Billing of EAS - Billing| Alibaba Cloud Documentation Center

This topic describes how Elastic Algorithm Service (EAS) of Machine Learning Platform for AI (PAI) is billed.

You are charged EAS fees for the resources that are used to deploy model services.

You can deploy model services in shared resource groups and dedicated resource groups. Bills are generated based on the used resources of resource groups. For more information about the differences between the two types of resource groups, see Dedicated resource groups.
If you deploy a model service in a shared resource group and a dedicated resource group, you are charged for using the resources of both resource groups.

The following table describes the billing rules for shared resource groups and dedicated resource groups.


Resource	Billable item	Billing method	Billing rule	Method to stop billing
Shared resource group	The amount of time for which a model service has been running. The value equals the amount of time for which the model services have occupied the shared resources.	Pay-as-you-go	Bills are generated based on the amount of time for which the model services have occupied shared resources. The billing starts immediately after model services are created.	Stop model services
Dedicated resource group	The amount of time for which the resource groups have been running.	Pay-as-you-go	Only dedicated resource groups are billed. Model services deployed in dedicated resource groups are not charged. The billing starts immediately after pay-as-you-go dedicated resource groups are created.	Stop dedicated resource groups
Dedicated resource group		Subscription		N/A

Billing rules for shared resource groups

You can use the default instance type or specify the instance type as needed if you use shared resource groups. Billing rules vary based on the instance type that you use.


Item	Default instance type	Specified instance type
Billing formula	`Bill amount of each model service = Number of instances × (Number of vCPUs × Unit price of vCPU + Memory size × Unit price of memory) × Usage duration` Note Usage duration is measured in minutes.	`Bill amount of each model service = Number of instances × Unit price × Usage duration` Note Usage duration is measured in minutes.
Unit price	For more information about the prices, see Prices of shared resource groups with the default instance type.	For more information about the prices, see Prices of shared resource groups with the specified instance type.
Usage duration	Bills are generated from the point in time when a model service starts to run and consume resources. The system stops billing immediately after a model service is stopped and releases resources.
Scaling	If you scale out a model service, newly added resources are billed after the scale-out activity is complete. If you scale in a model service, released resources are no longer billed, and only the remaining resources are billed.
Notes	Fees are charged on a per-minute basis. No fee is charged if a resource group is occupied for less than one minute. To prevent unnecessary costs, we recommend that you stop unwanted model services.	Fees are charged on a per-minute basis. No fee is charged if a resource group is occupied for less than one minute. To prevent unnecessary costs, we recommend that you stop unwanted model services. Some resources may be unavailable in one or more regions for a short period of time. During the time period, you cannot purchase relevant resource groups in these regions.

Prices of shared resource groups with the default instance type

If you use pay-as-you-go shared resource groups with the default instance type, fees are charged on a per-minute basis. The following table describes the unit prices per hour. You can calculate the unit prices per minute by dividing the prices listed in the table by 60.


Resource type	Unit price	Region
CPU	USD 0.03 per vCPU-hour	China (Hangzhou) China (Shanghai) China (Shenzhen) China (Beijing) China (Hong Kong) Singapore (Singapore) Indonesia (Jakarta) India (Mumbai) Germany (Frankfurt) US (Virginia)
Memory	USD 0.004 per GB-hour

Prices of shared resource groups with the specified instance type

The pay-as-you-go billing method is used for the shared resource groups with the specified instance type. The prices vary with instance types and regions. The following table describes the unit prices per hour. You can calculate the unit prices per minute by dividing the prices listed in the table by 60.


Instance type	Instance family	Number of vCPUs	Memory (GiB)	Unit price (USD per hour)	Region
ecs.c5.6xlarge	c5, compute-optimized instance family	24	48	1.08	India (Mumbai) US (Virginia)
				1.26	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) Singapore (Singapore)
				1.2	Indonesia (Jakarta) China (Hong Kong)
				0.96	China (Zhangjiakou)
				1.14	Germany (Frankfurt)
ecs.c6.2xlarge	c6, compute-optimized instance family	8	16	0.36	India (Mumbai) US (Virginia) US (Silicon Valley)
				0.42	Indonesia (Jakarta) Singapore (Singapore) Germany (Frankfurt)
				0.24	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.48	China (Hong Kong)
				0.18	China (Zhangjiakou)
ecs.c6.4xlarge	c6, compute-optimized instance family	16	32	0.72	India (Mumbai) US (Virginia) US (Silicon Valley)
				0.84	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				0.54	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.9	China (Hong Kong)
				0.36	China (Zhangjiakou)
ecs.c6.6xlarge	c6, compute-optimized instance family	24	48	1.08	India (Mumbai) US (Virginia) US (Silicon Valley)
				1.26	Singapore (Singapore) Indonesia (Jakarta)
				0.78	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.38	China (Hong Kong)
				0.54	China (Zhangjiakou)
				1.2	Germany (Frankfurt)
ecs.c6.8xlarge	c6, compute-optimized instance family	32	64	1.44	India (Mumbai) US (Virginia) US (Silicon Valley)
				1.68	Singapore (Singapore) Indonesia (Jakarta)
				1.08	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.8	China (Hong Kong)
				0.72	China (Zhangjiakou)
				1.62	Germany (Frankfurt)
ecs.g5.6xlarge	g5, general-purpose instance family	24	96	1.8	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.38	China (Zhangjiakou) US (Virginia)
				1.68	China (Hong Kong)
				1.32	India (Mumbai)
				1.74	Singapore (Singapore)
				1.62	Indonesia (Jakarta)
				1.56	Germany (Frankfurt)
ecs.g6.2xlarge	g6, general-purpose instance family	8	32	0.48	India (Mumbai)
				0.54	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				0.36	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.6	China (Hong Kong)
				0.24	China (Zhangjiakou)
				0.42	US (Virginia) US (Silicon Valley)
ecs.g6.4xlarge	g6, general-purpose instance family	16	64	0.9	India (Mumbai) US (Virginia) US (Silicon Valley)
				1.08	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				0.66	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.2	China (Hong Kong)
				0.48	China (Zhangjiakou)
ecs.g6.6xlarge	g6, general-purpose instance family	24	96	1.38	India (Mumbai)
				1.68	Singapore (Singapore) Indonesia (Jakarta)
				1.02	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.8	China (Hong Kong)
				0.72	China (Zhangjiakou)
				1.62	Germany (Frankfurt)
				1.32	US (Virginia) US (Silicon Valley)
ecs.g6.8xlarge	g6, general-purpose instance family	32	128	1.86	India (Mumbai)
				2.22	Singapore (Singapore) Indonesia (Jakarta)
				1.38	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.4	China (Hong Kong)
				0.96	China (Zhangjiakou)
				1.8	US (Virginia) US (Silicon Valley)
ecs.gn5-c28g1.7xlarge	gn5, GPU-accelerated compute-optimized instance family (NVIDIA P100 × 1)	28	112	3.78	India (Mumbai)
				3.72	Singapore (Singapore) Indonesia (Jakarta)
				4.08	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				3.54	China (Hong Kong)
				3.84	Germany (Frankfurt)
				3.48	US (Virginia) US (Silicon Valley)
ecs.gn5-c4g1.xlarge	gn5, GPU-accelerated compute-optimized instance family (NVIDIA P100 × 1)	4	30	2.04	India (Mumbai) China (Hong Kong) US (Silicon Valley)
				2.16	Singapore (Singapore) Indonesia (Jakarta) China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.98	China (Zhangjiakou)
				1.92	Germany (Frankfurt)
				1.86	US (Virginia)
ecs.gn5-c8g1.2xlarge	gn5, GPU-accelerated compute-optimized instance family (NVIDIA P100 × 1)	8	60	2.46	India (Mumbai) China (Hong Kong) US (Silicon Valley)
				2.58	Singapore (Singapore) Indonesia (Jakarta)
				2.64	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.34	China (Zhangjiakou)
				2.28	Germany (Frankfurt)
				2.22	US (Virginia)
ecs.gn5-c8g1.4xlarge	gn5, GPU-accelerated compute-optimized instance family (NVIDIA P100 × 2)	16	120	4.92	China (Hong Kong) India (Mumbai)
				5.16	Singapore (Singapore) Indonesia (Jakarta)
				5.22	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				4.74	China (Zhangjiakou)
				4.62	Germany (Frankfurt)
				4.44	US (Virginia)
				4.86	US (Silicon Valley)
ecs.gn5i-c4g1.xlarge	gn5i, GPU-accelerated compute-optimized instance family (NVIDIA P4 × 1)	4	16	1.68	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.gn5i-c4g1.xlarge		4	16	1.26	China (Zhangjiakou)
ecs.gn5i-c8g1.2xlarge	gn5i, GPU-accelerated compute-optimized instance family (NVIDIA P4 × 1)	8	32	1.98	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.gn5i-c8g1.2xlarge		8	32	1.5	China (Zhangjiakou)
ecs.gn6i-c16g1.4xlarge	gn6i, GPU-accelerated compute-optimized instance family (NVIDIA T4 × 1)	16	62	2.28	India (Mumbai) Germany (Frankfurt)
				2.34	Singapore (Singapore)
				2.22	Indonesia (Jakarta)
				2.82	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.1	China (Hong Kong)
				2.52	China (Zhangjiakou)
				1.98	US (Virginia)
ecs.gn6i-c24g1.12xlarge	gn6i, GPU-accelerated compute-optimized instance family (NVIDIA T4 × 2)	48	186	5.46	India (Mumbai)
				5.94	Singapore (Singapore)
				5.52	Indonesia (Jakarta)
				5.88	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				5.22	China (Hong Kong)
				5.28	China (Zhangjiakou)
				5.7	Germany (Frankfurt)
				5.04	US (Virginia)
ecs.gn6i-c24g1.6xlarge	gn6i, GPU-accelerated compute-optimized instance family (NVIDIA T4 × 1)	24	93	2.7	India (Mumbai)
				2.76	Indonesia (Jakarta)
				2.94	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) Singapore (Singapore)
				2.64	China (Hong Kong) China (Zhangjiakou)
				2.88	Germany (Frankfurt)
				2.52	US (Virginia)
ecs.gn6i-c4g1.xlarge	gn6i, GPU-accelerated compute-optimized instance family (NVIDIA T4 × 1)	4	15	1.5	India (Mumbai) Singapore (Singapore)
				1.44	Indonesia (Jakarta)
				1.98	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.32	China (Hong Kong) US (Virginia)
				1.8	China (Zhangjiakou)
				1.38	Germany (Frankfurt)
ecs.gn6i-c8g1.2xlarge	gn6i, GPU-accelerated compute-optimized instance family (NVIDIA T4 × 1)	8	31	1.74	India (Mumbai)
				1.8	Singapore (Singapore)
				1.68	Indonesia (Jakarta) Germany (Frankfurt)
				2.4	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.56	China (Hong Kong) US (Virginia)
				2.16	China (Zhangjiakou)
ecs.gn6v-c8g1.2xlarge	gn6v, GPU-accelerated compute-optimized instance family (NVIDIA V100 × 1)	8	32	5.22	Singapore (Singapore)
				4.5	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				3.36	China (Zhangjiakou)
				3.12	US (Virginia)
ecs.r6.2xlarge	r6, memory-optimized instance family	8	64	0.6	India (Mumbai) US (Virginia) US (Silicon Valley)
				0.72	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				0.48	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.78	China (Hong Kong)
				0.3	China (Zhangjiakou)
ecs.r6.4xlarge	r6, memory-optimized instance family	16	128	1.2	India (Mumbai)
				1.38	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				0.9	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.56	China (Hong Kong)
				0.6	China (Zhangjiakou)
				1.14	US (Virginia) US (Silicon Valley)
ecs.r6.6xlarge	r6, memory-optimized instance family	24	192	1.8	India (Mumbai)
				1.38	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.1	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				2.34	China (Hong Kong)
				0.96	China (Zhangjiakou)
				1.74	US (Virginia) US (Silicon Valley)
ecs.r6.8xlarge	r6, memory-optimized instance family	32	256	2.4	India (Mumbai)
				2.76	Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt)
				1.8	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				3.06	China (Hong Kong)
				1.26	China (Zhangjiakou)
				2.34	US (Virginia) US (Silicon Valley)
ecs.g7.2xlarge	g7, general-purpose instance family	8	32	0.54	Singapore (Singapore) Indonesia (Jakarta) China (Hong Kong)
ecs.g7.2xlarge	g7, general-purpose instance family	8	32	0.42	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.g7.4xlarge	g7, general-purpose instance family	16	64	1.02	Singapore (Singapore) Indonesia (Jakarta)
				0.78	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.14	China (Hong Kong)
ecs.g7.6xlarge	g7, general-purpose instance family	24	96	1.56	Singapore (Singapore) Indonesia (Jakarta)
				1.2	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.68	China (Hong Kong)
ecs.g7.8xlarge	g7, general-purpose instance family	32	128	2.04	Singapore (Singapore) Indonesia (Jakarta)
				1.56	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.28	China (Hong Kong)
ecs.c7.2xlarge	c7, compute-optimized instance family	8	16	0.42	Singapore (Singapore) Indonesia (Jakarta)
				0.3	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.48	China (Hong Kong)
ecs.c7.4xlarge	c7, compute-optimized instance family	16	32	0.84	Singapore (Singapore) Indonesia (Jakarta)
				0.6	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.9	China (Hong Kong)
ecs.c7.6xlarge	c7, compute-optimized instance family	24	48	1.26	Singapore (Singapore) Indonesia (Jakarta)
				0.9	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.38	China (Hong Kong)
ecs.c7.8xlarge	c7, compute-optimized instance family	32	64	1.68	Singapore (Singapore) Indonesia (Jakarta)
				1.2	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.86	China (Hong Kong)
ecs.r7.2xlarge	r7, memory-optimized instance family	8	64	0.66	Singapore (Singapore)
				0.54	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.72	China (Hong Kong)
ecs.r7.4xlarge	r7, memory-optimized instance family	16	128	1.32	Singapore (Singapore)
				1.02	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				1.44	China (Hong Kong)
ecs.r7.6xlarge	r7, memory-optimized instance family	24	192	1.98	Singapore (Singapore)
				1.56	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.16	China (Hong Kong)
ecs.r7.8xlarge	r7, memory-optimized instance family	32	256	2.58	Singapore (Singapore)
				2.04	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				2.88	China (Hong Kong)
ecs.gn7-c12g1.3xlarge	gn7, GPU-accelerated compute-optimized instance family (NVIDIA A100 × 1)	12	95	5.4	China (Beijing) China (Shanghai)
ecs.g7.16xlarge	g7, general-purpose instance family	64	256	4.08	Singapore (Singapore) Indonesia (Jakarta)
				3.12	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				4.5	China (Hong Kong)
ecs.c7.16xlarge	c7, compute-optimized instance family	64	128	3.36	Singapore (Singapore) Indonesia (Jakarta)
				2.46	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				3.66	China (Hong Kong)
ecs.r7.16xlarge	r7, memory-optimized instance family	64	512	5.22	Singapore (Singapore)
				4.08	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				5.7	China (Hong Kong)
ecs.gn7i-c8g1.2xlarge	gn7i, GPU-accelerated compute-optimized instance family (NVIDIA A10 × 1)	8	30	3.06	Singapore (Singapore)
ecs.gn7i-c8g1.2xlarge		8	30	2.16	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.gn7i-c16g1.4xlarge	gn7i, GPU-accelerated compute-optimized instance family (NVIDIA A10 × 1)	16	60	3.24	Singapore (Singapore)
ecs.gn7i-c16g1.4xlarge		16	60	2.28	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.gn7i-c32g1.8xlarge	gn7i, GPU-accelerated compute-optimized instance family (NVIDIA A10 × 1)	32	188	3.6	Singapore (Singapore)
ecs.gn7i-c32g1.8xlarge		32	188	2.52	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.gn6e-c12g1.3xlarge	gn6e, GPU-accelerated compute-optimized instance family (NVIDIA V100 × 1)	12	92	4.68	Singapore (Singapore)
ecs.gn6e-c12g1.3xlarge		12	92	3.36	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
ecs.g6.xlarge	g6, general-purpose instance family	4	16	0.24	India (Mumbai) Germany (Frankfurt) US (Virginia) US (Silicon Valley)
				0.3	Singapore (Singapore) Indonesia (Jakarta) China (Hong Kong)
				0.18	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.12	China (Zhangjiakou)
ecs.c6.xlarge	c6, compute-optimized instance family	4	8	0.18	India (Mumbai) Singapore (Singapore) Indonesia (Jakarta) Germany (Frankfurt) US (Virginia) US (Silicon Valley)
				0.12	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) China (Zhangjiakou)
				0.24	China (Hong Kong)
ecs.r6.xlarge	r6, memory-optimized instance family	4	32	0.3	India (Mumbai) US (Virginia) US (Silicon Valley)
				0.36	Singapore (Singapore) Indonesia (Jakarta) China (Hong Kong) Germany (Frankfurt)
				0.24	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen)
				0.18	China (Zhangjiakou)
ecs.g6.large	g6, general-purpose instance family	2	8	0.12	India (Mumbai) Singapore (Singapore) Indonesia (Jakarta) China (Hong Kong) Germany (Frankfurt) US (Virginia) US (Silicon Valley)
ecs.g6.large	g6, general-purpose instance family	2	8	0.06	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) China (Zhangjiakou)
ecs.c6.large	c6, compute-optimized instance family	2	4	0.12	India (Mumbai) Singapore (Singapore) Indonesia (Jakarta) China (Hong Kong) Germany (Frankfurt) US (Virginia) US (Silicon Valley)
ecs.c6.large	c6, compute-optimized instance family	2	4	0.06	China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) China (Zhangjiakou)
ecs.r6.large	r6, memory-optimized instance family	2	16	0.12	India (Mumbai) China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) US (Virginia) US (Silicon Valley)
				0.18	Singapore (Singapore) Indonesia (Jakarta) China (Hong Kong) Germany (Frankfurt)
				0.06	China (Zhangjiakou)

Billing examples for shared resource groups

Scenario:
You used shared resource groups with the specified instance type in the China (Hangzhou) region to deploy a model service.
- The model service occupied 2 vCPUs and 8 GB of memory, and started to run at 09:00:00 (UTC+8) on June 3, 2019.
- You scaled in the model service and reduced the occupied resources to 1 vCPU and 4 GB of memory at 10:00:00 (UTC+8) on June 3, 2019.
- Then, you scaled out the model service and increased the occupied resources to 4 vCPUs and 16 GB of memory at 11:00:00 (UTC+8) on June 3, 2019.
- At 12:00:00 (UTC+8) on June 3, 2019, the model service stopped running.

The bill amount is calculated based on the following formula:

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

Billing rules for dedicated resource groups

Two types of billing methods are supported for dedicated resource groups: subscription and pay-as-you-go. Billing rules vary based on the two types of billing methods.


Item	Subscription	Pay-as-you-go
Billing formula	`Bill amount of each resource group = Number of resources × Unit price × Subscription duration` Note Subscription duration is measured in minutes.	`Bill amount of each resource group = Number of resources × (Unit price/60) × Usage duration` Note Subscription duration is measured in minutes.
Unit price	For more information about the prices, see Prices of dedicated resource groups.
Subscription or usage duration	After you purchase a dedicated resource group, the resource group is free of charge on the current day, and the subscription takes effect from the next day. For example, you purchased a dedicated resource group on July 31, 2019, and the subscription duration is one month. Then, the resource group expired at 00:00:00 (UTC+8) on August 31, 2019. Note Valid subscription durations: one month, two months, three months, and six months.	The billing starts immediately after a dedicated resource group is created and enters the Running state. The billing stops immediately after the resource group enters the No Node state.
Scaling	N/A	If you scale out a model service, newly added resources are billed after the scale-out activity is complete. If you scale in a model service, released resources are no longer billed, and only the remaining resources are billed.
Notes	Some resources may be unavailable in one or more regions for a short period of time. During the time period, you cannot purchase relevant resource groups in these regions.	Fees are charged on a per-minute basis. No fee is charged if a resource group is occupied for less than one minute. To prevent unnecessary costs, we recommend that you stop unwanted model services. Some resources may be unavailable in one or more regions for a short period of time. During the time period, you cannot purchase relevant resource groups in these regions.

Prices of dedicated resource groups

For more information about the pricing of the subscription dedicated resource groups, go to the Dedicated Node for EAS (Subscription) buy page.

For more information about the pricing of pay-as-you-go dedicated resource groups, go to the Dedicated Node for EAS (Pay-As-You-Go) buy page.

Billing examples for dedicated resource groups

Subscription
- Scenario:
  You subscribe to two subscription NVIDIA T4 GPUs in the China (Hangzhou) region for three months, and the specifications of each GPU are 4 vCPUs and 15 GB of memory. The unit price of the subscribed GPUs is USD 570 per month.
- The bill amount is calculated based on the following formula:
```
Bill amount: 2 × 570 × 3 = USD 3,420
```
Pay-as-you-go
- Scenario:
  You purchase two pay-as-you-go Elastic Compute Service (ECS) instances whose instance type is ecs.g6.6xlarge in the China (Hangzhou) region, and use the instances for 45 minutes. The specifications of each ECS instance are 24 vCPUs and 96 GB of memory. The unit price of the ECS instance of the ecs.g6.6xlarge type is USD 2.94 per hour.
- The bill amount is calculated based on the following formula:
```
Bill amount: 2 × (2.94/60) × 45 = USD 4.41
```