.2</td>
<td>3963.6</td>
<td>2734.16</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2.16xlarge</td>
<td>64</td>
<td>512</td>
<td>30.13</td>
<td>14464</td>
<td>14464</td>
<td>12294.4</td>
<td>7955.2</td>
<td>5496.32</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2g.2xlarge</td>
<td>8</td>
<td>32</td>
<td>3.073</td>
<td>1475</td>
<td>1475</td>
<td>1253.75</td>
<td>811.25</td>
<td>560.5</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2g.4xlarge</td>
<td>16</td>
<td>64</td>
<td>6.146</td>
<td>2950</td>
<td>2950</td>
<td>2507.5</td>
<td>1622.5</td>
<td>1121</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2g.8xlarge</td>
<td>32</td>
<td>128</td>
<td>12.292</td>
<td>5900</td>
<td>5900</td>
<td>5015</td>
<td>3245</td>
<td>2242</td>
</tr>
<tr>
<td>本地SSD型 ecs.i2g.16xlarge</td>
<td>64</td>
<td>256</td>
<td>24.584</td>
<td>11800</td>
<td>11800</td>
<td>10030</td>
<td>6490</td>
<td>4484</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.large</td>
<td>2</td>
<td>4</td>
<td>0.448</td>
<td>215</td>
<td>215</td>
<td>182.75</td>
<td>118.25</td>
<td>81.7</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.896</td>
<td>430</td>
<td>430</td>
<td>365.5</td>
<td>236.5</td>
<td>163.4</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.792</td>
<td>860</td>
<td>860</td>
<td>731</td>
<td>473</td>
<td>326.8</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.688</td>
<td>1290</td>
<td>1290</td>
<td>1096.5</td>
<td>709.5</td>
<td>490.2</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.583</td>
<td>1720</td>
<td>1720</td>
<td>1462</td>
<td>946</td>
<td>653.6</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.6xlarge</td>
<td>24</td>
<td>48</td>
<td>5.375</td>
<td>2580</td>
<td>2580</td>
<td>2193</td>
<td>1419</td>
<td>980.4</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.8xlarge</td>
<td>32</td>
<td>64</td>
<td>7.167</td>
<td>3440</td>
<td>3440</td>
<td>2924</td>
<td>1892</td>
<td>1307.2</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.10xlarge</td>
<td>40</td>
<td>96</td>
<td>8.958</td>
<td>4300</td>
<td>4300</td>
<td>3655</td>
<td>2365</td>
<td>1634</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.16xlarge</td>
<td>64</td>
<td>128</td>
<td>14.333</td>
<td>6880</td>
<td>6880</td>
<td>5848</td>
<td>3784</td>
<td>2614.4</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.20xlarge</td>
<td>80</td>
<td>192</td>
<td>17.917</td>
<td>8600</td>
<td>8600</td>
<td>7310</td>
<td>4730</td>
<td>3268</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.large</td>
<td>2</td>
<td>8</td>
<td>0.55</td>
<td>264</td>
<td>264</td>
<td>224.4</td>
<td>145.2</td>
<td>100.32</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.xlarge</td>
<td>4</td>
<td>16</td>
<td>1.1</td>
<td>528</td>
<td>528</td>
<td>448.8</td>
<td>276.4</td>
<td>186.64</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.2xlarge</td>
<td>8</td>
<td>32</td>
<td>2.2</td>
<td>1056</td>
<td>1056</td>
<td>897.6</td>
<td>580.8</td>
<td>401.28</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.3xlarge</td>
<td>12</td>
<td>48</td>
<td>3.3</td>
<td>1584</td>
<td>1584</td>
<td>1346.4</td>
<td>857.2</td>
<td>587.92</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.4xlarge</td>
<td>16</td>
<td>64</td>
<td>4.4</td>
<td>2112</td>
<td>2112</td>
<td>1795.2</td>
<td>1161.6</td>
<td>802.56</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.6xlarge</td>
<td>24</td>
<td>96</td>
<td>6.6</td>
<td>3168</td>
<td>3168</td>
<td>2692.8</td>
<td>1742.4</td>
<td>1203.84</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.8xlarge</td>
<td>32</td>
<td>128</td>
<td>8.8</td>
<td>4224</td>
<td>4224</td>
<td>3590.4</td>
<td>2323.2</td>
<td>1605.12</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.10xlarge</td>
<td>40</td>
<td>192</td>
<td>11</td>
<td>5280</td>
<td>5280</td>
<td>4488</td>
<td>2890</td>
<td>1992.4</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.16xlarge</td>
<td>64</td>
<td>256</td>
<td>17.6</td>
<td>8448</td>
<td>8448</td>
<td>7180.8</td>
<td>4646.4</td>
<td>3210.24</td>
</tr>
<tr>
<td>高主频通用型 ecs.hfg6.20xlarge</td>
<td>80</td>
<td>384</td>
<td>22</td>
<td>10560</td>
<td>10560</td>
<td>8976</td>
<td>5794</td>
<td>3998.8</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.large</td>
<td>2</td>
<td>16</td>
<td>0.729</td>
<td>350</td>
<td>350</td>
<td>297.5</td>
<td>192.5</td>
<td>133</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.xlarge</td>
<td>4</td>
<td>32</td>
<td>1.458</td>
<td>700</td>
<td>700</td>
<td>595</td>
<td>371</td>
<td>252</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.2xlarge</td>
<td>8</td>
<td>64</td>
<td>2.917</td>
<td>1400</td>
<td>1400</td>
<td>1190</td>
<td>756</td>
<td>518</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.3xlarge</td>
<td>12</td>
<td>96</td>
<td>4.375</td>
<td>2100</td>
<td>2100</td>
<td>1785</td>
<td>1155</td>
<td>798</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.4xlarge</td>
<td>16</td>
<td>128</td>
<td>5.833</td>
<td>2800</td>
<td>2800</td>
<td>2380</td>
<td>1540</td>
<td>1064</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.6xlarge</td>
<td>24</td>
<td>192</td>
<td>8.75</td>
<td>4200</td>
<td>4200</td>
<td>3570</td>
<td>2296</td>
<td>1582</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.8xlarge</td>
<td>32</td>
<td>256</td>
<td>11.667</td>
<td>5600</td>
<td>5600</td>
<td>4760</td>
<td>3080</td>
<td>2128</td>
</tr>
<tr>
<td>高主频内存型 ecs.hfr6.16xlarge</td>
<td>64</td>
<td>512</td>
<td>23.333</td>
<td>11200</td>
<td>11200</td>
<td>9520</td>
<td>6160</td>
<td>4256</td>
</tr>
<tr>
<td>内存增强型 ecs.re4.20xlarge</td>
<td>80</td>
<td>960</td>
<td>68.75</td>
<td>19800</td>
<td>19800</td>
<td>16830</td>
<td>9900</td>
<td>9900</td>
</tr>
<tr>
<td>内存增强型 ecs.re4.40xlarge</td>
<td>160</td>
<td>1920</td>
<td>137.5</td>
<td>39600</td>
<td>39600</td>
<td>33660</td>
<td>19800</td>
<td>19800</td>
</tr>
<tr>
<td>大数据型 ecs.d1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>6.36</td>
<td>1833</td>
<td>1741.35</td>
<td>1374.75</td>
<td>824.85</td>
<td>549.9</td>
</tr>
<tr>
<td>大数据型 ecs.d1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>12.73</td>
<td>3666</td>
<td>3482.7</td>
<td>2749.5</td>
<td>1649.7</td>
<td>1099.8</td>
</tr>
<tr>
<td>大数据型 ecs.d1.6xlarge</td>
<td>24</td>
<td>96</td>
<td>19.09</td>
<td>5499</td>
<td>5224.05</td>
<td>4124.25</td>
<td>2474.55</td>
<td>1649.7</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1.xlarge</td>
<td>4</td>
<td>16</td>
<td>2.94</td>
<td>649</td>
<td>616.55</td>
<td>486.75</td>
<td>278.05</td>
<td>180.7</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>5.89</td>
<td>1298</td>
<td>1233.1</td>
<td>973.5</td>
<td>584.1</td>
<td>389.4</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1.3xlarge</td>
<td>12</td>
<td>48</td>
<td>6.76</td>
<td>1947</td>
<td>1849.65</td>
<td>1460.25</td>
<td>862.15</td>
<td>570.1</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>11.77</td>
<td>2596</td>
<td>2466.2</td>
<td>1947</td>
<td>1168.2</td>
<td>778.8</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1-c5d1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>14.44</td>
<td>3365.44</td>
<td>3197.17</td>
<td>2524.08</td>
<td>1514.45</td>
<td>1009.63</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>23.54</td>
<td>5192</td>
<td>4932.4</td>
<td>3894</td>
<td>2336.4</td>
<td>1557.6</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1-c10d1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>25.14</td>
<td>5653.44</td>
<td>5370.77</td>
<td>4240.08</td>
<td>2544.05</td>
<td>1696.03</td>
</tr>
<tr>
<td>本地SSD型 ecs.i1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>37.24</td>
<td>9086</td>
<td>8631.7</td>
<td>6814.5</td>
<td>4088.7</td>
<td>2725.8</td>
</tr>
<tr>
<td>轻量级GPU计算型 ecs.vgn6i-m8.2xlarge</td>
<td>10</td>
<td>46</td>
<td>5.053</td>
<td>2425.56</td>
<td>2425.56</td>
<td>2061.72</td>
<td>2075.72</td>
<td>2075.72</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c4g1.xlarge</td>
<td>4</td>
<td>15</td>
<td>11.63</td>
<td>3348</td>
<td>3348</td>
<td>2845.8</td>
<td>1827.4</td>
<td>1258.24</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c8g1.2xlarge</td>
<td>8</td>
<td>31</td>
<td>14</td>
<td>4032</td>
<td>4032</td>
<td>3427.2</td>
<td>2217.6</td>
<td>1532.16</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c16g1.4xlarge</td>
<td>16</td>
<td>62</td>
<td>16.41</td>
<td>4725</td>
<td>4725</td>
<td>4016.25</td>
<td>2598.75</td>
<td>1795.5</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c24g1.6xlarge</td>
<td>24</td>
<td>93</td>
<td>17.19</td>
<td>4950</td>
<td>4950</td>
<td>4207.5</td>
<td>2722.5</td>
<td>1881</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c24g1.12xlarge</td>
<td>48</td>
<td>186</td>
<td>34.38</td>
<td>9900</td>
<td>9900</td>
<td>8415</td>
<td>5445</td>
<td>3762</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c24g1.24xlarge</td>
<td>96</td>
<td>372</td>
<td>68.75</td>
<td>19800</td>
<td>19800</td>
<td>16830</td>
<td>10890</td>
<td>7524</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6e-c12g1.3xlarge</td>
<td>12</td>
<td>92</td>
<td>19.739</td>
<td>9475</td>
<td>5779.75</td>
<td>4737.5</td>
<td>3965.5</td>
<td>3965.5</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6e-c12g1.12xlarge</td>
<td>48</td>
<td>368</td>
<td>78.958</td>
<td>37900</td>
<td>23119</td>
<td>18950</td>
<td>15918</td>
<td>15918</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6e-c12g1.24xlarge</td>
<td>96</td>
<td>736</td>
<td>157.916</td>
<td>75800</td>
<td>46238</td>
<td>37900</td>
<td>31836</td>
<td>31836</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c8g1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>105.84</td>
<td>30480</td>
<td>18592.8</td>
<td>15240</td>
<td>12801.6</td>
<td>11582.4</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c8g1.16xlarge</td>
<td>64</td>
<td>256</td>
<td>211.68</td>
<td>60960</td>
<td>37185.6</td>
<td>30480</td>
<td>25603.2</td>
<td>23164.8</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c10g1.20xlarge</td>
<td>82</td>
<td>336</td>
<td>219.64</td>
<td>63255</td>
<td>38585.55</td>
<td>31627.5</td>
<td>26567.1</td>
<td>24036.9</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c4g1.xlarge</td>
<td>4</td>
<td>30</td>
<td>12.78</td>
<td>3681</td>
<td>3681</td>
<td>3128.85</td>
<td>1914.12</td>
<td>1288.35</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.2xlarge</td>
<td>8</td>
<td>60</td>
<td>15.39</td>
<td>4433</td>
<td>4433</td>
<td>3768.05</td>
<td>2291.16</td>
<td>1537.55</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c4g1.2xlarge</td>
<td>8</td>
<td>60</td>
<td>25.57</td>
<td>7363</td>
<td>7363</td>
<td>6258.55</td>
<td>3828.76</td>
<td>2577.05</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.4xlarge</td>
<td>16</td>
<td>120</td>
<td>30.78</td>
<td>8866</td>
<td>8866</td>
<td>7536.1</td>
<td>4610.32</td>
<td>3103.1</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c28g1.7xlarge</td>
<td>28</td>
<td>112</td>
<td>23.88</td>
<td>6877</td>
<td>6877</td>
<td>5845.45</td>
<td>3576.04</td>
<td>2406.95</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.8xlarge</td>
<td>32</td>
<td>240</td>
<td>61.57</td>
<td>17731</td>
<td>17731</td>
<td>15071.35</td>
<td>9220.12</td>
<td>6205.85</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c28g1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>47.75</td>
<td>13753</td>
<td>13753</td>
<td>11690.05</td>
<td>7151.56</td>
<td>4813.55</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.14xlarge</td>
<td>54</td>
<td>480</td>
<td>123.13</td>
<td>35462</td>
<td>35462</td>
<td>30142.7</td>
<td>18440.24</td>
<td>12411.7</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c2g1.large</td>
<td>2</td>
<td>8</td>
<td>8.68</td>
<td>2500</td>
<td>2375</td>
<td>1875</td>
<td>1125</td>
<td>750</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c4g1.xlarge</td>
<td>4</td>
<td>16</td>
<td>9.69</td>
<td>2790</td>
<td>2650.5</td>
<td>2092.5</td>
<td>1241.5</td>
<td>823</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c8g1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>11.67</td>
<td>3360</td>
<td>3192</td>
<td>2520</td>
<td>1512</td>
<td>1008</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c16g1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>15.63</td>
<td>4500</td>
<td>4275</td>
<td>3375</td>
<td>2025</td>
<td>1350</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c28g1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>43.06</td>
<td>12400</td>
<td>11780</td>
<td>9300</td>
<td>5580</td>
<td>3720</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f1-c8f1.2xlarge</td>
<td>8</td>
<td>60</td>
<td>8.66</td>
<td>2495</td>
<td>2370.25</td>
<td>1871.25</td>
<td>1122.75</td>
<td>748.5</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f1-c8f1.4xlarge</td>
<td>16</td>
<td>120</td>
<td>17.33</td>
<td>4990</td>
<td>4740.5</td>
<td>3742.5</td>
<td>2245.5</td>
<td>1497</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f1-c28f1.7xlarge</td>
<td>28</td>
<td>112</td>
<td>15.14</td>
<td>4360</td>
<td>4142</td>
<td>3270</td>
<td>1962</td>
<td>1308</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f1-c28f1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>30.28</td>
<td>8720</td>
<td>8284</td>
<td>6540</td>
<td>3924</td>
<td>2616</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn6v.24xlarge</td>
<td>96</td>
<td>384</td>
<td>237.125</td>
<td>68292</td>
<td>41658.12</td>
<td>34146</td>
<td>28668.64</td>
<td>28668.64</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn6i.24xlarge</td>
<td>96</td>
<td>384</td>
<td>68.75</td>
<td>19800</td>
<td>19800</td>
<td>16830</td>
<td>10890</td>
<td>7524</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc6.26xlarge</td>
<td>104</td>
<td>192</td>
<td>20.26</td>
<td>9724</td>
<td>9724</td>
<td>8265.4</td>
<td>5348.2</td>
<td>3695.12</td>
</tr>
<tr>
<td>通用型弹性裸金属服务器 ecs.ebmg6.26xlarge</td>
<td>104</td>
<td>384</td>
<td>26</td>
<td>12480</td>
<td>12480</td>
<td>10608</td>
<td>6864</td>
<td>4742.4</td>
</tr>
<tr>
<td>内存型弹性裸金属服务器 ecs.ebmr6.26xlarge</td>
<td>104</td>
<td>768</td>
<td>34.45</td>
<td>16536</td>
<td>16536</td>
<td>14055.6</td>
<td>9094.8</td>
<td>6283.68</td>
</tr>
<tr>
<td>高主频计算型弹性裸金属服务器 ecs.ebmhfc6.20xlarge</td>
<td>80</td>
<td>192</td>
<td>17.917</td>
<td>8600</td>
<td>8600</td>
<td>7310</td>
<td>4730</td>
<td>3268</td>
</tr>
<tr>
<td>高主频通用型弹性裸金属服务器 ecs.ebmhfg6.20xlarge</td>
<td>80</td>
<td>384</td>
<td>22</td>
<td>10560</td>
<td>10560</td>
<td>8976</td>
<td>5808</td>
<td>4012.8</td>
</tr>
<tr>
<td>高主频内存型弹性裸金属服务器 ecs.ebmhfr6.20xlarge</td>
<td>80</td>
<td>768</td>
<td>29.167</td>
<td>14000</td>
<td>14000</td>
<td>11900</td>
<td>7700</td>
<td>5320</td>
</tr>
<tr>
<td>高主频型超级计算集群 ecs.scch5.16xlarge</td>
<td>64</td>
<td>192</td>
<td>42.36</td>
<td>12200</td>
<td>11590</td>
<td>9150</td>
<td>5490</td>
<td>3660</td>
</tr>
<tr>
<td>通用型超级计算集群 ecs.sccg5.24xlarge</td>
<td>96</td>
<td>384</td>
<td>44.63</td>
<td>12852</td>
<td>12209.4</td>
<td>9639</td>
<td>5783.4</td>
<td>3855.6</td>
</tr>
<tr>
<td>计算网络增强型弹性裸金属服务器 ecs.ebmc5s.24xlarge</td>
<td>96</td>
<td>192</td>
<td>29.83</td>
<td>8592</td>
<td>8592</td>
<td>7303.2</td>
<td>4725.6</td>
<td>3264.96</td>
</tr>
<tr>
<td>通用网络增强型弹性裸金属服务器 ecs.ebmg5s.24xlarge</td>
<td>96</td>
<td>384</td>
<td>42.5</td>
<td>12240</td>
<td>11628</td>
<td>9180</td>
<td>5508</td>
<td>3672</td>
</tr>
<tr>
<td>通用网络增强型弹性裸金属服务器 ecs.ebmr5s.24xlarge</td>
<td>96</td>
<td>768</td>
<td>54.33</td>
<td>15648</td>
<td>15648</td>
<td>13300.8</td>
<td>8606.4</td>
<td>5946.24</td>
</tr>
<tr>
<td>共享内存型 ecs.e4.small</td>
<td>1</td>
<td>8</td>
<td>0.9</td>
<td>260</td>
<td>260</td>
<td>221</td>
<td>130</td>
<td>130</td>
</tr>
</tbody>
</table>
阿里云服务器最新带宽价格表
<table>
<thead>
<tr>
<th>计费方式</th>
<th>类型</th>
<th>价格</th>
</tr>
</thead>
<tbody>
<tr>
<td>预付费, 按固定带宽阶梯计费</td>
<td>1Mbps</td>
<td>23.0 元/月</td>
</tr>
<tr>
<td>预付费, 按固定带宽阶梯计费</td>
<td>2Mbps</td>
<td>46.0 元/月</td>
</tr>
<tr>
<td>预付费, 按固定带宽阶梯计费</td>
<td>3Mbps</td>
<td>71.0 元/月</td>
</tr>
<tr>
<td>预付费, 按固定带宽阶梯计费</td>
<td>4Mbps</td>
<td>96.0 元/月</td>
</tr>
<tr>
<td>预付费, 按固定带宽阶梯计费</td>
<td>5Mbps</td>
<td>125.0 元/月</td>
</tr>
<tr>
<td>预付费, 按固定带宽阶梯计费</td>
<td>6Mbps及以上，每Mbps费用</td>
<td>80.0 元/月</td>
</tr>
<tr>
<td>按量，按固定带宽阶梯计费</td>
<td>1－5 Mbps</td>
<td>0.063 元/小时</td>
</tr>
<tr>
<td>按量，按固定带宽阶梯计费</td>
<td>6Mbps及以上，每Mbps费用</td>
<td>0.248 元/小时</td>
</tr>
<tr>
<td>按使用量线性计费</td>
<td>1GB</td>
<td>0.8 元/小时</td>
</tr>
</tbody>
</table>
需要注意的是，带宽收费中，6M是一个分水岭，超过6M以上的带宽每M带宽的收费价格明显上涨。
其他地域和不同级别，不同操作系统的实时租用价格表，上阿里云服务器的产品定价页面查询即可。
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