The billing of general computing resources involves public and dedicated resource groups. This topic describes the billing of the resource groups of general computing resources.
Billing
The following figure shows how general computing resources are billed:
Billing methods
The following table describes how public and dedicated resource groups are billed.
Billable resource | Billable item | Billing rule | Billing method | How to stop billing |
Public resource group | The running duration of a Data Science Workshop (DSW) instance or a Deep Learning Containers (DLC) job, which is the duration for which the public resource group is occupied. | The public resource group is billed based on the amount of time during which public resources are occupied by the DSW instance or the DLC job. | Pay-as-you-go | Stop the DSW instance, stop the DLC training job or wait until the job is complete. |
Dedicated resource group | The running duration of computing resources in the dedicated resource group. | You are charged only for the computing resources in the dedicated resource group. You are not charged for DSW instances or DLC jobs that are deployed by using the dedicated resource group. | Subscription | N/A |
Public resource group
The public resource group supports only the pay-as-you-go billing method. The following table describes the billing details.
Billing method | Fee calculation | Unit price | Billing duration | Scaling description | Usage notes |
Pay-as-you-go |
| For more information, see Appendix: Pricing details of the public resource group. | Running duration of a DSW instance or a DLC job | N/A | None |
Dedicated resource group
The public resource group supports only the subscription billing method. The following table describes the billing details.
Billing method | Fee calculation | Unit price | Billing duration | Scaling description | Usage notes |
Subscription |
| For more information about pricing, go to the AI Computing Resource Group (International Site) page. |
| N/A | None |
Billing examples
The following billing examples are only for reference. Refer to the console or buy page of the service that you want to purchase for the actual fees.
Public resource group
Scenario
For example, you use an instance of the ecs.g6.2xlarge instance type that is deployed in the China (Shanghai) region to create a training job. The job runs for 1 minute and 15 seconds.
Fee calculation
Bill amount = 1 × 0.6/60 × 1.25 = 0.0125. Unit: USD.
Dedicated resource group
Scenario
For example, you purchase two instances of the ecs.g6.13xlarge-52c192g instance type in the China (Shanghai) region for 2 months. The subscription unit price is 980.57 USD /month. The price is used as an example.
Fee calculation
Bill amount = 2 × 980.57 × 2 = 3922.28. Unit: USD.
Overdue payments
Causes
The balance of your Alibaba Cloud account is insufficient.
Subscription: Your account balance is insufficient to pay for subscription fees.
Pay-as-you-go: Your account balance is less than the bill amount of the previous billing cycle. If the system fails to deduct fees, your account enters the overdue state.
Service suspension
In PAI, bills are generated within 4 hours after the previous billing cycle ends and displayed by using the T + 1 settlement method. If your account enters the overdue state, you are notified by text message.
After your account enters the overdue state, your PAI resources remain available for 24 hours. After 24 hours, your resources are stopped.
To ensure service continuity, we recommend that you top up your account in a timely manner.
View the overdue amount
Log on to the Billing Management console.
On the Account Overview page, view the outstanding amount due.
Renewal rules
Dedicated resource groups support the following renewal methods:
Auto-renewal
To enable auto-renewal, select Auto-renewal when you purchase subscription computing resources. For more information, see Create and manage general computing resources.
Manual renewal
On the dedicated resource group list, find the dedicated resource group that you want to renew and click Renew in the Actions column to renew the resources. For more information, see Create and manage general computing resources.
Refund policy
Pay-as-you-go fees cannot be refunded.
Subscription fees can be refunded based on the following rules:
Five-day money-back guarantee: You are eligible for a full refund for unused resources that are purchased within five days.
Partial refund: You are eligible for a refund for the remaining duration. The refund amount is calculated by using the following formula:
Refund amount = Amount paid - Amount consumed.
Refund for renewal orders: You can cancel renewal orders that have not taken effect.
Appendix: Pricing details of the public resource group
The following table provides the pricing details.
Instance type | Specification | GPU | Price (USD/hour) | Region |
ecs.g6.2xlarge | 8 vCPUs + 32 GB memory | None | 0.6 |
|
ecs.g6.4xlarge | 16 vCPUs + 64 GB memory | None | 0.6 |
|
1.2 |
| |||
ecs.g6.8xlarge | 32 vCPUs + 128 GB memory | None | 1.2 |
|
1.8 |
| |||
2.4 | China (Hong Kong) | |||
ecs.gn5-c28g1.7xlarge | 28 vCPUs + 112 GB memory | 1 * NVIDIA P100 | 3.6 |
|
3 | China (Hong Kong) | |||
ecs.gn5-c4g1.xlarge | 4 vCPUs + 30 GB memory | 1 * NVIDIA P100 | 1.8 |
|
ecs.gn5-c8g1.2xlarge | 8 vCPUs + 60 GB memory | 1 * NVIDIA P100 | 2.4 |
|
ecs.gn5-c8g1.4xlarge | 16 vCPUs + 120 GB memory | 2 * NVIDIA P100 | 4.8 |
|
4.2 | Germany (Frankfurt) | |||
ecs.gn6e-c12g1.12xlarge | 48 vCPUs + 368 GB memory | 4 * NVIDIA V100 | 17.4 | India (Mumbai) |
18 | Singapore | |||
18.6 |
| |||
12.6 |
| |||
15.6 | Germany (Frankfurt) | |||
ecs.gn6e-c12g1.3xlarge | 12 vCPUs + 92 GB memory | 1 * NVIDIA V100 | 4.2 |
|
4.8 |
| |||
3 |
| |||
3.6 | Germany (Frankfurt) | |||
ecs.gn6e-c12g1.24xlarge | 96 vCPUs + 736 GB memory | 8 * NVIDIA V100 | 35.4 | India (Mumbai) |
36 | Singapore | |||
37.8 |
| |||
25.8 |
| |||
31.8 | Germany (Frankfurt) | |||
ecs.gn6v-c8g1.2xlarge | 8 vCPUs + 32 GB memory | 1 * NVIDIA V100 | 4.2 |
|
4.8 | Singapore | |||
ecs.gn6v-c8g1.8xlarge | 32 vCPUs + 128 GB memory | 4 * NVIDIA V100 | 16.8 |
|
19.8 | Singapore | |||
ecs.gn6v-c8g1.16xlarge | 64 vCPUs + 256 GB memory | 8 * NVIDIA V100 | 34.2 |
|
39.6 | Singapore | |||
ecs.gn6v-c10g1.20xlarge | 82 vCPUs + 336 GB memory | 8 * NVIDIA V100 | 35.4 |
|
ecs.r7.16xlarge | 64 vCPUs + 512 GB memory | None | 4.8 |
|
1.8 |
| |||
5.4 | China (Hong Kong) | |||
ecs.r7.xlarge | 4 vCPUs + 32 GB memory | None | 0.6 | China (Hong Kong) |
ecs.r7.2xlarge | 8 vCPUs + 64 GB memory | None | 0.6 |
|
ecs.r7.4xlarge | 16 vCPUs + 128 GB memory | None | 0.6 |
|
1.2 |
| |||
ecs.r7.6xlarge | 24 vCPUs + 192 GB memory | None | 1.8 |
|
1.2 |
| |||
ecs.r7.8xlarge | 32 vCPUs + 256 GB memory | None | 2.4 |
|
1.8 |
| |||
ecs.g7.2xlarge | 8 vCPUs + 32 GB memory | None | 0.6 |
|
ecs.g7.3xlarge | 12 vCPUs + 48 GB memory | |||
ecs.g5.2xlarge | 8 vCPUs + 32 GB memory | None | 0.6 |
|
ecs.g6.3xlarge | 12 vCPUs + 48 GB memory | |||
ecs.g7.4xlarge | 16 vCPUs + 64 GB memory | None | 1.2 |
|
0.6 |
| |||
ecs.r7.3xlarge | 12 vCPUs + 96 GB memory | None | 0.6 |
|
1.2 | China (Hong Kong) | |||
ecs.c6e.8xlarge | 32 vCPUs + 64 GB memory | None | 1.8 |
|
1.2 |
| |||
ecs.g6.6xlarge | 24 vCPUs + 96 GB memory | None | 1.2 |
|
1.8 | China (Hong Kong) | |||
ecs.g7.6xlarge | 24 vCPUs + 96 GB memory | None | 1.2 |
|
ecs.g5.4xlarge | 16 vCPUs + 64 GB memory | None | 0.6 | India (Mumbai) |
1.2 |
| |||
ecs.hfc6.8xlarge | 32 vCPUs + 64 GB memory | None | 1.8 |
|
1.2 |
| |||
ecs.g7.8xlarge | 32 vCPUs + 128 GB memory | None | 1.8 |
|
1.2 |
| |||
2.4 | China (Hong Kong) | |||
ecs.hfc6.10xlarge | 40 vCPUs + 96 GB memory | None | 2.4 |
|
1.2 |
| |||
ecs.g6.13xlarge | 52 vCPUs + 192 GB memory | None | 3 |
|
3.6 |
| |||
2.4 |
| |||
ecs.g5.8xlarge | 32 vCPUs + 128 GB memory | None | 1.8 |
|
2.4 |
| |||
ecs.hfc6.16xlarge | 64 vCPUs + 128 GB memory | None | 3.6 |
|
2.4 |
| |||
ecs.g7.16xlarge | 64 vCPUs + 256 GB memory | None | 3.6 |
|
2.4 |
| |||
4.2 | China (Hong Kong) | |||
ecs.hfc6.20xlarge | 80 vCPUs + 192 GB memory | None | 4.8 |
|
4.2 |
| |||
3 |
| |||
ecs.g6.26xlarge | 104 vCPUs + 384 GB memory | None | 5.4 | India (Mumbai) |
6.6 |
| |||
4.2 |
| |||
7.2 | China (Hong Kong) | |||
ecs.g5.16xlarge | 64 vCPUs + 256 GB memory | None | 3 | India (Mumbai) |
4.2 |
| |||
3.6 |
| |||
4.8 |
| |||
ecs.gn6i-c4g1.xlarge | 4 vCPUs + 15 GB memory | 1 * NVIDIA T4 | 1.2 |
|
1.8 |
| |||
ecs.gn6i-c8g1.2xlarge | 8 vCPUs + 31 GB memory | 1 * NVIDIA T4 | 1.8 |
|
1.2 |
| |||
2.4 |
| |||
ecs.gn6i-c16g1.4xlarge | 16 vCPUs + 62 GB memory | 1 * NVIDIA T4 | 2.4 |
|
1.8 |
| |||
ecs.gn7-c12g1.3xlarge | 12 vCPUs + 95 GB memory | 1 * GU50 | 3.6 | Malaysia (Kuala Lumpur) |
5.4 | China (Beijing) | |||
2.4 |
| |||
ecs.gn6i-c24g1.6xlarge | 24 vCPUs + 93 GB memory | 1 * NVIDIA T4 | 2.4 |
|
3 |
| |||
ecs.gn7i-c32g1.8xlarge | 32 vCPUs + 188 GB memory | 1 * NVIDIA A10 | 6.6 | India (Mumbai) |
3.6 |
| |||
3 | Indonesia (Jakarta) | |||
4.8 |
| |||
12.6 | China (Hong Kong) | |||
2.4 | China (Shenzhen) | |||
7.2 | Germany (Frankfurt) | |||
ecs.gn7e-c16g1.4xlarge | 16 vCPUs + 125 GB memory | 1 * GU100 | 6.6 | Indonesia (Jakarta) |
4.8 | China (Beijing) | |||
9.6 |
| |||
2.4 | China (Hong Kong) | |||
ecs.gn7i-c16g1.4xlarge | 16 vCPUs + 60 GB memory | 1 * NVIDIA A10 | 3 |
|
7.2 | Malaysia (Kuala Lumpur) | |||
6 | China (Beijing) | |||
40.8 |
| |||
2.4 | China (Shenzhen) | |||
ecs.gn6i-c24g1.12xlarge | 48 vCPUs + 186 GB memory | 2 * NVIDIA T4 | 4.8 |
|
5.4 |
| |||
ecs.gn7i-c32g1.16xlarge | 64 vCPUs + 376 GB memory | 2 * NVIDIA A10 | 13.2 | India (Mumbai) |
6.6 | Singapore | |||
3 |
| |||
6 |
| |||
14.4 | Germany (Frankfurt) | |||
ecs.gn7-c13g1.13xlarge | 52 vCPUs + 380 GB memory | 4 * NVIDIA A10 | 3 |
|
12.6 | China (Beijing) | |||
ecs.gn7i-c32g1.32xlarge | 128 vCPUs + 752 GB memory | 4 * NVIDIA A10 | 3 |
|
13.8 | Singapore | |||
2.4 |
| |||
3.6 |
| |||
ecs.gn6i-c24g1.24xlarge | 96 vCPUs + 372 GB memory | 4 * NVIDIA T4 | 10.2 |
|
10.8 |
| |||
9.6 | China (Hong Kong) | |||
ecs.gn6v-c10g1.20xlarge | 82 vCPUs + 336 GB memory | 8 * NVIDIA V100 | 35.4 |
|
ecs.gn7-c13g1.26xlarge | 104 vCPUs + 760 GB memory | 8 * GU50 | 3 |
|
3.6 |
| |||
ecs.ebmgn7e.32xlarge | 128 vCPUs + 1,024 GB memory | 8 * GU100 | 45 |
|
50.4 | Germany (Frankfurt) |