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阿里云服务器的租用价格表主要包含最新的实例价格表、带宽价格表、磁盘价格表，了解阿里云服务器最新的租用价格表，可以方便我们根据预算和需求选择合适的阿里云服务器配置。
<h5>目录：</h5>
一.阿里云服务器最新实例价格表
二.阿里云服务器最新带宽价格表
三.阿里云服务器最新磁盘价格表
需要注意的是：按量、按月、按年付的价格会有所不同，一般按年付都是有优惠的，不同地域价格也有所差异，以下是2023年截至到目前为止最新的阿里云服务器租用价格表。另外，我们在实际购买过程中可以先免费领取阿里云最新的代金券和9折优惠码，在结算订单的时候可以使用代金券和9折优惠码抵扣订单金额，达到优惠购买的目的。领取地址为：阿里云-云小站
<div class="image-caption">云小站.png
一.阿里云服务器最新实例价格表
<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 (g6) ecs.g6.large</td>
<td>2</td>
<td>8</td>
<td>0.5</td>
<td>240</td>
<td>240</td>
<td>204</td>
<td>132</td>
<td>91.2</td>
</tr>
<tr>
<td>通用型 (g6) 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 (g6) 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 (g6) 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 (g6) 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 (g6) ecs.g6.6xlarge</td>
<td>24</td>
<td>96</td>
<td>6</td>
<td>2880</td>
<td>2880</td>
<td>2448</td>
<td>1584</td>
<td>1094.4</td>
</tr>
<tr>
<td>通用型 (g6) ecs.g6.8xlarge</td>
<td>32</td>
<td>128</td>
<td>8</td>
<td>3840</td>
<td>3840</td>
<td>3264</td>
<td>2112</td>
<td>1459.2</td>
</tr>
<tr>
<td>通用型 (g6) 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 (g6) 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>内存型 (r6) 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 (r6) 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 (r6) 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 (r6) 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 (r6) 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 (r6) 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 (r6) 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 (r6) 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 (r6) 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>计算型 (c6) 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 (c6) 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 (c6) 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 (c6) 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>617.1</td>
<td>426.36</td>
</tr>
<tr>
<td>计算型 (c6) 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 (c6) 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 (c6) 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 (c6) ecs.c6.13xlarge</td>
<td>52</td>
<td>104</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 (c6) 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>5348.2</td>
<td>3695.12</td>
</tr>
<tr>
<td>通用型 (g5) 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 (g5) 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 (g5) 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 (g5) 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>688.5</td>
<td>459</td>
</tr>
<tr>
<td>通用型 (g5) 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 (g5) 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 (g5) 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 (g5) ecs.g5.16xlarge</td>
<td>64</td>
<td>256</td>
<td>28.33</td>
<td>8160</td>
<td>7752</td>
<td>6120</td>
<td>3672</td>
<td>2448</td>
</tr>
<tr>
<td>计算型 (c5) 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 (c5) 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 (c5) 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 (c5) 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 (c5) 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>787.6</td>
<td>529.84</td>
</tr>
<tr>
<td>计算型 (c5) 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 (c5) 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 (c5) 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 (r5) 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>146.7</td>
<td>97.8</td>
</tr>
<tr>
<td>内存型 (r5) 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>293.4</td>
<td>195.6</td>
</tr>
<tr>
<td>内存型 (r5) 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 (r5) 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 (r5) 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>1173.6</td>
<td>782.4</td>
</tr>
<tr>
<td>内存型 (r5) 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 (r5) 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 (r5) 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>密集计算型 (ic5) 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 (ic5) 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 (ic5) 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 (ic5) 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 (ic5) 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>GPU计算型 (gn5) 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 (gn5) 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>2305.16</td>
<td>1551.55</td>
</tr>
<tr>
<td>GPU计算型 (gn5) 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 (gn5) 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 (gn5) 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 (gn5) 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 (gn5) 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 (gn5) 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 (gn6i) 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>1841.4</td>
<td>1272.24</td>
</tr>
<tr>
<td>GPU计算型 (gn6i) 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 (gn6i) 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 (gn6i) 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 (gn6i) 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 (gn6i) 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 (gn6v) ecs.gn6v-c8g1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>26.46</td>
<td>7620</td>
<td>4648.2</td>
<td>3810</td>
<td>3200.4</td>
<td>2895.6</td>
</tr>
<tr>
<td>GPU计算型 (gn6v) 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 (gn6v) 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 (gn6v) ecs.gn6v-c10g1.20xlarge</td>
<td>96</td>
<td>384</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计算型弹性裸金属服务器 (ebmgn6i) 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>GPU计算型 (gn5i) 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 (gn5i) 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>1255.5</td>
<td>837</td>
</tr>
<tr>
<td>GPU计算型 (gn5i) 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 (gn5i) 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 (gn5i) 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>高主频计算型 (hfc5) ecs.hfc5.large</td>
<td>2</td>
<td>4</td>
<td>0.87</td>
<td>251</td>
<td>251</td>
<td>208.33</td>
<td>125.5</td>
<td>82.83</td>
</tr>
<tr>
<td>高主频计算型 (hfc5) ecs.hfc5.xlarge</td>
<td>4</td>
<td>8</td>
<td>1.74</td>
<td>502</td>
<td>502</td>
<td>416.66</td>
<td>251</td>
<td>165.66</td>
</tr>
<tr>
<td>高主频计算型 (hfc5) ecs.hfc5.2xlarge</td>
<td>8</td>
<td>16</td>
<td>3.49</td>
<td>1004</td>
<td>1004</td>
<td>833.32</td>
<td>502</td>
<td>331.32</td>
</tr>
<tr>
<td>高主频计算型 (hfc5) ecs.hfc5.3xlarge</td>
<td>12</td>
<td>24</td>
<td>5.23</td>
<td>1506</td>
<td>1506</td>
<td>1249.98</td>
<td>753</td>
<td>496.98</td>
</tr>
<tr>
<td>高主频计算型 (hfc5) ecs.hfc5.4xlarge</td>
<td>16</td>
<td>32</td>
<td>6.97</td>
<td>2008</td>
<td>2008</td>
<td>1666.64</td>
<td>1004</td>
<td>662.64</td>
</tr>
<tr>
<td>高主频计算型 (hfc5) ecs.hfc5.6xlarge</td>
<td>24</td>
<td>48</td>
<td>10.46</td>
<td>3012</td>
<td>3012</td>
<td>2499.96</td>
<td>1506</td>
<td>993.96</td>
</tr>
<tr>
<td>高主频计算型 (hfc5) ecs.hfc5.8xlarge</td>
<td>32</td>
<td>64</td>
<td>13.94</td>
<td>4016</td>
<td>4016</td>
<td>3333.28</td>
<td>2008</td>
<td>1325.28</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.large</td>
<td>2</td>
<td>8</td>
<td>1.15</td>
<td>332</td>
<td>332</td>
<td>268.92</td>
<td>162.68</td>
<td>106.24</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.xlarge</td>
<td>4</td>
<td>16</td>
<td>2.31</td>
<td>664</td>
<td>664</td>
<td>537.84</td>
<td>325.36</td>
<td>212.48</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.2xlarge</td>
<td>8</td>
<td>32</td>
<td>4.61</td>
<td>1328</td>
<td>1328</td>
<td>1075.68</td>
<td>650.72</td>
<td>424.96</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.3xlarge</td>
<td>12</td>
<td>48</td>
<td>6.92</td>
<td>1992</td>
<td>1992</td>
<td>1613.52</td>
<td>976.08</td>
<td>637.44</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.4xlarge</td>
<td>16</td>
<td>64</td>
<td>9.22</td>
<td>2656</td>
<td>2656</td>
<td>2151.36</td>
<td>1301.44</td>
<td>849.92</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.6xlarge</td>
<td>24</td>
<td>96</td>
<td>13.83</td>
<td>3984</td>
<td>3984</td>
<td>3227.04</td>
<td>1952.16</td>
<td>1274.88</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.8xlarge</td>
<td>32</td>
<td>128</td>
<td>18.44</td>
<td>5312</td>
<td>5312</td>
<td>4302.72</td>
<td>2602.88</td>
<td>1699.84</td>
</tr>
<tr>
<td>高主频通用型 (hfg5) ecs.hfg5.14xlarge</td>
<td>56</td>
<td>160</td>
<td>30.58</td>
<td>8808</td>
<td>8808</td>
<td>7134.48</td>
<td>4315.92</td>
<td>2818.56</td>
</tr>
<tr>
<td>GPU轻量型 (vgn5i) ecs.vgn5i-m1.large</td>
<td>2</td>
<td>6</td>
<td>1.95</td>
<td>562.5</td>
<td>562.5</td>
<td>478.13</td>
<td>309.38</td>
<td>213.75</td>
</tr>
<tr>
<td>GPU轻量型 (vgn5i) ecs.vgn5i-m2.xlarge</td>
<td>4</td>
<td>12</td>
<td>3.91</td>
<td>1125</td>
<td>1125</td>
<td>956.25</td>
<td>618.75</td>
<td>427.5</td>
</tr>
<tr>
<td>GPU轻量型 (vgn5i) ecs.vgn5i-m4.2xlarge</td>
<td>8</td>
<td>24</td>
<td>7.81</td>
<td>2250</td>
<td>2250</td>
<td>1912.5</td>
<td>1237.5</td>
<td>855</td>
</tr>
<tr>
<td>GPU轻量型 (vgn5i) ecs.vgn5i-m8.4xlarge</td>
<td>16</td>
<td>48</td>
<td>15.63</td>
<td>4500</td>
<td>4500</td>
<td>3825</td>
<td>2475</td>
<td>1710</td>
</tr>
<tr>
<td>通用型弹性裸金属服务器 (ebmg5) ecs.ebmg5.24xlarge</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>------------------------------------------------------</td>
<td>---</td>
<td>----</td>
<td>-----</td>
<td>-------</td>
<td>--------</td>
<td>--------</td>
<td>-------</td>
<td>-------</td>
</tr>
<tr>
<td>高主频型弹性裸金属服务器 (ebmhfg5) ecs.ebmhfg5.2xlarge</td>
<td>8</td>
<td>32</td>
<td>5.53</td>
<td>1594</td>
<td>1594</td>
<td>1291.14</td>
<td>781.06</td>
<td>510.08</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 (ebmc4) ecs.ebmc4.8xlarge</td>
<td>32</td>
<td>64</td>
<td>9.84</td>
<td>3150</td>
<td>3150</td>
<td>2677.5</td>
<td>1732.5</td>
<td>1197</td>
</tr>
<tr>
<td>高主频型超级计算集群 (scch5) 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 (sccg5) 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>内存增强型 (re4) 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 (re4) 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>计算网络增强型 (sn1ne) ecs.sn1ne.large</td>
<td>2</td>
<td>4</td>
<td>0.68</td>
<td>197</td>
<td>197</td>
<td>167.45</td>
<td>108.35</td>
<td>74.86</td>
</tr>
<tr>
<td>计算网络增强型 (sn1ne) ecs.sn1ne.xlarge</td>
<td>4</td>
<td>8</td>
<td>1.37</td>
<td>394</td>
<td>394</td>
<td>334.9</td>
<td>216.7</td>
<td>149.72</td>
</tr>
<tr>
<td>计算网络增强型 (sn1ne) ecs.sn1ne.2xlarge</td>
<td>8</td>
<td>16</td>
<td>2.74</td>
<td>788</td>
<td>788</td>
<td>669.8</td>
<td>433.4</td>
<td>299.44</td>
</tr>
<tr>
<td>计算网络增强型 (sn1ne) ecs.sn1ne.3xlarge</td>
<td>12</td>
<td>24</td>
<td>4.1</td>
<td>1182</td>
<td>1182</td>
<td>1004.7</td>
<td>650.1</td>
<td>449.16</td>
</tr>
<tr>
<td>计算网络增强型 (sn1ne) ecs.sn1ne.4xlarge</td>
<td>16</td>
<td>32</td>
<td>5.47</td>
<td>1576</td>
<td>1576</td>
<td>1339.6</td>
<td>866.8</td>
<td>598.88</td>
</tr>
<tr>
<td>计算网络增强型 (sn1ne) ecs.sn1ne.6xlarge</td>
<td>24</td>
<td>48</td>
<td>8.21</td>
<td>2364</td>
<td>2364</td>
<td>2009.4</td>
<td>1300.2</td>
<td>898.32</td>
</tr>
<tr>
<td>计算网络增强型 (sn1ne) ecs.sn1ne.8xlarge</td>
<td>32</td>
<td>64</td>
<td>10.94</td>
<td>3152</td>
<td>3152</td>
<td>2679.2</td>
<td>1733.6</td>
<td>1197.76</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.large</td>
<td>2</td>
<td>8</td>
<td>0.99</td>
<td>286</td>
<td>271.7</td>
<td>214.5</td>
<td>128.7</td>
<td>85.8</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.xlarge</td>
<td>4</td>
<td>16</td>
<td>1.99</td>
<td>572</td>
<td>543.4</td>
<td>429</td>
<td>257.4</td>
<td>171.6</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.2xlarge</td>
<td>8</td>
<td>32</td>
<td>3.97</td>
<td>1144</td>
<td>1086.8</td>
<td>858</td>
<td>514.8</td>
<td>343.2</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.3xlarge</td>
<td>12</td>
<td>48</td>
<td>5.96</td>
<td>1716</td>
<td>1630.2</td>
<td>1287</td>
<td>772.2</td>
<td>514.8</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.4xlarge</td>
<td>16</td>
<td>64</td>
<td>7.94</td>
<td>2288</td>
<td>2173.6</td>
<td>1716</td>
<td>1029.6</td>
<td>686.4</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.6xlarge</td>
<td>24</td>
<td>96</td>
<td>11.92</td>
<td>3432</td>
<td>3260.4</td>
<td>2574</td>
<td>1544.4</td>
<td>1029.6</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.8xlarge</td>
<td>32</td>
<td>128</td>
<td>15.89</td>
<td>4576</td>
<td>4347.2</td>
<td>3432</td>
<td>2059.2</td>
<td>1372.8</td>
</tr>
<tr>
<td>通用网络增强型 (sn2ne) ecs.sn2ne.14xlarge</td>
<td>56</td>
<td>224</td>
<td>27.81</td>
<td>8008</td>
<td>7607.6</td>
<td>6006</td>
<td>3603.6</td>
<td>2402.4</td>
</tr>
<tr>
<td>内存型 (se1) ecs.se1.large</td>
<td>2</td>
<td>16</td>
<td>1.53</td>
<td>366</td>
<td>366</td>
<td>311.1</td>
<td>183</td>
<td>183</td>
</tr>
<tr>
<td>内存型 (se1) ecs.se1.xlarge</td>
<td>4</td>
<td>32</td>
<td>3.07</td>
<td>732</td>
<td>732</td>
<td>622.2</td>
<td>366</td>
<td>366</td>
</tr>
<tr>
<td>内存型 (se1) ecs.se1.2xlarge</td>
<td>8</td>
<td>64</td>
<td>6.14</td>
<td>1464</td>
<td>1464</td>
<td>1244.4</td>
<td>732</td>
<td>732</td>
</tr>
<tr>
<td>内存型 (se1) ecs.se1.4xlarge</td>
<td>16</td>
<td>128</td>
<td>12.28</td>
<td>2928</td>
<td>2928</td>
<td>2488.8</td>
<td>1464</td>
<td>1464</td>
</tr>
<tr>
<td>内存型 (se1) ecs.se1.8xlarge</td>
<td>32</td>
<td>256</td>
<td>24.56</td>
<td>5856</td>
<td>5856</td>
<td>4977.6</td>
<td>2928</td>
<td>2928</td>
</tr>
<tr>
<td>内存型 (se1) ecs.se1.14xlarge</td>
<td>56</td>
<td>480</td>
<td>44.29</td>
<td>10248</td>
<td>10248</td>
<td>8710.8</td>
<td>5124</td>
<td>5124</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.large</td>
<td>2</td>
<td>16</td>
<td>1.27</td>
<td>366</td>
<td>347.7</td>
<td>274.5</td>
<td>164.7</td>
<td>109.8</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.xlarge</td>
<td>4</td>
<td>32</td>
<td>2.54</td>
<td>732</td>
<td>695.4</td>
<td>549</td>
<td>329.4</td>
<td>219.6</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.2xlarge</td>
<td>8</td>
<td>64</td>
<td>5.08</td>
<td>1464</td>
<td>1390.8</td>
<td>1098</td>
<td>658.8</td>
<td>439.2</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.3xlarge</td>
<td>12</td>
<td>96</td>
<td>7.63</td>
<td>2196</td>
<td>2086.2</td>
<td>1647</td>
<td>988.2</td>
<td>658.8</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.4xlarge</td>
<td>16</td>
<td>128</td>
<td>10.17</td>
<td>2928</td>
<td>2781.6</td>
<td>2196</td>
<td>1317.6</td>
<td>878.4</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.6xlarge</td>
<td>24</td>
<td>192</td>
<td>15.25</td>
<td>4392</td>
<td>4172.4</td>
<td>3294</td>
<td>1976.4</td>
<td>1317.6</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.8xlarge</td>
<td>32</td>
<td>256</td>
<td>20.33</td>
<td>5856</td>
<td>5563.2</td>
<td>4392</td>
<td>2635.2</td>
<td>1756.8</td>
</tr>
<tr>
<td>内存网络增强型 (se1ne) ecs.se1ne.14xlarge</td>
<td>56</td>
<td>480</td>
<td>35.58</td>
<td>10248</td>
<td>9735.6</td>
<td>7686</td>
<td>4611.6</td>
<td>3074.4</td>
</tr>
<tr>
<td>本地SSD型 (i1) 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>292.05</td>
<td>194.7</td>
</tr>
<tr>
<td>本地SSD型 (i1) 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 (i1) 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>876.15</td>
<td>584.1</td>
</tr>
<tr>
<td>本地SSD型 (i1) 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 (i1) 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 (i1) 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 (i1) 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 (i1) 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>本地SSD型 (i2) 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 (i2) 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 (i2) 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 (i2) 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>3977.6</td>
<td>2748.16</td>
</tr>
<tr>
<td>本地SSD型 (i2) 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>大数据型 (d1) 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 (d1) 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 (d1) 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>大数据型 (d1) ecs.d1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>25.46</td>
<td>7332</td>
<td>6965.4</td>
<td>5499</td>
<td>3299.4</td>
<td>2199.6</td>
</tr>
<tr>
<td>大数据型 (d1) ecs.d1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>44.55</td>
<td>12831</td>
<td>12189.45</td>
<td>9623.25</td>
<td>5773.95</td>
<td>3849.3</td>
</tr>
<tr>
<td>大数据网络增强型 (d1ne) 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 (d1ne) 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 (d1ne) 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 (d1ne) 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 (d1ne) 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 (d1ne) 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 (d1ne) 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>GPU计算型 (gn4) ecs.gn4-c4g1.xlarge</td>
<td>4</td>
<td>30</td>
<td>10.88</td>
<td>3134</td>
<td>2977.3</td>
<td>2350.5</td>
<td>1410.3</td>
<td>940.2</td>
</tr>
<tr>
<td>GPU计算型 (gn4) ecs.gn4-c8g1.2xlarge</td>
<td>8</td>
<td>30</td>
<td>12.41</td>
<td>3575</td>
<td>3396.25</td>
<td>2681.25</td>
<td>1608.75</td>
<td>1072.5</td>
</tr>
<tr>
<td>GPU计算型 (gn4) ecs.gn4-c4g1.2xlarge</td>
<td>8</td>
<td>60</td>
<td>21.76</td>
<td>6268</td>
<td>5954.6</td>
<td>4701</td>
<td>2820.6</td>
<td>1880.4</td>
</tr>
<tr>
<td>GPU计算型 (gn4) ecs.gn4-c8g1.4xlarge</td>
<td>16</td>
<td>60</td>
<td>24.83</td>
<td>7150</td>
<td>6792.5</td>
<td>5362.5</td>
<td>3217.5</td>
<td>2145</td>
</tr>
<tr>
<td>GPU计算型 (gn4) ecs.gn4.8xlarge</td>
<td>32</td>
<td>48</td>
<td>14.93</td>
<td>4300</td>
<td>4085</td>
<td>3225</td>
<td>1935</td>
<td>1290</td>
</tr>
<tr>
<td>GPU计算型 (gn4) ecs.gn4.14xlarge</td>
<td>56</td>
<td>96</td>
<td>29.86</td>
<td>8599</td>
<td>8169.05</td>
<td>6449.25</td>
<td>3869.55</td>
<td>2579.7</td>
</tr>
<tr>
<td>GPU可视化计算型 (ga1) ecs.ga1.4xlarge</td>
<td>16</td>
<td>40</td>
<td>8.79</td>
<td>2531</td>
<td>2404.45</td>
<td>1898.25</td>
<td>1138.95</td>
<td>759.3</td>
</tr>
<tr>
<td>GPU可视化计算型 (ga1) ecs.ga1.8xlarge</td>
<td>32</td>
<td>80</td>
<td>17.58</td>
<td>5062</td>
<td>4808.9</td>
<td>3796.5</td>
<td>2277.9</td>
<td>1518.6</td>
</tr>
<tr>
<td>GPU可视化计算型 (ga1) ecs.ga1.14xlarge</td>
<td>56</td>
<td>160</td>
<td>35.16</td>
<td>10125</td>
<td>9618.75</td>
<td>7593.75</td>
<td>4556.25</td>
<td>3037.5</td>
</tr>
<tr>
<td>FPGA计算型 (f3) ecs.f3-c16f1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>17.5</td>
<td>5040</td>
<td>5040</td>
<td>4284</td>
<td>2772</td>
<td>1915.2</td>
</tr>
<tr>
<td>FPGA计算型 (f3) ecs.f3-c16f1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>35</td>
<td>10080</td>
<td>10080</td>
<td>8568</td>
<td>5544</td>
<td>3830.4</td>
</tr>
<tr>
<td>FPGA计算型 (f3) ecs.f3-c16f1.16xlarge</td>
<td>64</td>
<td>256</td>
<td>70</td>
<td>20160</td>
<td>20160</td>
<td>17136</td>
<td>11088</td>
<td>7660.8</td>
</tr>
</tbody>
</table>
二.阿里云服务器最新带宽价格表
<table>
<thead>
<tr>
<th>计费方式</th>
<th>规格</th>
<th>价格</th>
</tr>
</thead>
<tbody>
<tr>
<td>包年包月实例，固定带宽阶梯计费</td>
<td>1Mbps</td>
<td>23.0 元/1Mbps/月</td>
</tr>
<tr>
<td>包年包月实例，固定带宽阶梯计费</td>
<td>2Mbps</td>
<td>46.0 元/2Mbps/月</td>
</tr>
<tr>
<td>包年包月实例，固定带宽阶梯计费</td>
<td>3Mbps</td>
<td>71.0 元/3Mbps/月</td>
</tr>
<tr>
<td>包年包月实例，固定带宽阶梯计费</td>
<td>4Mbps</td>
<td>96.0 元/4Mbps/月</td>
</tr>
<tr>
<td>包年包月实例，固定带宽阶梯计费</td>
<td>5Mbps</td>
<td>125.0 元/5Mbps/月</td>
</tr>
<tr>
<td>包年包月实例，固定带宽阶梯计费</td>
<td>6Mbps及以上, 每Mbps费用</td>
<td>80.0 元/Mbps/月</td>
</tr>
<tr>
<td>按量计费实例，固定带宽阶梯计费</td>
<td>1－5 Mbps</td>
<td>0.063 元/Mbps/小时</td>
</tr>
<tr>
<td>按量计费实例，固定带宽阶梯计费</td>
<td>6Mbps及以上, 每Mbps费用</td>
<td>0.248 元/Mbps/小时</td>
</tr>
<tr>
<td>按使用量线性计费</td>
<td>1GB</td>
<td>0.8 元/GB</td>
</tr>
</tbody>
</table>
三.阿里云服务器最新磁盘价格表
1.ESSD磁盘价格
<table>
<thead>
<tr>
<th>云盘规格</th>
<th>性能上限</th>
<th>容量范围</th>
<th>按量价格</th>
<th>包月价格</th>
</tr>
</thead>
<tbody>
<tr>
<td>PL1</td>
<td>Max IOPS=5万; Max Throughput=350MB</td>
<td>20GiB ~ 32768GiB</td>
<td>0.0021 元/1GB/小时</td>
<td>1.0 元/1GB/月</td>
</tr>
<tr>
<td>PL2</td>
<td>Max IOPS=10万; Max Throughput=750MB</td>
<td>461GiB ~ 32768GiB</td>
<td>0.0042 元/1GB/小时</td>
<td>2.0 元/1GB/月</td>
</tr>
<tr>
<td>PL3</td>
<td>Max IOPS=100万; Max Throughput=4000MB</td>
<td>1261GiB ~ 32768GiB</td>
<td>0.0084 元/1GB/小时</td>
<td>4.0 元/1GB/月</td>
</tr>
</tbody>
</table>
2.其它磁盘价格
<table>
<thead>
<tr>
<th>计费项</th>
<th>类型</th>
<th>规格</th>
<th>按量价格</th>
<th>包月价格</th>
</tr>
</thead>
<tbody>
<tr>
<td>系统盘（40 GB起售价）</td>
<td>普通云盘</td>
<td>40GB</td>
<td>0.017 元/40GB/小时</td>
<td>12.0 元/40GB/月</td>
</tr>
<tr>
<td>系统盘（40 GB起售价）</td>
<td>高效云盘</td>
<td>40GB</td>
<td>0.02 元/40GB/小时</td>
<td>14.0 元/40GB/月</td>
</tr>
<tr>
<td>系统盘（40 GB起售价）</td>
<td>SSD云盘</td>
<td>40GB</td>
<td>0.056 元/40GB/小时</td>
<td>40.0 元/40GB/月</td>
</tr>
<tr>
<td>系统盘（线性计费）</td>
<td>普通云盘</td>
<td>1GB</td>
<td>0.00042 元/1GB/小时</td>
<td>0.3 元/1GB/月</td>
</tr>
<tr>
<td>系统盘（线性计费）</td>
<td>高效云盘</td>
<td>1GB</td>
<td>0.00049 元/1GB/小时</td>
<td>0.35 元/1GB/月</td>
</tr>
<tr>
<td>系统盘（线性计费）</td>
<td>SSD云盘</td>
<td>1GB</td>
<td>0.0014 元/1GB/小时</td>
<td>1.0 元/1GB/月</td>
</tr>
<tr>
<td>数据盘（线性计费）</td>
<td>普通云盘</td>
<td>1GB</td>
<td>0.00042 元/1GB/小时</td>
<td>0.3 元/1GB/月</td>
</tr>
<tr>
<td>数据盘（线性计费）</td>
<td>高效云盘</td>
<td>1GB</td>
<td>0.00049 元/1GB/小时</td>
<td>0.35 元/1GB/月</td>
</tr>
<tr>
<td>数据盘（线性计费）</td>
<td>SSD云盘</td>
<td>1GB</td>
<td>0.0014 元/1GB/小时</td>
<td>1.0 元/1GB/月</td>
</tr>
</tbody>
</table>
以上为阿里云服务器最新版的价格表（实例+磁盘收费+宽带价格表）仅供参考，报价很全面，在我们做预算支出表的时候，一定要将实例、磁盘、带宽价格均算到成本里面，只算实例价格是不准确的哦。 |
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发表于 2024-10-5 22:45:45