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Platform For AI:Billing of EAS

Last Updated:Jan 08, 2026

This document describes the billable items, billing methods, and pricing of Elastic Algorithm Service (EAS).

Billing overview

When you use EAS to deploy a service, charges may be incurred for compute resources, system disks, and dedicated gateways.

  • Computing resources: Includes public resources, dedicated resources, and Lingjun AI computing resources.

  • (Optional) System disks: EAS provides a free quota of 30 GiB for public resources and 200 GiB for dedicated resources. You are charged separately for any additional system disk usage.

  • (Optional) Dedicated gateways: By default, services use a shared gateway, which is free. If you require features like security isolation, access control, or custom domain names, you can purchase a dedicated gateway. You must manually configure your service to use a dedicated gateway.

EAS provides two billing methods:

  • Pay-as-you-go: You are charged based on the runtime of your service, not the number of service calls. This method is ideal for scenarios with uncertain or fluctuating demand.

  • Subscription: You pay upfront for long-term usage at a discounted rate. This method is suitable for stable, long-running workloads.

For SDWebUI and ComfyUI, EAS offers a Serverless Edition. Service deployment is free, and you are charged only for the actual inference duration during service calls.

Important

If you use other Alibaba Cloud services such as Elastic IP Address (EIP), Object Storage Service (OSS), or Apsara File Storage NAS, you will incur charges associated with those services.

image

Billing period

Pay-as-you-go

For pay-as-you-go resources, understand when billing starts and stops to avoid unexpected charges. Stop idle model services promptly to avoid unnecessary costs.

Resource

Start time of billing

End time of billing

Public resources

Billing starts when the service enters the Running state and continues even if no calls are made to the service.

When the model service is stopped (the service resources are released).

Important

You must stop or delete the service instance.

Dedicated resources

Billing starts when the machine enters the Running state and continues even if no services are deployed on it.

  • When the machine is deleted.

  • When the machine enters the Stopped state due to an overdue payment.

Important

The Stopped state can only be triggered by an overdue payment. You cannot manually stop a machine to pause billing. To stop charges, you must delete the pay-as-you-go machine.

System disk

When the system disk is purchased.

  • When the machine in the dedicated resource group is deleted.

  • When the service deployed using public resources is deleted.

Dedicated gateway

Billing starts when the dedicated gateway is created and continues even if it is not used.

When the dedicated gateway is deleted.

Service scaling

  • Scale-out: Billing for new resources starts when they are added.

  • Scale-in: Billing stops for resources when they are released. Billing continues for the remaining resources.

Subscription

Resource

Start time of billing

End time of billing

Dedicated resources

The next day after the purchase, at 00:00:00.

The expiration time.

AI computing resources (Lingjun resources)

The next day after the purchase, at 00:00:00.

The expiration time.

System disk

The time when you purchase the system disk.

The expiration time.

Dedicated gateway

The time when you purchase the dedicated gateway.

The expiration time.

Service inference (Serverless Edition)

  • Serverless Edition service deployment is free. You are charged only for the inference duration when the service is invoked.

  • For example, if you click Generate on the WebUI page and it takes 10 seconds to generate an image, you are billed for 10 seconds.

  • Currently, only SDWebUI and ComfyUI support deployment in the Serverless Edition. You can view pricing details on the Deploy Service page.

Notes

All prices in this document are for reference only. Your official bill reflects your actual costs.

Billable resources

The following table provides details about the billing methods for resources.

Billable item

Billing method

Fee calculation

Unit price

Usage notes

Public resources

Pay-as-you-go

(specific resources required by the instance)

Billable amount per model service = Number of instances × (Number of CPU cores × (Unit price / 60) + Memory size × (Unit price / 60)) × Duration (in minutes)

Fees are calculated per minute on a pay-as-you-go basis and rounded down to the minute. For more information about the hourly prices of vCPU and memory resources, see Resource-based configuration. To calculate the unit prices per minute, divide the prices listed in Table 1 by 60.

  • Billing duration is calculated in minutes.

  • We recommend stopping idle model services to avoid unnecessary charges.

Pay-as-you-go (specific instance type)

Bill amount for each model service = Number of instances × (Unit price/60) × Usage duration (minutes)

Pricing varies by region and instance type. The price on the console prevails.

For information about the instance types supported by the public resource group, see Appendix: Public resource group instance types.

  • Billing duration is calculated in minutes.

  • We recommend stopping idle model services to avoid unnecessary charges.

  • Some instance types may be temporarily unavailable in certain regions.

Dedicated resources

Pay-as-you-go

Bill amount for each resource group = Number of instances × (Unit price/60) × Usage duration (minutes)

For pay-as-you-go pricing, go to the EAS post-payment for dedicated machine buy page.

  • Billing duration is calculated in minutes.

  • Some machine resources may be temporarily unavailable in certain regions.

Subscription

Bill amount for each resource group = Number of instances × Unit price × Subscription duration (months)

For information about the unit prices of subscription instances, go to the EAS pre-payment for dedicated machine buy page.

Some machine resources may be temporarily unavailable in certain regions.

AI computing resources (Lingjun resources)

Subscription

You can prepay for Lingjun AI computing resources and use the corresponding resource quota to deploy EAS services. For more information, see Billing of AI computing resources.

System disk

Pay-as-you-go

Bill amount = Number of instances × System disk capacity (GiB) × (Unit price/60) × Usage duration (minutes)

To view the unit prices of system disks, go to the product overview page of Elastic Compute Service (ECS), click the Pricing tab, and then click Storage. The pricing information of Enterprise SSD (ESSD) PL1 is displayed in the System Cloud Disk Fee section.

N/A.

Subscription

Bill amount = Number of instances × System disk capacity (GiB) × Unit price × Subscription duration (months)

To view the unit prices of system disks, go to the product overview page of ECS, click the Pricing tab, and then click Storage. The pricing information of ESSD PL1 is displayed in the System Cloud Disk Fee section.

N/A.

Dedicated gateway

Pay-as-you-go

Bill amount = (Unit price/60) × Number of gateway nodes × Usage duration (minutes)

To view the unit prices of dedicated gateways, go to the EAS Dedicated Gateway Postpay buy page.

N/A.

Subscription

Bill amount = Unit price × Number of gateway nodes × Usage duration (months)

To view the unit prices of subscription dedicated gateways, go to the EAS dedicated gateway prepay buy page.

N/A.

Service inference (Serverless Edition)

Pay by actual inference duration when service is called

Bill amount = Service inference duration (seconds) × Unit price (USD/second)

Only Stable Diffusion web UI and ComfyUI support the deployment of Serverless Edition services. You can view the pricing details on the deployment page.

N/A.

Table 1 Resource-based configuration

Important

The pricing information below is for reference only. Your actual costs are based on the price displayed on the console or purchase page of the cloud service.

Resource

Pricing

CPU

USD 0.03 per vCPU-hour

Memory

USD 0.004 per GB-hour

Billing examples

Important

The following examples are for reference only. Your actual costs are based on the prices shown on the console or the product purchase page of the cloud service.

Public resource group billing example

Pay-as-you-go example

  • Scenario description:

    You deploy a model service using the Specified resources option in a Public Resource Group in the China (Hangzhou) region.

    • At 09:00:00 on June 3, 2019, the service enters the running state, initially using 2 CPU cores and 8 GiB of memory.

    • At 10:00:00 on June 3, 2019, the service is scaled in to 1 CPU core and 4 GiB of memory.

    • At 11:00:00 on June 3, 2019, the service is scaled out to 4 CPU cores and 16 GiB of memory.

    • At 12:00:00 on June 3, 2019, the service is stopped.

  • Cost calculation:

    Bill amount = 2 × 0.03 + 8 × 0.004 + 1 × 0.03 + 4 × 0.004 + 4 × 0.03 + 16 × 0.004 = USD 0.322

Dedicated resource group billing example

Subscription example

  • Scenario description:

    You purchase two 4-core CPU, 15 GiB GPU (T4) machines in the China (Hangzhou) region with a three-month subscription. The price is 570 USD/month. The actual price is shown on the product purchase page.

  • Cost calculation:

    Bill amount = 2 × 570 × 3 = USD 3,420

Pay-as-you-go example

  • Scenario description:

    You purchase two ecs.g6.6xlarge (24-core CPU, 96 GiB) machines in the China (Hangzhou) region using the pay-as-you-go method. You use them for 45 minutes. The price is 1.02 USD/hour. The actual price is shown on the product purchase page.

  • Cost calculation:

    Bill amount = 2 × (1.02/60) × 45 = USD 1.53

System disk billing example

Subscription example

  • Scenario description:

    You purchase two dedicated machines in the China (Hangzhou) region with a three-month subscription. Each machine is configured with a 300 GiB system disk.

  • Cost calculation:

    Bill amount = 2 × (300 - 200) × 0.153 × 3 = USD 91.8

Pay-as-you-go example

  • Dedicated resource group

    • Scenario description:

      You purchase two dedicated machines in the China (Hangzhou) region using the pay-as-you-go method. Each machine is configured with a 300 GiB system disk and is used for 5 hours.

    • Cost calculation:

      Bill amount = 2 × (300 - 200) × 0.000319 × 5 = USD 0.319
  • Public resource group

    • Scenario description:

      You purchase two instances in a Public Resource Group in the China (Hangzhou) region using the pay-as-you-go method. Each instance is configured with a 300 GiB system disk and is used for 5 hours.

    • Cost calculation:

      Bill amount = 2 × (300 - 30) × 0.000319 × 5 = USD 0.8613

Appendix: Public resource group instance types

The following table lists some of the public resource instance types that EAS supports. For a complete list, navigate to the Resource Deployment Information section on the Deploy Service page for Custom Deployment. For more information, see Custom deployment. Supported instance types vary by region. The information on the console prevails.

CPU type

Instance type

vCPU

Memory (GB)

ecs.c7.large

2

4

ecs.c7.xlarge

4

8

ecs.c7.2xlarge

8

16

ecs.c7.4xlarge

16

32

ecs.c7.6xlarge

24

48

ecs.c7.8xlarge

32

64

ecs.c7.16xlarge

64

128

ecs.r7.4xlarge

16

128

ecs.r7.large

2

16

ecs.r7.xlarge

4

32

ecs.r7.2xlarge

8

64

ecs.r7.6xlarge

24

192

ecs.r7.8xlarge

32

256

ecs.r7.16xlarge

64

512

ecs.g7.large

2

8

ecs.g7.xlarge

4

16

ecs.g7.2xlarge

8

32

ecs.g7.4xlarge

16

64

ecs.g7.6xlarge

24

96

ecs.g7.8xlarge

32

128

ecs.g7.16xlarge

64

256

ecs.g6.large

2

8

ecs.g6.xlarge

4

16

ecs.g6.2xlarge

8

32

ecs.g6.4xlarge

16

64

ecs.g6.6xlarge

24

96

ecs.g6.8xlarge

32

128

ecs.c6.large

2

4

ecs.c6.xlarge

4

8

ecs.c6.2xlarge

8

16

ecs.c6.4xlarge

16

32

ecs.c6.6xlarge

24

48

ecs.c6.8xlarge

32

64

ecs.r6.large

2

16

ecs.r6.xlarge

4

32

ecs.r6.2xlarge

8

64

ecs.r6.4xlarge

16

128

ecs.r6.6xlarge

24

192

ecs.r6.8xlarge

32

256

ecs.g5.6xlarge

24

96

ecs.c5.6xlarge

24

48

ecs.g8y.large

2

8

ecs.g8y.xlarge

4

16

ecs.g8y.2xlarge

8

32

ecs.g8y.4xlarge

16

64

ecs.g8y.8xlarge

32

128

ecs.g8y.16xlarge

64

256

ecs.c7a.large

2

4

ecs.c7a.xlarge

4

8

ecs.c7a.2xlarge

8

16

ecs.c7a.4xlarge

16

32

ecs.c7a.8xlarge

32

64

ecs.c7a.16xlarge

64

128

ecs.g7a.large

2

8

ecs.g7a.xlarge

4

16

ecs.g7a.2xlarge

8

32

ecs.g7a.4xlarge

16

64

ecs.g7a.8xlarge

32

128

ecs.g7a.16xlarge

64

256

GPU type

Instance type

vCPU

Memory (GB)

GPU memory

ml.gu7i.c8m30.1-gu30

8

30

1 × 24 GB

ml.gu7i.c16m60.1-gu30

16

60

1 × 24 GB

ml.gu7i.c32m188.1-gu30

32

188

1 × 24 GB

ml.gu7i.c64m376.2-gu30

64

376

2 × 24 GB

ml.gu7i.c128m752.4-gu30

80

256

4 × 24 GB

ecs.gn5i-c4g1.xlarge

4

16

1 × 8 GB

ecs.gn5i-c8g1.2xlarge

8

32

1 × 8 GB

ecs.gn5-c4g1.xlarge

4

30

1 × 16 GB

ecs.gn5-c8g1.2xlarge

8

60

1 × 16 GB

ecs.gn5-c8g1.4xlarge

16

120

2 × 16 GB

ecs.gn5-c28g1.7xlarge

28

112

1 × 16 GB

ecs.vgn6i-m4-vws.xlarge

4

23

1 × 4 GB

ecs.vgn6i-m8-vws.2xlarge

10

46

1 × 8 GB

ecs.gn6i-c4g1.xlarge

4

15

1 × 16 GB

ecs.gn6i-c8g1.2xlarge

8

31

1 × 16 GB

ecs.gn6i-c16g1.4xlarge

16

62

1 × 16 GB

ecs.gn6i-c24g1.6xlarge

24

93

1 × 16 GB

ecs.gn6i-c24g1.12xlarge

48

186

2 × 16 GB

ecs.gn6i-c24g1.24xlarge

96

372

4 × 16 GB

ecs.gn7i-c8g1.2xlarge

8

30

1 × 24 GB

ecs.gn7i-c16g1.4xlarge

16

60

1 × 24 GB

ecs.gn7i-c32g1.8xlarge

32

188

1 × 24 GB

ecs.gn7i-c32g1.16xlarge

64

376

2 × 24 GB

ecs.gn7i-c32g1.32xlarge

128

752

4 × 24 GB

ecs.gn6v-c8g1.2xlarge

8

32

1 × 16 GB

ecs.gn6v-c8g1.4xlarge

16

64

2 × 16 GB

ecs.gn6v-c8g1.8xlarge

32

128

4 × 16 GB

ecs.gn6e-c12g1.3xlarge

12

92

1 × 32 GB

ecs.gn6e-c12g1.12xlarge

48

368

4 × 32 GB

ecs.gn6e-c12g1.24xlarge

96

736

8 × 32 GB

ecs.gn7-c12g1.3xlarge

12

94

1 × 40 GB

ecs.gn7-c13g1.13xlarge

52

378

4 × 40 GB

ecs.gn7-c13g1.26xlarge

104

756

8 × 40 GB

ecs.gn7-c13g1.6xlarge

26

189

2 × 40 GB

ecs.gn7e-c16g1.4xlarge

16

125

1 × 80 GB

ecs.gn7e-c16g1.8xlarge

32

250

2 × 80 GB

ecs.gn7e-c16g1.16xlarge

64

500

4 × 80 GB

ecs.gn7e-c16g1.32xlarge

128

1000

8 × 80 GB