Vertex AI pricing

Prices are listed in US Dollars (USD). If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Vertex AI pricing compared to legacy product pricing

The costs for Vertex AI remain the same as they are for the legacy AI Platform and AutoML products that Vertex AI supersedes, with the following exceptions:

Legacy AI Platform Prediction and AutoML Tables predictions supported lower-cost, lower-performance machine types that aren't supported for Vertex AI Prediction and AutoML tabular.
Legacy AI Platform Prediction supported scale-to-zero, which isn't supported for Vertex AI Prediction.

Vertex AI also offers more ways to optimize costs, such as the following:

Optimized TensorFlow runtime.
Support for co-hosting models.
No minimum usage duration for Training and Prediction. Instead, usage is charged in 30 second increments.

Pricing for Generative AI support on Vertex AI

Preview

PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the GCP Service Specific Terms. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions. Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview terms and conditions (Preview Terms).

For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).

Trusted Tester Program Opt-Out. If you previously opted in to permit Google to use your data to improve pre-GA AI/ML services as part of the Trusted Tester Program terms, you can use the Trusted Tester Program - Opt Out Request form to opt out.

Generative AI support on Vertex AI charges by prediction request based on the combined character count of the input (prompt) and output (response). Characters are counted by UTF-8 code points and white space is excluded from the count. During the Preview stage, charges are 100% discounted.

Model	Type	Region	Function	Resource charge	Price	Pricing unit
text-bison@001	Input	Global	Online requests Batch requests	0%	$0.0010	1000 characters
text-bison@001	Output	Global	Online requests Batch requests	0%	$0.0010	1000 characters
chat-bison@001	Input	Global	Online requests Batch requests	0%	$0.0005	1000 characters
chat-bison@001	Output	Global	Online requests Batch requests	0%	$0.0005	1000 characters
textembedding-gecko@001	Input	Global	Online requests Batch requests	0%	$0.0001	1000 characters

Pricing for AutoML models

For Vertex AI AutoML models, you pay for three main activities:

Training the model
Deploying the model to an endpoint
Using the model to make predictions

Vertex AI uses predefined machine configurations for Vertex AutoML models, and the hourly rate for these activities reflects the resource usage.

The time required to train your model depends on the size and complexity of your training data. Models must be deployed before they can provide online predictions or online explanations.

You pay for each model deployed to an endpoint, even if no prediction is made. You must undeploy your model to stop incurring further charges. Models that are not deployed or have failed to deploy are not charged.

You pay only for compute hours used; if training fails for any reason other than a user-initiated cancellation, you are not billed for the time. You are charged for training time if you cancel the operation.

Select a model type below for pricing information.

Image data

Operation	Price per node hour (classification)	Price per node hour (object detection)
Training	$3.465	$3.465
Training (Edge on-device model)	$18.00	$18.00
Deployment and online prediction	$1.375	$2.002
Batch prediction	$2.222	$2.222

Video data

Operation	Price per node hour (classification, object tracking)	Price per node hour (action recognition)
Training	$3.234	$3.300
Training (Edge on-device model)	$10.78	$11.00
Predictions	$0.462	$0.550

Tabular data

Operation	Price per node hour for classification/regression	Price for forecasting
Training	$21.252	Refer to Vertex AI Forecast
Prediction	Same price as predictions for custom-trained models. Vertex AI performs batch prediction using 40 `n1-highmem-8` machines.	Refer to Vertex AI Forecast

Text data

Operation	Price
Legacy data upload (PDF only)	First 1,000 pages free each month $1.50 per 1,000 pages $0.60 per 1,000 pages over 5,000,000
Training	$3.30 per hour
Deployment	$0.05 per hour
Prediction	$5.00 per 1,000 text records $25.00 per 1,000 document pages, such as PDF files (legacy only)

Prices for Vertex AutoML text prediction requests are computed based on the number of text records you send for analysis. A text record is plain text of up to 1,000 Unicode characters (including whitespace and any markup such as HTML or XML tags).

If the text provided in a prediction request contains more than 1,000 characters, it counts as one text record for each 1,000 characters. For example, if you send three requests that contain 800, 1,500, and 600 characters respectively, you would be charged for four text records: one for the first request (800), two for the second request (1,500), and one for the third request (600).

Prediction charges for Vertex Explainable AI

Compute associated with Vertex Explainable AI is charged at same rate as prediction. However, explanations take longer to process than normal predictions, so heavy usage of Vertex Explainable AI along with auto-scaling could result in more nodes being started, which would increase prediction charges.

Vertex AI Forecast

AutoML

Stage	Pricing
Prediction	$0.2 per 1K data points* (0-1M points) $0.1 per 1K data points* (1M-50M points) $0.02 per 1K data points* (>50M points)
Training	$21.25/hr in all regions
Explainable AI	Explainability using Shapley values. Refer to Vertex AI Prediction and Explanation pricing page.

* A prediction data point is one time point in the forecast horizon. For example, with daily granularity a 7-day horizon is 7 points per each time series.

Up to 5 prediction quantiles can be included at no additional cost.
The number of data points consumed per tier is refreshed monthly.

ARIMA+

Stage	Pricing
Prediction	$5.00 per TB
Training	$250.00 per TB x Number of Candidate Models x Number of Backtesting Windows*
Explainable AI	Explainability with time series decomposition does not add any additional cost. Explainability using Shapley values is not supported.

Refer to the BigQuery ML pricing page for additional details. Each training and prediction job incurs the cost of 1 managed pipeline run, as described in Vertex AI pricing.

* A backtesting window is created for each period in the test set. The AUTO_ARIMA_MAX_ORDER used determines the number of candidate models. It ranges from 6-42 for models with multiple time series.

Custom-trained models

Training

The tables below provide the approximate price per hour of various training configurations. You can choose a custom configuration of selected machine types. To calculate pricing, sum the costs of the virtual machines you use.

If you use Compute Engine machine types and attach accelerators, the cost of the accelerators is separate. To calculate this cost, multiply the prices in the table of accelerators below by how many machine hours of each type of accelerator you use.

Machine types

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Accelerators

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

* The price for training using a Cloud TPU Pod is based on the number of cores in the Pod. The number of cores in a pod is always a multiple of 32. To determine the price of training on a Pod that has more than 32 cores, take the price for a 32-core Pod, and multiply it by the number of cores, divided by 32. For example, for a 128-core Pod, the price is (32-core Pod price) * (128/32). For information about which Cloud TPU Pods are available for a specific region, see System Architecture in the Cloud TPU documentation.

Disks

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

All use is subject to the Vertex AI quota policy.
You are required to store your data and program files in Google Cloud Storage buckets during the Vertex AI lifecycle. See more about Cloud Storage usage.

You are charged for training your models from the moment when resources are provisioned for a job until the job finishes.

Scale tiers for predefined configurations (AI Platform Training)

You can control the type of processing cluster to use when training your model. The simplest way is to choose from one of the predefined configurations called scale tiers. Read more about scale tiers.

Machine types for custom configurations

If you use Vertex AI or select CUSTOM as your scale tier for AI Platform Training, you have control over the number and type of virtual machines to use for the cluster's master, worker and parameter servers. Read more about machine types for Vertex AI and machine types for AI Platform Training.

The cost of training with a custom processing cluster is the sum of all the machines you specify. You are charged for the total time of the job, not for the active processing time of individual machines.

Prediction and explanation

The following tables provide the prices of batch prediction, online prediction, and online explanation per node hour. A node hour represents the time a virtual machine spends running your prediction job or waiting in an active state (an endpoint with one or more models deployed) to handle prediction or explanation requests.

Choose a region to view its pricing table:

The Americas
Europe
Asia Pacific
Middle east

Pricing for the Americas

The following tables provide the price per node hour for each machine type.

E2 Series

e2-standard-2 Approximations:

`us-west2`	$0.0926
`us-west4`	$0.0868
`us-east4`	$0.0868
`northamerica-northeast1`	$0.0848
`northamerica-northeast2`	$0.0848
`southamerica-east1`	$0.1223
Other Americas regions	$0.0771

e2-standard-4 Approximations:

`us-west2`	$0.1851
`us-west4`	$0.1736
`us-east4`	$0.1736
`northamerica-northeast1`	$0.1697
`northamerica-northeast2`	$0.1697
`southamerica-east1`	$0.2446
Other Americas regions	$0.1541

e2-standard-8 Approximations:

`us-west2`	$0.3702
`us-west4`	$0.3471
`us-east4`	$0.3471
`northamerica-northeast1`	$0.3393
`northamerica-northeast2`	$0.3393
`southamerica-east1`	$0.4893
Other Americas regions	$0.3082

e2-standard-16 Approximations:

`us-west2`	$0.7405
`us-west4`	$0.6942
`us-east4`	$0.6942
`northamerica-northeast1`	$0.6787
`northamerica-northeast2`	$0.6787
`southamerica-east1`	$0.9786
Other Americas regions	$0.6165

e2-standard-32 Approximations:

`us-west2`	$1.4809
`us-west4`	$1.3885
`us-east4`	$1.3885
`northamerica-northeast1`	$1.3574
`northamerica-northeast2`	$1.3574
`southamerica-east1`	$1.9572
Other Americas regions	$1.2329

e2-highmem-2 Approximations:

`us-west2`	$0.1249
`us-west4`	$0.1171
`us-east4`	$0.1171
`northamerica-northeast1`	$0.1144
`northamerica-northeast2`	$0.1144
`southamerica-east1`	$0.165
Other Americas regions	$0.1039

e2-highmem-4 Approximations:

`us-west2`	$0.2497
`us-west4`	$0.2341
`us-east4`	$0.2341
`northamerica-northeast1`	$0.2289
`northamerica-northeast2`	$0.2289
`southamerica-east1`	$0.33
Other Americas regions	$0.2079

e2-highmem-8 Approximations:

`us-west2`	$0.4994
`us-west4`	$0.4682
`us-east4`	$0.4682
`northamerica-northeast1`	$0.4578
`northamerica-northeast2`	$0.4578
`southamerica-east1`	$0.66
Other Americas regions	$0.4158

e2-highmem-16 Approximations:

`us-west2`	$0.9989
`us-west4`	$0.9365
`us-east4`	$0.9365
`northamerica-northeast1`	$0.9155
`northamerica-northeast2`	$0.9155
`southamerica-east1`	$1.3201
Other Americas regions	$0.8316

e2-highcpu-2 Approximations:

`us-west2`	$0.0683
`us-west4`	$0.0641
`us-east4`	$0.0641
`northamerica-northeast1`	$0.0626
`northamerica-northeast2`	$0.0626
`southamerica-east1`	$0.0903
Other Americas regions	$0.0569

e2-highcpu-4 Approximations:

`us-west2`	$0.1367
`us-west4`	$0.1281
`us-east4`	$0.1281
`northamerica-northeast1`	$0.1253
`northamerica-northeast2`	$0.1253
`southamerica-east1`	$0.1806
Other Americas regions	$0.1138

e2-highcpu-8 Approximations:

`us-west2`	$0.2733
`us-west4`	$0.2563
`us-east4`	$0.2563
`northamerica-northeast1`	$0.2505
`northamerica-northeast2`	$0.2505
`southamerica-east1`	$0.3612
Other Americas regions	$0.2276

e2-highcpu-16 Approximations:

`us-west2`	$0.5467
`us-west4`	$0.5126
`us-east4`	$0.5126
`northamerica-northeast1`	$0.501
`northamerica-northeast2`	$0.501
`southamerica-east1`	$0.7225
Other Americas regions	$0.4551

e2-highcpu-32 Approximations:

`us-west2`	$1.0933
`us-west4`	$1.0252
`us-east4`	$1.0252
`northamerica-northeast1`	$1.0021
`northamerica-northeast2`	$1.0021
`southamerica-east1`	$1.4449
Other Americas regions	$0.9102

N1 Series

n1-standard-2 Approximations:

`us-east4`	$0.123
`northamerica-northeast1`	$0.1203
Other Americas regions	$0.1093

n1-standard-4 Approximations:

`us-east4`	$0.2461
`northamerica-northeast1`	$0.2405
Other Americas regions	$0.2186

n1-standard-8 Approximations:

`us-east4`	$0.4922
`northamerica-northeast1`	$0.4811
Other Americas regions	$0.4372

n1-standard-16 Approximations:

`us-east4`	$0.9843
`northamerica-northeast1`	$0.9622
Other Americas regions	$0.8744

n1-standard-32 Approximations:

`us-east4`	$1.9687
`northamerica-northeast1`	$1.9243
Other Americas regions	$1.7488

n1-highmem-2 Approximations:

`us-east4`	$0.1532
`northamerica-northeast1`	$0.1498
Other Americas regions	$0.1361

n1-highmem-4 Approximations:

`us-east4`	$0.3064
`northamerica-northeast1`	$0.2995
Other Americas regions	$0.2723

n1-highmem-8 Approximations:

`us-east4`	$0.6129
`northamerica-northeast1`	$0.5991
Other Americas regions	$0.5445

n1-highmem-16 Approximations:

`us-east4`	$1.2257
`northamerica-northeast1`	$1.1982
Other Americas regions	$1.089

n1-highcpu-2 Approximations:

`us-east4`	$0.0918
`northamerica-northeast1`	$0.0897
Other Americas regions	$0.0815

n1-highcpu-4 Approximations:

`us-east4`	$0.1835
`northamerica-northeast1`	$0.1794
Other Americas regions	$0.163

n1-highcpu-8 Approximations:

`us-east4`	$0.3671
`northamerica-northeast1`	$0.3588
Other Americas regions	$0.326

n1-highcpu-16 Approximations:

`us-east4`	$0.7341
`northamerica-northeast1`	$0.7176
Other Americas regions	$0.6519

n1-highcpu-32 Approximations:

`us-east4`	$1.4683
`northamerica-northeast1`	$1.4352
Other Americas regions	$1.3039

N2 Series

n2-standard-2 Approximations:

`northamerica_northeast1`	$0.123
`northamerica_northeast2`	$0.123
`southamerica_east1`	$0.1773
`us_central1`	$0.1117
`us_east1`	$0.1117
`us_east4`	$0.1258
`us_south1`	$0.1318
`us_west1`	$0.1117
`us_west2`	$0.1341
`us_west3`	$0.1341
`us_west4`	$0.1258

n2-standard-4 Approximations:

`northamerica_northeast1`	$0.2459
`northamerica_northeast2`	$0.2459
`southamerica_east1`	$0.3546
`us_central1`	$0.2234
`us_east1`	$0.2234
`us_east4`	$0.2516
`us_south1`	$0.2636
`us_west1`	$0.2234
`us_west2`	$0.2683
`us_west3`	$0.2683
`us_west4`	$0.2516

n2-standard-8 Approximations:

`northamerica_northeast1`	$0.4918
`northamerica_northeast2`	$0.4918
`southamerica_east1`	$0.7091
`us_central1`	$0.4467
`us_east1`	$0.4467
`us_east4`	$0.5031
`us_south1`	$0.5272
`us_west1`	$0.4467
`us_west2`	$0.5366
`us_west3`	$0.5366
`us_west4`	$0.5031

n2-standard-16 Approximations:

`northamerica_northeast1`	$0.9836
`northamerica_northeast2`	$0.9836
`southamerica_east1`	$1.4183
`us_central1`	$0.8935
`us_east1`	$0.8935
`us_east4`	$1.0063
`us_south1`	$1.0543
`us_west1`	$0.8935
`us_west2`	$1.0732
`us_west3`	$1.0732
`us_west4`	$1.0062

n2-standard-32 Approximations:

`northamerica_northeast1`	$1.9673
`northamerica_northeast2`	$1.9673
`southamerica_east1`	$2.8365
`us_central1`	$1.787
`us_east1`	$1.787
`us_east4`	$2.0126
`us_south1`	$2.1087
`us_west1`	$1.787
`us_west2`	$2.1464
`us_west3`	$2.1464
`us_west4`	$2.0125

n2-highmem-2 Approximations:

`northamerica_northeast1`	$0.1659
`northamerica_northeast2`	$0.1659
`southamerica_east1`	$0.2392
`us_central1`	$0.1507
`us_east1`	$0.1507
`us_east4`	$0.1697
`us_south1`	$0.1778
`us_west1`	$0.1507
`us_west2`	$0.181
`us_west3`	$0.181
`us_west4`	$0.1697

n2-highmem-4 Approximations:

`northamerica_northeast1`	$0.3317
`northamerica_northeast2`	$0.3317
`southamerica_east1`	$0.4783
`us_central1`	$0.3013
`us_east1`	$0.3013
`us_east4`	$0.3394
`us_south1`	$0.3556
`us_west1`	$0.3013
`us_west2`	$0.3619
`us_west3`	$0.3619
`us_west4`	$0.3393

n2-highmem-8 Approximations:

`northamerica_northeast1`	$0.6634
`northamerica_northeast2`	$0.6634
`southamerica_east1`	$0.9566
`us_central1`	$0.6027
`us_east1`	$0.6027
`us_east4`	$0.6787
`us_south1`	$0.7112
`us_west1`	$0.6027
`us_west2`	$0.7239
`us_west3`	$0.7239
`us_west4`	$0.6787

n2-highmem-16 Approximations:

`northamerica_northeast1`	$1.3269
`northamerica_northeast2`	$1.3269
`southamerica_east1`	$1.9132
`us_central1`	$1.2053
`us_east1`	$1.2053
`us_east4`	$1.3574
`us_south1`	$1.4223
`us_west1`	$1.2053
`us_west2`	$1.4477
`us_west3`	$1.4477
`us_west4`	$1.3574

n2-highcpu-2 Approximations:

`northamerica_northeast1`	$0.0908
`northamerica_northeast2`	$0.0908
`southamerica_east1`	$0.1309
`us_central1`	$0.0825
`us_east1`	$0.0825
`us_east4`	$0.0929
`us_south1`	$0.0973
`us_west1`	$0.0825
`us_west2`	$0.099
`us_west3`	$0.099
`us_west4`	$0.0929

n2-highcpu-4 Approximations:

`northamerica_northeast1`	$0.1815
`northamerica_northeast2`	$0.1815
`southamerica_east1`	$0.2618
`us_central1`	$0.1649
`us_east1`	$0.1649
`us_east4`	$0.1857
`us_south1`	$0.1946
`us_west1`	$0.1649
`us_west2`	$0.1981
`us_west3`	$0.1981
`us_west4`	$0.1857

n2-highcpu-8 Approximations:

`northamerica_northeast1`	$0.3631
`northamerica_northeast2`	$0.3631
`southamerica_east1`	$0.5235
`us_central1`	$0.3298
`us_east1`	$0.3298
`us_east4`	$0.3715
`us_south1`	$0.3892
`us_west1`	$0.3298
`us_west2`	$0.3961
`us_west3`	$0.3961
`us_west4`	$0.3714

n2-highcpu-16 Approximations:

`northamerica_northeast1`	$0.7262
`northamerica_northeast2`	$0.7262
`southamerica_east1`	$1.0471
`us_central1`	$0.6596
`us_east1`	$0.6596
`us_east4`	$0.7429
`us_south1`	$0.7783
`us_west1`	$0.6596
`us_west2`	$0.7923
`us_west3`	$0.7923
`us_west4`	$0.7429

n2-highcpu-32 Approximations:

`northamerica_northeast1`	$1.4523
`northamerica_northeast2`	$1.4523
`southamerica_east1`	$2.0941
`us_central1`	$1.3192
`us_east1`	$1.3192
`us_east4`	$1.4858
`us_south1`	$1.5567
`us_west1`	$1.3192
`us_west2`	$1.5846
`us_west3`	$1.5846
`us_west4`	$1.4858

N2D Series

n2d-standard-2 Approximations:

`northamerica_northeast1`	$0.107
`southamerica_east1`	$0.1542
`us_central1`	$0.0972
`us_east1`	$0.0972
`us_east4`	$0.1094
`us_west1`	$0.0972
`us_west2`	$0.1167
`us_west4`	$0.1094

n2d-standard-4 Approximations:

`northamerica_northeast1`	$0.2139
`southamerica_east1`	$0.3085
`us_central1`	$0.1943
`us_east1`	$0.1943
`us_east4`	$0.2189
`us_west1`	$0.1943
`us_west2`	$0.2334
`us_west4`	$0.2189

n2d-standard-8 Approximations:

`northamerica_northeast1`	$0.4279
`southamerica_east1`	$0.617
`us_central1`	$0.3887
`us_east1`	$0.3887
`us_east4`	$0.4377
`us_west1`	$0.3887
`us_west2`	$0.4668
`us_west4`	$0.4377

n2d-standard-16 Approximations:

`northamerica_northeast1`	$0.8558
`southamerica_east1`	$1.2339
`us_central1`	$0.7773
`us_east1`	$0.7773
`us_east4`	$0.8755
`us_west1`	$0.7773
`us_west2`	$0.9336
`us_west4`	$0.8755

n2d-standard-32 Approximations:

`northamerica_northeast1`	$1.7116
`southamerica_east1`	$2.4678
`us_central1`	$1.5547
`us_east1`	$1.5547
`us_east4`	$1.7509
`us_west1`	$1.5547
`us_west2`	$1.8673
`us_west4`	$1.7509

n2d-highmem-2 Approximations:

`northamerica_northeast1`	$0.1443
`southamerica_east1`	$0.2081
`us_central1`	$0.1311
`us_east1`	$0.1311
`us_east4`	$0.1476
`us_west1`	$0.1311
`us_west2`	$0.1574
`us_west4`	$0.1476

n2d-highmem-4 Approximations:

`northamerica_northeast1`	$0.2886
`southamerica_east1`	$0.4161
`us_central1`	$0.2622
`us_east1`	$0.2622
`us_east4`	$0.2952
`us_west1`	$0.2622
`us_west2`	$0.3149
`us_west4`	$0.2952

n2d-highmem-8 Approximations:

`northamerica_northeast1`	$0.5772
`southamerica_east1`	$0.8323
`us_central1`	$0.5243
`us_east1`	$0.5243
`us_east4`	$0.5905
`us_west1`	$0.5243
`us_west2`	$0.6297
`us_west4`	$0.5905

n2d-highmem-16 Approximations:

`northamerica_northeast1`	$1.1545
`southamerica_east1`	$1.6646
`us_central1`	$1.0486
`us_east1`	$1.0486
`us_east4`	$1.181
`us_west1`	$1.0486
`us_west2`	$1.2595
`us_west4`	$1.181

n2d-highcpu-2 Approximations:

`northamerica_northeast1`	$0.079
`southamerica_east1`	$0.1139
`us_central1`	$0.0717
`us_east1`	$0.0717
`us_east4`	$0.0808
`us_west1`	$0.0717
`us_west2`	$0.0862
`us_west4`	$0.0808

n2d-highcpu-4 Approximations:

`northamerica_northeast1`	$0.1579
`southamerica_east1`	$0.2277
`us_central1`	$0.1435
`us_east1`	$0.1435
`us_east4`	$0.1616
`us_west1`	$0.1435
`us_west2`	$0.1723
`us_west4`	$0.1616

n2d-highcpu-8 Approximations:

`northamerica_northeast1`	$0.3159
`southamerica_east1`	$0.4555
`us_central1`	$0.2869
`us_east1`	$0.2869
`us_east4`	$0.3232
`us_west1`	$0.2869
`us_west2`	$0.3446
`us_west4`	$0.3232

n2d-highcpu-16 Approximations:

`northamerica_northeast1`	$0.6318
`southamerica_east1`	$0.9109
`us_central1`	$0.5739
`us_east1`	$0.5739
`us_east4`	$0.6463
`us_west1`	$0.5739
`us_west2`	$0.6893
`us_west4`	$0.6463

n2d-highcpu-32 Approximations:

`northamerica_northeast1`	$1.2636
`southamerica_east1`	$1.8219
`us_central1`	$1.1477
`us_east1`	$1.1477
`us_east4`	$1.2927
`us_west1`	$1.1477
`us_west2`	$1.3786
`us_west4`	$1.2927

C2 Series

c2-standard-4 Approximations:

`northamerica_northeast1`	$0.264
`southamerica_east1`	$0.3812
`us_central1`	$0.24
`us_east1`	$0.24
`us_east4`	$0.2702
`us_west1`	$0.24
`us_west2`	$0.2884
`us_west3`	$0.2889
`us_west4`	$0.2702

c2-standard-8 Approximations:

`northamerica_northeast1`	$0.5281
`southamerica_east1`	$0.7623
`us_central1`	$0.4801
`us_east1`	$0.4801
`us_east4`	$0.5405
`us_west1`	$0.4801
`us_west2`	$0.5768
`us_west3`	$0.5778
`us_west4`	$0.5405

c2-standard-16 Approximations:

`northamerica_northeast1`	$1.0562
`southamerica_east1`	$1.5246
`us_central1`	$0.9601
`us_east1`	$0.9601
`us_east4`	$1.081
`us_west1`	$0.9601
`us_west2`	$1.1537
`us_west3`	$1.1555
`us_west4`	$1.081

c2-standard-30 Approximations:

`northamerica_northeast1`	$1.9803
`southamerica_east1`	$2.8587
`us_central1`	$1.8002
`us_east1`	$1.8002
`us_east4`	$2.0269
`us_west1`	$1.8002
`us_west2`	$2.1631
`us_west3`	$2.1666
`us_west4`	$2.0269

c2-standard-60 Approximations:

`northamerica_northeast1`	$3.9606
`southamerica_east1`	$5.7173
`us_central1`	$3.6004
`us_east1`	$3.6004
`us_east4`	$4.0537
`us_west1`	$3.6004
`us_west2`	$4.3263
`us_west3`	$4.3332
`us_west4`	$4.0537

C2D Series

c2d-standard-2 Approximations:

`us_central1`	$0.1044
`us_east1`	$0.1044
`us_east4`	$0.1176

c2d-standard-4 Approximations:

`us_central1`	$0.2088
`us_east1`	$0.2088
`us_east4`	$0.2352

c2d-standard-8 Approximations:

`us_central1`	$0.4177
`us_east1`	$0.4177
`us_east4`	$0.4704

c2d-standard-16 Approximations:

`us_central1`	$0.8353
`us_east1`	$0.8353
`us_east4`	$0.9408

c2d-standard-32 Approximations:

`us_central1`	$1.6707
`us_east1`	$1.6707
`us_east4`	$1.8815

c2d-standard-56 Approximations:

`us_central1`	$2.9237
`us_east1`	$2.9237
`us_east4`	$3.2926

c2d-standard-112 Approximations:

`us_central1`	$5.8474
`us_east1`	$5.8474
`us_east4`	$6.5853

A2 Series

a2-highgpu-1g Approximations:

`us-west4`	$4.525
Other Americas regions	$4.2245

a2-highgpu-2g Approximations:

`us-west4`	$9.05
Other Americas regions	$8.449

a2-highgpu-4g Approximations:

`us-west4`	$18.1001
Other Americas regions	$16.898

a2-highgpu-8g Approximations:

`us-west4`	$36.2001
Other Americas regions	$33.796

a2-megagpu-16g Approximations:

`us-west4`	$68.4695
Other Americas regions	$64.1021

G2 Series

g2-standard-4 Approximations:

us-central1 $0.8129

g2-standard-8 Approximations:

us-central1 $0.9818

g2-standard-12 Approximations:

us-central1 $1.1507

g2-standard-16 Approximations:

us-central1 $1.3196

g2-standard-24 Approximations:

us-central1 $2.3014

g2-standard-32 Approximations:

us-central1 $1.9951

g2-standard-48 Approximations:

us-central1 $4.6028

g2-standard-96 Approximations:

us-central1 $9.2055

Pricing for Europe

The following tables provide the price per node hour for each machine type.

E2 Series

e2-standard-2 Approximations:

`europe-west1`	$0.0848
`europe-west2`	$0.0993
`europe-west3`	$0.0993
`europe-west4`	$0.0848
`europe-west6`	$0.1078
`europe-west9`	$0.1079

e2-standard-4 Approximations:

`europe-west1`	$0.1695
`europe-west2`	$0.1986
`europe-west3`	$0.1986
`europe-west4`	$0.1697
`europe-west6`	$0.2156
`europe-west9`	$0.2158

e2-standard-8 Approximations:

`europe-west1`	$0.3391
`europe-west2`	$0.3971
`europe-west3`	$0.3971
`europe-west4`	$0.3393
`europe-west6`	$0.4313
`europe-west9`	$0.4316

e2-standard-16 Approximations:

`europe-west1`	$0.6782
`europe-west2`	$0.7943
`europe-west3`	$0.7943
`europe-west4`	$0.6787
`europe-west6`	$0.8626
`europe-west9`	$0.8631

e2-standard-32 Approximations:

`europe-west1`	$1.3563
`europe-west2`	$1.5885
`europe-west3`	$1.5885
`europe-west4`	$1.3574
`europe-west6`	$1.7251
`europe-west9`	$1.7262

e2-highmem-2 Approximations:

`europe-west1`	$0.1144
`europe-west2`	$0.1339
`europe-west3`	$0.1339
`europe-west4`	$0.1144
`europe-west6`	$0.1454
`europe-west9`	$0.1455

e2-highmem-4 Approximations:

`europe-west1`	$0.2287
`europe-west2`	$0.2679
`europe-west3`	$0.2679
`europe-west4`	$0.2289
`europe-west6`	$0.2909
`europe-west9`	$0.2911

e2-highmem-8 Approximations:

`europe-west1`	$0.4574
`europe-west2`	$0.5357
`europe-west3`	$0.5357
`europe-west4`	$0.4578
`europe-west6`	$0.5818
`europe-west9`	$0.5822

e2-highmem-16 Approximations:

`europe-west1`	$0.9149
`europe-west2`	$1.0714
`europe-west3`	$1.0714
`europe-west4`	$0.9155
`europe-west6`	$1.1636
`europe-west9`	$1.1643

e2-highcpu-2 Approximations:

`europe-west1`	$0.0626
`europe-west2`	$0.0733
`europe-west3`	$0.0733
`europe-west4`	$0.0626
`europe-west6`	$0.0796
`europe-west9`	$0.0796

e2-highcpu-4 Approximations:

`europe-west1`	$0.1252
`europe-west2`	$0.1466
`europe-west3`	$0.1466
`europe-west4`	$0.1253
`europe-west6`	$0.1592
`europe-west9`	$0.1593

e2-highcpu-8 Approximations:

`europe-west1`	$0.2503
`europe-west2`	$0.2932
`europe-west3`	$0.2932
`europe-west4`	$0.2505
`europe-west6`	$0.3184
`europe-west9`	$0.3186

e2-highcpu-16 Approximations:

`europe-west1`	$0.5006
`europe-west2`	$0.5864
`europe-west3`	$0.5864
`europe-west4`	$0.501
`europe-west6`	$0.6368
`europe-west9`	$0.6372

e2-highcpu-32 Approximations:

`europe-west1`	$1.0013
`europe-west2`	$1.1728
`europe-west3`	$1.1728
`europe-west4`	$1.0021
`europe-west6`	$1.2736
`europe-west9`	$1.2743

N1 Series

n1-standard-2 Approximations:

`europe-west2`	$0.1408
Other Europe regions	$0.1265

n1-standard-4 Approximations:

`europe-west2`	$0.2815
Other Europe regions	$0.2531

n1-standard-8 Approximations:

`europe-west2`	$0.563
Other Europe regions	$0.5061

n1-standard-16 Approximations:

`europe-west2`	$1.126
Other Europe regions	$1.0123

n1-standard-32 Approximations:

`europe-west2`	$2.2521
Other Europe regions	$2.0245

n1-highmem-2 Approximations:

`europe-west2`	$0.1753
Other Europe regions	$0.1575

n1-highmem-4 Approximations:

`europe-west2`	$0.3506
Other Europe regions	$0.3151

n1-highmem-8 Approximations:

`europe-west2`	$0.7011
Other Europe regions	$0.6302

n1-highmem-16 Approximations:

`europe-west2`	$1.4022
Other Europe regions	$1.2603

n1-highcpu-2 Approximations:

`europe-west2`	$0.105
Other Europe regions	$0.0944

n1-highcpu-4 Approximations:

`europe-west2`	$0.21
Other Europe regions	$0.1888

n1-highcpu-8 Approximations:

`europe-west2`	$0.4199
Other Europe regions	$0.3776

n1-highcpu-16 Approximations:

`europe-west2`	$0.8398
Other Europe regions	$0.7552

n1-highcpu-32 Approximations:

`europe-west2`	$1.6796
Other Europe regions	$1.5104

N2 Series

n2-standard-2 Approximations:

`europe_central2`	$0.1439
`europe_west1`	$0.1229
`europe_west2`	$0.1439
`europe_west3`	$0.1439
`europe_west4`	$0.1229
`europe_west6`	$0.1564
`europe_west9`	$0.1296

n2-standard-4 Approximations:

`europe_central2`	$0.2878
`europe_west1`	$0.2457
`europe_west2`	$0.2878
`europe_west3`	$0.2878
`europe_west4`	$0.2457
`europe_west6`	$0.3127
`europe_west9`	$0.2591

n2-standard-8 Approximations:

`europe_central2`	$0.5756
`europe_west1`	$0.4914
`europe_west2`	$0.5756
`europe_west3`	$0.5756
`europe_west4`	$0.4914
`europe_west6`	$0.6254
`europe_west9`	$0.5182

n2-standard-16 Approximations:

`europe_central2`	$1.1511
`europe_west1`	$0.9829
`europe_west2`	$1.1511
`europe_west3`	$1.1511
`europe_west4`	$0.9828
`europe_west6`	$1.2508
`europe_west9`	$1.0364

n2-standard-32 Approximations:

`europe_central2`	$2.3023
`europe_west1`	$1.9658
`europe_west2`	$2.3023
`europe_west3`	$2.3023
`europe_west4`	$1.9657
`europe_west6`	$2.5017
`europe_west9`	$2.0729

n2-highmem-2 Approximations:

`europe_central2`	$0.1941
`europe_west1`	$0.1657
`europe_west2`	$0.1941
`europe_west3`	$0.1941
`europe_west4`	$0.1657
`europe_west6`	$0.2109
`europe_west9`	$0.1748

n2-highmem-4 Approximations:

`europe_central2`	$0.3882
`europe_west1`	$0.3315
`europe_west2`	$0.3882
`europe_west3`	$0.3882
`europe_west4`	$0.3315
`europe_west6`	$0.4218
`europe_west9`	$0.3495

n2-highmem-8 Approximations:

`europe_central2`	$0.7764
`europe_west1`	$0.663
`europe_west2`	$0.7764
`europe_west3`	$0.7764
`europe_west4`	$0.6629
`europe_west6`	$0.8436
`europe_west9`	$0.6991

n2-highmem-16 Approximations:

`europe_central2`	$1.5528
`europe_west1`	$1.3259
`europe_west2`	$1.5528
`europe_west3`	$1.5528
`europe_west4`	$1.3259
`europe_west6`	$1.6873
`europe_west9`	$1.3982

n2-highcpu-2 Approximations:

`europe_central2`	$0.1062
`europe_west1`	$0.0907
`europe_west2`	$0.1062
`europe_west3`	$0.1062
`europe_west4`	$0.0907
`europe_west6`	$0.1154
`europe_west9`	$0.0956

n2-highcpu-4 Approximations:

`europe_central2`	$0.2125
`europe_west1`	$0.1814
`europe_west2`	$0.2125
`europe_west3`	$0.2125
`europe_west4`	$0.1814
`europe_west6`	$0.2309
`europe_west9`	$0.1913

n2-highcpu-8 Approximations:

`europe_central2`	$0.4249
`europe_west1`	$0.3628
`europe_west2`	$0.4249
`europe_west3`	$0.4249
`europe_west4`	$0.3628
`europe_west6`	$0.4617
`europe_west9`	$0.3826

n2-highcpu-16 Approximations:

`europe_central2`	$0.8499
`europe_west1`	$0.7256
`europe_west2`	$0.8499
`europe_west3`	$0.8499
`europe_west4`	$0.7256
`europe_west6`	$0.9235
`europe_west9`	$0.7651

n2-highcpu-32 Approximations:

`europe_central2`	$1.6997
`europe_west1`	$1.4512
`europe_west2`	$1.6997
`europe_west3`	$1.6997
`europe_west4`	$1.4511
`europe_west6`	$1.847
`europe_west9`	$1.5303

N2D Series

n2d-standard-2 Approximations:

`europe_west1`	$0.1069
`europe_west2`	$0.1252
`europe_west3`	$0.1252
`europe_west4`	$0.107
`europe_west9`	$0.1127

n2d-standard-4 Approximations:

`europe_west1`	$0.2138
`europe_west2`	$0.2504
`europe_west3`	$0.2504
`europe_west4`	$0.2139
`europe_west9`	$0.2254

n2d-standard-8 Approximations:

`europe_west1`	$0.4275
`europe_west2`	$0.5007
`europe_west3`	$0.5007
`europe_west4`	$0.4279
`europe_west9`	$0.4509

n2d-standard-16 Approximations:

`europe_west1`	$0.8551
`europe_west2`	$1.0015
`europe_west3`	$1.0015
`europe_west4`	$0.8558
`europe_west9`	$0.9017

n2d-standard-32 Approximations:

`europe_west1`	$1.7102
`europe_west2`	$2.0029
`europe_west3`	$2.0029
`europe_west4`	$1.7116
`europe_west9`	$1.8034

n2d-highmem-2 Approximations:

`europe_west1`	$0.1442
`europe_west2`	$0.1689
`europe_west3`	$0.1689
`europe_west4`	$0.1443
`europe_west9`	$0.1521

n2d-highmem-4 Approximations:

`europe_west1`	$0.2884
`europe_west2`	$0.3377
`europe_west3`	$0.3377
`europe_west4`	$0.2886
`europe_west9`	$0.3041

n2d-highmem-8 Approximations:

`europe_west1`	$0.5768
`europe_west2`	$0.6755
`europe_west3`	$0.6755
`europe_west4`	$0.5772
`europe_west9`	$0.6082

n2d-highmem-16 Approximations:

`europe_west1`	$1.1535
`europe_west2`	$1.3509
`europe_west3`	$1.3509
`europe_west4`	$1.1545
`europe_west9`	$1.2164

n2d-highcpu-2 Approximations:

`europe_west1`	$0.0789
`europe_west2`	$0.0924
`europe_west3`	$0.0924
`europe_west4`	$0.079
`europe_west9`	$0.0832

n2d-highcpu-4 Approximations:

`europe_west1`	$0.1578
`europe_west2`	$0.1848
`europe_west3`	$0.1848
`europe_west4`	$0.1579
`europe_west9`	$0.1664

n2d-highcpu-8 Approximations:

`europe_west1`	$0.3156
`europe_west2`	$0.3697
`europe_west3`	$0.3697
`europe_west4`	$0.3159
`europe_west9`	$0.3328

n2d-highcpu-16 Approximations:

`europe_west1`	$0.6313
`europe_west2`	$0.7394
`europe_west3`	$0.7394
`europe_west4`	$0.6318
`europe_west9`	$0.6657

n2d-highcpu-32 Approximations:

`europe_west1`	$1.2625
`europe_west2`	$1.4787
`europe_west3`	$1.4787
`europe_west4`	$1.2636
`europe_west9`	$1.3314

C2 Series

c2-standard-4 Approximations:

`europe_west1`	$0.2641
`europe_west2`	$0.3094
`europe_west3`	$0.3092
`europe_west4`	$0.2643
`europe_west6`	$0.3362

c2-standard-8 Approximations:

`europe_west1`	$0.5283
`europe_west2`	$0.6187
`europe_west3`	$0.6184
`europe_west4`	$0.5285
`europe_west6`	$0.6724

c2-standard-16 Approximations:

`europe_west1`	$1.0565
`europe_west2`	$1.2375
`europe_west3`	$1.2368
`europe_west4`	$1.0571
`europe_west6`	$1.3449

c2-standard-30 Approximations:

`europe_west1`	$1.981
`europe_west2`	$2.3202
`europe_west3`	$2.3191
`europe_west4`	$1.982
`europe_west6`	$2.5216

c2-standard-60 Approximations:

`europe_west1`	$3.962
`europe_west2`	$4.6404
`europe_west3`	$4.6382
`europe_west4`	$3.964
`europe_west6`	$5.0432

C2D Series

c2d-standard-2 Approximations:

`us_central1`	$0.1044
`us_east1`	$0.1044
`us_east4`	$0.1176

c2d-standard-4 Approximations:

`us_central1`	$0.2088
`us_east1`	$0.2088
`us_east4`	$0.2352

c2d-standard-8 Approximations:

`us_central1`	$0.4177
`us_east1`	$0.4177
`us_east4`	$0.4704

c2d-standard-16 Approximations:

`us_central1`	$0.8353
`us_east1`	$0.8353
`us_east4`	$0.9408

c2d-standard-32 Approximations:

`us_central1`	$1.6707
`us_east1`	$1.6707
`us_east4`	$1.8815

c2d-standard-56 Approximations:

`us_central1`	$2.9237
`us_east1`	$2.9237
`us_east4`	$3.2926

c2d-standard-112 Approximations:

`us_central1`	$5.8474
`us_east1`	$5.8474
`us_east4`	$6.5853

A2 Series

a2-highgpu-1g Approximations:

europe-west4 $4.3103

a2-highgpu-2g Approximations:

europe-west4 $8.6205

a2-highgpu-4g Approximations:

europe-west4 $17.2411

a2-highgpu-8g Approximations:

europe-west4 $34.4822

a2-megagpu-16g Approximations:

europe-west4 $65.1222

G2 Series

g2-standard-4 Approximations:

europe-west4 $0.8951

g2-standard-8 Approximations:

europe-west4 $1.081

g2-standard-12 Approximations:

europe-west4 $1.2669

g2-standard-16 Approximations:

europe-west4 $1.4528

g2-standard-24 Approximations:

europe-west4 $2.5338

g2-standard-32 Approximations:

europe-west4 $2.1965

g2-standard-48 Approximations:

europe-west4 $5.0677

g2-standard-96 Approximations:

europe-west4 $10.1354

Pricing for Asia Pacific

The following tables provide the price per node hour for each machine type.

E2 Series

e2-standard-2 Approximations:

`asia-east1`	$0.0892
`asia-east2`	$0.1078
`asia-northeast1`	$0.0989
`asia-northeast3`	$0.0989
`asia-south1`	$0.0926
`asia-southeast1`	$0.0951
`australia-southeast1`	$0.1093

e2-standard-4 Approximations:

`asia-east1`	$0.1785
`asia-east2`	$0.2156
`asia-northeast1`	$0.1977
`asia-northeast3`	$0.1977
`asia-south1`	$0.1851
`asia-southeast1`	$0.1901
`australia-southeast1`	$0.2187

e2-standard-8 Approximations:

`asia-east1`	$0.3569
`asia-east2`	$0.4313
`asia-northeast1`	$0.3954
`asia-northeast3`	$0.3954
`asia-south1`	$0.3702
`asia-southeast1`	$0.3802
`australia-southeast1`	$0.4373

e2-standard-16 Approximations:

`asia-east1`	$0.7138
`asia-east2`	$0.8626
`asia-northeast1`	$0.7909
`asia-northeast3`	$0.7909
`asia-south1`	$0.7405
`asia-southeast1`	$0.7605
`australia-southeast1`	$0.8747

e2-standard-32 Approximations:

`asia-east1`	$1.4276
`asia-east2`	$1.7251
`asia-northeast1`	$1.5817
`asia-northeast3`	$1.5817
`asia-south1`	$1.4809
`asia-southeast1`	$1.5209
`australia-southeast1`	$1.7494

e2-highmem-2 Approximations:

`asia-east1`	$0.1204
`asia-east2`	$0.1454
`asia-northeast1`	$0.1333
`asia-northeast3`	$0.1333
`asia-south1`	$0.1249
`asia-southeast1`	$0.1282
`australia-southeast1`	$0.1475

e2-highmem-4 Approximations:

`asia-east1`	$0.2407
`asia-east2`	$0.2909
`asia-northeast1`	$0.2665
`asia-northeast3`	$0.2665
`asia-south1`	$0.2497
`asia-southeast1`	$0.2564
`australia-southeast1`	$0.295

e2-highmem-8 Approximations:

`asia-east1`	$0.4815
`asia-east2`	$0.5818
`asia-northeast1`	$0.533
`asia-northeast3`	$0.533
`asia-south1`	$0.4994
`asia-southeast1`	$0.5129
`australia-southeast1`	$0.59

e2-highmem-16 Approximations:

`asia-east1`	$0.963
`asia-east2`	$1.1636
`asia-northeast1`	$1.0661
`asia-northeast3`	$1.0661
`asia-south1`	$0.9989
`asia-southeast1`	$1.0258
`australia-southeast1`	$1.1799

e2-highcpu-2 Approximations:

`asia-east1`	$0.0659
`asia-east2`	$0.0796
`asia-northeast1`	$0.0731
`asia-northeast3`	$0.0731
`asia-south1`	$0.0683
`asia-southeast1`	$0.0702
`australia-southeast1`	$0.0807

e2-highcpu-4 Approximations:

`asia-east1`	$0.1317
`asia-east2`	$0.1592
`asia-northeast1`	$0.1461
`asia-northeast3`	$0.1461
`asia-south1`	$0.1367
`asia-southeast1`	$0.1404
`australia-southeast1`	$0.1614

e2-highcpu-8 Approximations:

`asia-east1`	$0.2635
`asia-east2`	$0.3184
`asia-northeast1`	$0.2922
`asia-northeast3`	$0.2922
`asia-south1`	$0.2733
`asia-southeast1`	$0.2807
`australia-southeast1`	$0.3229

e2-highcpu-16 Approximations:

`asia-east1`	$0.527
`asia-east2`	$0.6368
`asia-northeast1`	$0.5845
`asia-northeast3`	$0.5845
`asia-south1`	$0.5467
`asia-southeast1`	$0.5615
`australia-southeast1`	$0.6458

e2-highcpu-32 Approximations:

`asia-east1`	$1.0539
`asia-east2`	$1.2736
`asia-northeast1`	$1.169
`asia-northeast3`	$1.169
`asia-south1`	$1.0933
`asia-southeast1`	$1.1229
`australia-southeast1`	$1.2916

N1 Series

n1-standard-2 Approximations:

`asia-northeast1`	$0.1402
`asia-southeast1`	$0.1348
`australia-southeast1`	$0.155
Other Asia Pacific regions	$0.1265

n1-standard-4 Approximations:

`asia-northeast1`	$0.2803
`asia-southeast1`	$0.2695
`australia-southeast1`	$0.31
Other Asia Pacific regions	$0.2531

n1-standard-8 Approximations:

`asia-northeast1`	$0.5606
`asia-southeast1`	$0.5391
`australia-southeast1`	$0.6201
Other Asia Pacific regions	$0.5061

n1-standard-16 Approximations:

`asia-northeast1`	$1.1213
`asia-southeast1`	$1.0782
`australia-southeast1`	$1.2401
Other Asia Pacific regions	$1.0123

n1-standard-32 Approximations:

`asia-northeast1`	$2.2426
`asia-southeast1`	$2.1564
`australia-southeast1`	$2.4802
Other Asia Pacific regions	$2.0245

n1-highmem-2 Approximations:

`asia-northeast1`	$0.1744
`asia-southeast1`	$0.1678
`australia-southeast1`	$0.193
Other Asia Pacific regions	$0.1575

n1-highmem-4 Approximations:

`asia-northeast1`	$0.3489
`asia-southeast1`	$0.3357
`australia-southeast1`	$0.3861
Other Asia Pacific regions	$0.3151

n1-highmem-8 Approximations:

`asia-northeast1`	$0.6977
`asia-southeast1`	$0.6713
`australia-southeast1`	$0.7721
Other Asia Pacific regions	$0.6302

n1-highmem-16 Approximations:

`asia-northeast1`	$1.3955
`asia-southeast1`	$1.3426
`australia-southeast1`	$1.5443
Other Asia Pacific regions	$1.2603

n1-highcpu-2 Approximations:

`asia-northeast1`	$0.1046
`asia-southeast1`	$0.1005
`australia-southeast1`	$0.1156
Other Asia Pacific regions	$0.0944

n1-highcpu-4 Approximations:

`asia-northeast1`	$0.2093
`asia-southeast1`	$0.201
`australia-southeast1`	$0.2312
Other Asia Pacific regions	$0.1888

n1-highcpu-8 Approximations:

`asia-northeast1`	$0.4186
`asia-southeast1`	$0.4021
`australia-southeast1`	$0.4624
Other Asia Pacific regions	$0.3776

n1-highcpu-16 Approximations:

`asia-northeast1`	$0.8371
`asia-southeast1`	$0.8041
`australia-southeast1`	$0.9249
Other Asia Pacific regions	$0.7552

n1-highcpu-32 Approximations:

`asia-northeast1`	$1.6742
`asia-southeast1`	$1.6082
`australia-southeast1`	$1.8498
Other Asia Pacific regions	$1.5104

N2 Series

n2-standard-2 Approximations:

`asia_east1`	$0.1293
`asia_east2`	$0.1563
`asia_northeast1`	$0.1433
`asia_northeast3`	$0.1433
`asia_south1`	$0.1341
`asia_southeast1`	$0.1378
`asia_southeast2`	$0.1502
`australia_southeast1`	$0.1585

n2-standard-4 Approximations:

`asia_east1`	$0.2586
`asia_east2`	$0.3125
`asia_northeast1`	$0.2866
`asia_northeast3`	$0.2866
`asia_south1`	$0.2683
`asia_southeast1`	$0.2756
`asia_southeast2`	$0.3003
`australia_southeast1`	$0.3169

n2-standard-8 Approximations:

`asia_east1`	$0.5173
`asia_east2`	$0.6251
`asia_northeast1`	$0.5731
`asia_northeast3`	$0.5731
`asia_south1`	$0.5366
`asia_southeast1`	$0.5511
`asia_southeast2`	$0.6007
`australia_southeast1`	$0.6339

n2-standard-16 Approximations:

`asia_east1`	$1.0346
`asia_east2`	$1.2502
`asia_northeast1`	$1.1462
`asia_northeast3`	$1.1462
`asia_south1`	$1.0731
`asia_southeast1`	$1.1022
`asia_southeast2`	$1.2014
`australia_southeast1`	$1.2678

n2-standard-32 Approximations:

`asia_east1`	$2.0691
`asia_east2`	$2.5003
`asia_northeast1`	$2.2924
`asia_northeast3`	$2.2924
`asia_south1`	$2.1462
`asia_southeast1`	$2.2044
`asia_southeast2`	$2.4028
`australia_southeast1`	$2.5355

n2-highmem-2 Approximations:

`asia_east1`	$0.1745
`asia_east2`	$0.2108
`asia_northeast1`	$0.1931
`asia_northeast3`	$0.1931
`asia_south1`	$0.181
`asia_southeast1`	$0.1859
`asia_southeast2`	$0.2026
`australia_southeast1`	$0.2138

n2-highmem-4 Approximations:

`asia_east1`	$0.3489
`asia_east2`	$0.4216
`asia_northeast1`	$0.3863
`asia_northeast3`	$0.3863
`asia_south1`	$0.3619
`asia_southeast1`	$0.3717
`asia_southeast2`	$0.4052
`australia_southeast1`	$0.4275

n2-highmem-8 Approximations:

`asia_east1`	$0.6978
`asia_east2`	$0.8432
`asia_northeast1`	$0.7725
`asia_northeast3`	$0.7725
`asia_south1`	$0.7238
`asia_southeast1`	$0.7434
`asia_southeast2`	$0.8103
`australia_southeast1`	$0.8551

n2-highmem-16 Approximations:

`asia_east1`	$1.3956
`asia_east2`	$1.6865
`asia_northeast1`	$1.545
`asia_northeast3`	$1.545
`asia_south1`	$1.4476
`asia_southeast1`	$1.4868
`asia_southeast2`	$1.6206
`australia_southeast1`	$1.7102

n2-highcpu-2 Approximations:

`asia_east1`	$0.0955
`asia_east2`	$0.1154
`asia_northeast1`	$0.1059
`asia_northeast3`	$0.1059
`asia_south1`	$0.099
`asia_southeast1`	$0.1017
`asia_southeast2`	$0.1109
`australia_southeast1`	$0.117

n2-highcpu-4 Approximations:

`asia_east1`	$0.1909
`asia_east2`	$0.2307
`asia_northeast1`	$0.2118
`asia_northeast3`	$0.2118
`asia_south1`	$0.1981
`asia_southeast1`	$0.2034
`asia_southeast2`	$0.2217
`australia_southeast1`	$0.234

n2-highcpu-8 Approximations:

`asia_east1`	$0.3819
`asia_east2`	$0.4615
`asia_northeast1`	$0.4235
`asia_northeast3`	$0.4235
`asia_south1`	$0.3961
`asia_southeast1`	$0.4069
`asia_southeast2`	$0.4435
`australia_southeast1`	$0.468

n2-highcpu-16 Approximations:

`asia_east1`	$0.7637
`asia_east2`	$0.9229
`asia_northeast1`	$0.8471
`asia_northeast3`	$0.8471
`asia_south1`	$0.7923
`asia_southeast1`	$0.8137
`asia_southeast2`	$0.887
`australia_southeast1`	$0.936

n2-highcpu-32 Approximations:

`asia_east1`	$1.5275
`asia_east2`	$1.8458
`asia_northeast1`	$1.6942
`asia_northeast3`	$1.6942
`asia_south1`	$1.5845
`asia_southeast1`	$1.6275
`asia_southeast2`	$1.7739
`australia_southeast1`	$1.8719

N2D Series

n2d-standard-2 Approximations:

`asia_east1`	$0.1125
`asia_east2`	$0.136
`asia_northeast1`	$0.1247
`asia_south1`	$0.0641
`asia_southeast1`	$0.1199
`australia_southeast1`	$0.1379

n2d-standard-4 Approximations:

`asia_east1`	$0.225
`asia_east2`	$0.2719
`asia_northeast1`	$0.2493
`asia_south1`	$0.1283
`asia_southeast1`	$0.2397
`australia_southeast1`	$0.2757

n2d-standard-8 Approximations:

`asia_east1`	$0.45
`asia_east2`	$0.5438
`asia_northeast1`	$0.4986
`asia_south1`	$0.2565
`asia_southeast1`	$0.4795
`australia_southeast1`	$0.5515

n2d-standard-16 Approximations:

`asia_east1`	$0.9001
`asia_east2`	$1.0876
`asia_northeast1`	$0.9972
`asia_south1`	$0.513
`asia_southeast1`	$0.959
`australia_southeast1`	$1.103

n2d-standard-32 Approximations:

`asia_east1`	$1.8001
`asia_east2`	$2.1752
`asia_northeast1`	$1.9945
`asia_south1`	$1.0261
`asia_southeast1`	$1.9179
`australia_southeast1`	$2.206

n2d-highmem-2 Approximations:

`asia_east1`	$0.1518
`asia_east2`	$0.1834
`asia_northeast1`	$0.168
`asia_south1`	$0.0865
`asia_southeast1`	$0.1617
`australia_southeast1`	$0.186

n2d-highmem-4 Approximations:

`asia_east1`	$0.3035
`asia_east2`	$0.3668
`asia_northeast1`	$0.3361
`asia_south1`	$0.173
`asia_southeast1`	$0.3234
`australia_southeast1`	$0.372

n2d-highmem-8 Approximations:

`asia_east1`	$0.6071
`asia_east2`	$0.7336
`asia_northeast1`	$0.6721
`asia_south1`	$0.346
`asia_southeast1`	$0.6468
`australia_southeast1`	$0.744

n2d-highmem-16 Approximations:

`asia_east1`	$1.2142
`asia_east2`	$1.4672
`asia_northeast1`	$1.3443
`asia_south1`	$0.6921
`asia_southeast1`	$1.2936
`australia_southeast1`	$1.4879

n2d-highcpu-2 Approximations:

`asia_east1`	$0.0831
`asia_east2`	$0.1004
`asia_northeast1`	$0.0921
`asia_south1`	$0.0473
`asia_southeast1`	$0.0885
`australia_southeast1`	$0.1018

n2d-highcpu-4 Approximations:

`asia_east1`	$0.1661
`asia_east2`	$0.2007
`asia_northeast1`	$0.1842
`asia_south1`	$0.0947
`asia_southeast1`	$0.177
`australia_southeast1`	$0.2036

n2d-highcpu-8 Approximations:

`asia_east1`	$0.3322
`asia_east2`	$0.4015
`asia_northeast1`	$0.3685
`asia_south1`	$0.1894
`asia_southeast1`	$0.354
`australia_southeast1`	$0.4071

n2d-highcpu-16 Approximations:

`asia_east1`	$0.6645
`asia_east2`	$0.8029
`asia_northeast1`	$0.737
`asia_south1`	$0.3787
`asia_southeast1`	$0.708
`australia_southeast1`	$0.8143

n2d-highcpu-32 Approximations:

`asia_east1`	$1.3289
`asia_east2`	$1.6059
`asia_northeast1`	$1.4739
`asia_south1`	$0.7575
`asia_southeast1`	$1.4159
`australia_southeast1`	$1.6286

C2 Series

c2-standard-4 Approximations:

`asia_east1`	$0.278
`asia_east2`	$0.336
`asia_northeast1`	$0.308
`asia_northeast3`	$0.308
`asia_south1`	$0.2884
`asia_southeast1`	$0.2962
`australia_southeast1`	$0.3407

c2-standard-8 Approximations:

`asia_east1`	$0.5561
`asia_east2`	$0.672
`asia_northeast1`	$0.6161
`asia_northeast3`	$0.6161
`asia_south1`	$0.5768
`asia_southeast1`	$0.5924
`australia_southeast1`	$0.6814

c2-standard-16 Approximations:

`asia_east1`	$1.1122
`asia_east2`	$1.3439
`asia_northeast1`	$1.2321
`asia_northeast3`	$1.2321
`asia_south1`	$1.1536
`asia_southeast1`	$1.1849
`australia_southeast1`	$1.3629

c2-standard-30 Approximations:

`asia_east1`	$2.0853
`asia_east2`	$2.5199
`asia_northeast1`	$2.3103
`asia_northeast3`	$2.3103
`asia_south1`	$2.1631
`asia_southeast1`	$2.2217
`australia_southeast1`	$2.5553

c2-standard-60 Approximations:

`asia_east1`	$4.1706
`asia_east2`	$5.0397
`asia_northeast1`	$4.6205
`asia_northeast3`	$4.6205
`asia_south1`	$4.3262
`asia_southeast1`	$4.4433
`australia_southeast1`	$5.1107

C2D Series

c2d-standard-2 Approximations:

`asia_east1`	$0.1209
`asia_southeast1`	$0.1288

c2d-standard-4 Approximations:

`asia_east1`	$0.2418
`asia_southeast1`	$0.2576

c2d-standard-8 Approximations:

`asia_east1`	$0.4836
`asia_southeast1`	$0.5153

c2d-standard-16 Approximations:

`asia_east1`	$0.9672
`asia_southeast1`	$1.0305

c2d-standard-32 Approximations:

`asia_east1`	$1.9345
`asia_southeast1`	$2.0611

c2d-standard-56 Approximations:

`asia_east1`	$3.3853
`asia_southeast1`	$3.6069

c2d-standard-112 Approximations:

`asia_east1`	$6.7706
`asia_southeast1`	$7.2137

A2 Series

a2-highgpu-1g Approximations:

`asia-northeast1`	$4.6575
`asia-northeast3`	$4.6575
`asia-southeast1`	$4.6163

a2-highgpu-2g Approximations:

`asia-northeast1`	$9.3151
`asia-northeast3`	$9.3151
`asia-southeast1`	$9.2327

a2-highgpu-4g Approximations:

`asia-northeast1`	$18.6301
`asia-northeast3`	$18.6301
`asia-southeast1`	$18.4653

a2-highgpu-8g Approximations:

`asia-northeast1`	$37.2603
`asia-northeast3`	$37.2603
`asia-southeast1`	$36.9306

a2-megagpu-16g Approximations:

`asia-northeast1`	$70.0363
`asia-northeast3`	$70.0363
`asia-southeast1`	$69.5557

Pricing for the Middle East

N2 Series

n2-standard-2 Approximations:

me_west1 $0.1229

n2-standard-4 Approximations:

me_west1 $0.2457

n2-standard-8 Approximations:

me_west1 $0.4914

n2-standard-16 Approximations:

me_west1 $0.9828

n2-standard-32 Approximations:

me_west1 $1.9657

n2-highmem-2 Approximations:

me_west1 $0.1657

n2-highmem-4 Approximations:

me_west1 $0.3315

n2-highmem-8 Approximations:

me_west1 $0.6629

n2-highmem-16 Approximations:

me_west1 $1.3259

n2-highcpu-2 Approximations:

me_west1 $0.0907

n2-highcpu-4 Approximations:

me_west1 $0.1814

n2-highcpu-8 Approximations:

me_west1 $0.3628

n2-highcpu-16 Approximations:

me_west1 $0.7256

n2-highcpu-32 Approximations:

me_west1 $1.4511

N2D Series

n2d-standard-2 Approximations:

me_west1 $0.1069

n2d-standard-4 Approximations:

me_west1 $0.2138

n2d-standard-8 Approximations:

me_west1 $0.4275

n2d-standard-16 Approximations:

me_west1 $0.8551

n2d-standard-32 Approximations:

me_west1 $1.7101

n2d-highmem-2 Approximations:

me_west1 $0.1442

n2d-highmem-4 Approximations:

me_west1 $0.2884

n2d-highmem-8 Approximations:

me_west1 $0.5767

n2d-highmem-16 Approximations:

me_west1 $1.1535

n2d-highcpu-2 Approximations:

me_west1 $0.0789

n2d-highcpu-4 Approximations:

me_west1 $0.1578

n2d-highcpu-8 Approximations:

me_west1 $0.3156

n2d-highcpu-16 Approximations:

me_west1 $0.6312

n2d-highcpu-32 Approximations:

me_west1 $1.2625

Each machine type is charged as the following SKUs on your Google Cloud bill:

vCPU cost: measured in vCPU hours
RAM cost: measured in GB hours
GPU cost: if either built into the machine or optionally configured, measured in GPU hours

The prices for machine types are used to approximate the total hourly cost for each prediction node of a model version using that machine type.

For example, a machine type of n1-highcpu-32 includes 32 vCPUs and 32 GB of RAM. Therefore, the hourly pricing equals 32 vCPU hours + 32 GB hours.

The SKU pricing table is available by region. Each table shows the vCPU, RAM, and built-in GPU pricing for prediction machine types, which more precisely reflect the SKUs charged.

To view the SKU pricing per region, choose a region to view its pricing table:

The Americas
Europe
Asia Pacific
Middle East

SKU pricing for the Americas

E2 Series

vCPU

Location	Price per hour
Los Angeles (`us-west2`)	`$0.0301288` per vCPU hour
Las Vegas (`us-west4`)	`$0.028252` per vCPU hour
N. Virginia (`us-east4`)	`$0.028252` per vCPU hour
Montréal (`northamerica-northeast1`)	`$0.0276149` per vCPU hour
Toronto (`northamerica-northeast2`)	`$0.0276149` per vCPU hour
Sao Paulo (`southamerica-east1`)	`$0.0398176` per vCPU hour
Other Americas regions	`$0.0250826` per vCPU hour

RAM

Location	Price per hour
Los Angeles (`us-west2`)	`$0.0040376` per GB hour
Las Vegas (`us-west4</code>`)	`$0.0037846` per GB hour
N. Virginia (`us-east4`)	`$0.0037846` per GB hour
Montréal (`northamerica-northeast1`)	`$0.0037007` per GB hour
Toronto (`northamerica-northeast2`)	`$0.0037007` per GB hour
Sao Paulo (`southamerica-east1`)	`$0.005336` per GB hour
Other Americas regions	`$0.0033614` per GB hour

N1 Series

vCPU

Location	Price per hour
N. Virginia (`us-east4`)	`$0.04094575` per vCPU hour
Montréal (`northamerica-northeast1`)	`$0.0400223` per vCPU hour
Other Americas regions	`$0.03635495` per vCPU hour

RAM

Location	Price per hour
N. Virginia (`us-east4`)	`$0.00548665` per GB hour
Montréal (`northamerica-northeast1`)	`$0.0053636` per GB hour
Other Americas regions	`$0.0048783` per GB hour

N2 Series

vCPU

Location	Price per hour
Montréal (`northamerica_northeast1`)	`$0.0400223` per vCPU hour
Toronto (`northamerica_northeast2`)	`$0.0400223` per vCPU hour
São Paulo (`southamerica_east1`)	`$0.057707` per vCPU hour
Iowa (`us_central1`)	`$0.0363527` per vCPU hour
South Carolina (`us_east1`)	`$0.0363527` per vCPU hour
N. Virginia (`us_east4`)	`$0.0409457` per vCPU hour
Dallas (`us_south1`)	`$0.0428962` per vCPU hour
Oregon (`us_west1`)	`$0.0363527` per vCPU hour
Los Angeles (`us_west2`)	`$0.0436655` per vCPU hour
Salt Lake City (`us_west3`)	`$0.0436655` per vCPU hour
Las Vegas (`us_west4`)	`$0.0409434` per vCPU hour

RAM

Location	Price per hour
Montréal (`northamerica_northeast1`)	`$0.0053636` per GB hour
Toronto (`northamerica_northeast2`)	`$0.0053636` per GB hour
São Paulo (`southamerica_east1`)	`$0.0077337` per GB hour
Iowa (`us_central1`)	`$0.0048725` per GB hour
South Carolina (`us_east1`)	`$0.0048725` per GB hour
N. Virginia (`us_east4`)	`$0.0054867` per GB hour
Dallas (`us_south1`)	`$0.00575` per GB hour
Oregon (`us_west1`)	`$0.0048725` per GB hour
Los Angeles (`us_west2`)	`$0.0058523` per GB hour
Salt Lake City (`us_west3`)	`$0.0058523` per GB hour
Las Vegas (`us_west4`)	`$0.0054867` per GB hour

N2D Series

vCPU

Location	Price per hour
Montréal (`northamerica_northeast1`)	`$0.0348197` per vCPU hour
São Paulo (`southamerica_east1`)	`$0.0502055` per vCPU hour
Iowa (`us_central1`)	`$0.0316273` per vCPU hour
South Carolina (`us_east1`)	`$0.0316273` per vCPU hour
N. Virginia (`us_east4`)	`$0.0356224` per vCPU hour
Oregon (`us_west1`)	`$0.0316273` per vCPU hour
Los Angeles (`us_west2`)	`$0.0379891` per vCPU hour
Las Vegas (`us_west4`)	`$0.0356224` per vCPU hour

RAM

Location	Price per hour
Montréal (`northamerica_northeast1`)	`$0.0046667` per GB hour
São Paulo (`southamerica_east1`)	`$0.0067287` per GB hour
Iowa (`us_central1`)	`$0.0042389` per GB hour
South Carolina (`us_east1`)	`$0.0042389` per GB hour
N. Virginia (`us_east4`)	`$0.0047736` per GB hour
Oregon (`us_west1`)	`$0.0042389` per GB hour
Los Angeles (`us_west2`)	`$0.005091` per GB hour
Las Vegas (`us_west4`)	`$0.0047736` per GB hour

C2 Series

vCPU

Location	Price per hour
Montréal (`northamerica_northeast1`)	`$0.04301` per vCPU hour
São Paulo (`southamerica_east1`)	`$0.0620356` per vCPU hour
Iowa (`us_central1`)	`$0.039077` per vCPU hour
South Carolina (`us_east1`)	`$0.039077` per vCPU hour
N. Virginia (`us_east4`)	`$0.0440105` per vCPU hour
Oregon (`us_west1`)	`$0.039077` per vCPU hour
Los Angeles (`us_west2`)	`$0.046943` per vCPU hour
Salt Lake City (`us_west3`)	`$0.04692` per vCPU hour
Las Vegas (`us_west4`)	`$0.0440105` per vCPU hour

RAM

Location	Price per hour
Montréal (`northamerica_northeast1`)	`$0.00575` per GB hour
São Paulo (`southamerica_east1`)	`$0.0083133` per GB hour
Iowa (`us_central1`)	`$0.0052325` per GB hour
South Carolina (`us_east1`)	`$0.0052325` per GB hour
N. Virginia (`us_east4`)	`$0.005888` per GB hour
Oregon (`us_west1`)	`$0.0052325` per GB hour
Los Angeles (`us_west2`)	`$0.0062905` per GB hour
Salt Lake City (`us_west3`)	`$0.006325` per GB hour
Las Vegas (`us_west4`)	`$0.005888` per GB hour

C2D Series

vCPU

Location	Price per hour
Iowa (`us_central1`)	`$0.0339974` per vCPU hour
South Carolina (`us_east1`)	`$0.0339974` per vCPU hour
N. Virginia (`us_east4`)	`$0.0382904` per vCPU hour

RAM

Location	Price per hour
Iowa (`us_central1`)	`$0.0045528` per GB hour
South Carolina (`us_east1`)	`$0.0045528` per GB hour
N. Virginia (`us_east4`)	`$0.0051267` per GB hour

A2 Series

vCPU

Location	Price per hour
Las Vegas (`us-west4`)	`$0.0409457` per vCPU hour
Other Americas regions	`$0.0363527` per vCPU hour

RAM

Location	Price per hour
Las Vegas (`us-west4`)	`$0.0054867` per GB hour
Other Americas regions	`$0.0048725` per GB hour

GPU

Location	Price per hour
Las Vegas (`us-west4`)	`$3.5673` per GPU hour
Other Americas regions	`$3.3741` per GPU hour

G2 Series

vCPU

Location	Price per hour
Iowa (`us-central1`)	`$0.02874` per vCPU hour

RAM

Location	Price per hour
Iowa (`us-central1`)	`$0.00337` per GB hour

GPU

Location	Price per hour
Iowa (`us-central1`)	`$0.64405` per GPU hour

SKU pricing for Europe

E2 Series

vCPU

Location	Price per hour
Belgium (`europe-west1`)	`$0.0275919` per vCPU hour
London (`europe-west2`)	`$0.0323184` per vCPU hour
Frankfurt (`europe-west3`)	`$0.0323184` per vCPU hour
Netherlands (`europe-west4`)	`$0.0276149` per vCPU hour
Zurich (`europe-west6`)	`$0.0350968` per vCPU hour
Paris (`europe-west9`)	`$0.0351164` per vCPU hour

RAM

Location	Price per hour
Belgium (`europe-west1`)	`$0.0036984` per GB hour
London (`europe-west2`)	`$0.0043309` per GB hour
Frankfurt (`europe-west3`)	`$0.0043309` per GB hour
Netherlands (`europe-west4`)	`$0.0037007` per GB hour
Zurich (`europe-west6`)	`$0.0047035` per GB hour
Paris (`europe-west9`)	`$0.0047069` per GB hour

N1 Series

vCPU

Location	Price per hour
London (`europe-west2`)	`$0.0468395` per vCPU hour
Other Europe regions	`$0.0421268` per vCPU hour

RAM

Location	Price per hour
London (`europe-west2`)	`$0.0062767` per GB hour
Other Europe regions	`$0.0056373` per GB hour

N2 Series

vCPU

Location	Price per hour
Warsaw (`europe_central2`)	`$0.0468395` per vCPU hour
Belgium (`europe_west1`)	`$0.0399889` per vCPU hour
London (`europe_west2`)	`$0.0468395` per vCPU hour
Frankfurt (`europe_west3`)	`$0.0468395` per vCPU hour
Netherlands (`europe_west4`)	`$0.0399879` per vCPU hour
Zurich (`europe_west6`)	`$0.050899` per vCPU hour
Paris (`europe_west9`)	`$0.0421693` per vCPU hour

RAM

Location	Price per hour
Warsaw (`europe_central2`)	`$0.0062767` per GB hour
Belgium (`europe_west1`)	`$0.0053602` per GB hour
London (`europe_west2`)	`$0.0062767` per GB hour
Frankfurt (`europe_west3`)	`$0.0062767` per GB hour
Netherlands (`europe_west4`)	`$0.0053598` per GB hour
Zurich (`europe_west6`)	`$0.0068195` per GB hour
Paris (`europe_west9`)	`$0.0056522` per GB hour

N2D Series

vCPU

Location	Price per hour
Belgium (`europe_west1`)	`$0.0347909` per vCPU hour
London (`europe_west2`)	`$0.0407502` per vCPU hour
Frankfurt (`europe_west3`)	`$0.0407502` per vCPU hour
Netherlands (`europe_west4`)	`$0.0348197` per vCPU hour
Paris (`europe_west9`)	`$0.0366873` per vCPU hour

RAM

Location	Price per hour
Belgium (`europe_west1`)	`$0.0046632` per GB hour
London (`europe_west2`)	`$0.0054602` per GB hour
Frankfurt (`europe_west3`)	`$0.0054602` per GB hour
Netherlands (`europe_west4`)	`$0.0046667` per GB hour
Paris (`europe_west9`)	`$0.0049174` per GB hour

C2 Series

vCPU

Location	Price per hour
Belgium (`europe_west1`)	`$0.042987` per vCPU hour
London (`europe_west2`)	`$0.0503527` per vCPU hour
Frankfurt (`europe_west3`)	`$0.050347` per vCPU hour
Netherlands (`europe_west4`)	`$0.0430215` per vCPU hour
Zurich (`europe_west6`)	`$0.0547055` per vCPU hour

RAM

Location	Price per hour
Belgium (`europe_west1`)	`$0.0057615` per GB hour
London (`europe_west2`)	`$0.006747` per GB hour
Frankfurt (`europe_west3`)	`$0.006739` per GB hour
Netherlands (`europe_west4`)	`$0.0057615` per GB hour
Zurich (`europe_west6`)	`$0.007337` per GB hour

C2D Series

vCPU

Location	Price per hour
London (`europe_west2`)	`$0.0438012` per vCPU hour
Netherlands (`europe_west4`)	`$0.0374336` per vCPU hour

RAM

Location	Price per hour
London (`europe_west2`)	`$0.005865` per GB hour
Netherlands (`europe_west4`)	`$0.0050128` per GB hour

A2 Series

vCPU

Location	Price per hour
Netherlands (`europe-west4`)	`$0.0400223` per vCPU hour

RAM

Location	Price per hour
Netherlands (`europe-west4`)	`$0.0053636` per GB hour

GPU

Location	Price per hour
Netherlands (`europe-west4`)	`$3.3741` per GPU hour

G2 Series

vCPU

Location	Price per hour
Netherlands (`europe-west4`)	`$0.03164` per vCPU hour

RAM

Location	Price per hour
Netherlands (`europe-west4`)	`$0.00371` per GB hour

GPU

Location	Price per hour
Netherlands (`europe-west4`)	`$0.70916` per GPU hour

SKU pricing for Asia Pacific

E2 Series

E2 prediction machine type SKUs

vCPU

Location	Price per hour
Taiwan (`asia-east1`)	`$0.0290432` per vCPU hour
Hong Kong (`asia-east2`)	`$0.0350968` per vCPU hour
Tokyo (`asia-northeast1`)	`$0.0322299` per vCPU hour
Seoul (`asia-northeast3`)	`$0.0322299` per vCPU hour
Mumbai (`asia-south1`)	`$0.0301288` per vCPU hour
Singapore (`asia-southeast1`)	`$0.0309453` per vCPU hour
Sydney (`australia-southeast1`)	`$0.0355925` per vCPU hour

RAM

Location	Price per hour
Taiwan (`asia-east1`)	`$0.0038927` per GB hour
Hong Kong (`asia-east2`)	`$0.0047035` per GB hour
Tokyo (`asia-northeast1`)	`$0.0042999` per GB hour
Seoul (`asia-northeast3`)	`$0.0042999` per GB hour
Mumbai (`asia-south1`)	`$0.0040376` per GB hour
Singapore (`asia-southeast1`)	`$0.0041458` per GB hour
Sydney (`australia-southeast1`)	`$0.004769` per GB hour

N1 Series

N1 Prediction machine type SKUs

vCPU

Location	Price per hour
Tokyo (`asia-northeast1`)	`$0.0467107` per vCPU hour
Singapore (`asia-southeast1`)	`$0.04484885` per vCPU hour
Sydney (`australia-southeast1`)	`$0.0515844` per vCPU hour
Other Asia Pacific regions	`$0.0421268` per vCPU hour

RAM

Location	Price per hour
Tokyo (`asia-northeast1`)	`$0.00623185` per GB hour
Singapore (`asia-southeast1`)	`$0.0060099` per GB hour
Sydney (`australia-southeast1`)	`$0.00691265` per GB hour
Other Asia Pacific regions	`$0.0056373` per GB hour

N2 Series

vCPU

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0420923` per vCPU hour
Hong Kong (`asia_east2`)	`$0.0508656` per vCPU hour
Tokyo (`asia_northeast1`)	`$0.0467107` per vCPU hour
Seoul (`asia_northeast3`)	`$0.0467107` per vCPU hour
Mumbai (`asia_south1`)	`$0.0436655` per vCPU hour
Singapore (`asia_southeast1`)	`$0.0448488` per vCPU hour
Jakarta (`asia_southeast2`)	`$0.0488853` per vCPU hour
Sydney (`australia_southeast1`)	`$0.0515844` per vCPU hour

RAM

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0056419` per GB hour
Hong Kong (`asia_east2`)	`$0.0068172` per GB hour
Tokyo (`asia_northeast1`)	`$0.0062318` per GB hour
Seoul (`asia_northeast3`)	`$0.0062318` per GB hour
Mumbai (`asia_south1`)	`$0.0058512` per GB hour
Singapore (`asia_southeast1`)	`$0.0060099` per GB hour
Jakarta (`asia_southeast2`)	`$0.0065504` per GB hour
Sydney (`australia_southeast1`)	`$0.0069126` per GB hour

N2D Series

vCPU

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0366206` per vCPU hour
Hong Kong (`asia_east2`)	`$0.0442531` per vCPU hour
Tokyo (`asia_northeast1`)	`$0.0406387` per vCPU hour
Mumbai (`asia_south1`)	`$0.0208725` per vCPU hour
Singapore (`asia_southeast1`)	`$0.0390184` per vCPU hour
Sydney (`australia_southeast1`)	`$0.0448787` per vCPU hour

RAM

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0049082` per GB hour
Hong Kong (`asia_east2`)	`$0.0059305` per GB hour
Tokyo (`asia_northeast1`)	`$0.0054222` per GB hour
Mumbai (`asia_south1`)	`$0.0027979` per GB hour
Singapore (`asia_southeast1`)	`$0.005229` per GB hour
Sydney (`australia_southeast1`)	`$0.0060145` per GB hour

C2 Series

vCPU

Location	Price per hour
Taiwan (`asia_east1`)	`$0.045249` per vCPU hour
Hong Kong (`asia_east2`)	`$0.0546802` per vCPU hour
Tokyo (`asia_northeast1`)	`$0.0502136` per vCPU hour
Seoul (`asia_northeast3`)	`$0.0502136` per vCPU hour
Mumbai (`asia_south1`)	`$0.0469407` per vCPU hour
Singapore (`asia_southeast1`)	`$0.0482126` per vCPU hour
Sydney (`australia_southeast1`)	`$0.055453` per vCPU hour

RAM

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0060651` per GB hour
Hong Kong (`asia_east2`)	`$0.0073289` per GB hour
Tokyo (`asia_northeast1`)	`$0.0066987` per GB hour
Seoul (`asia_northeast3`)	`$0.0066987` per GB hour
Mumbai (`asia_south1`)	`$0.0062905` per GB hour
Singapore (`asia_southeast1`)	`$0.0064607` per GB hour
Sydney (`australia_southeast1`)	`$0.0074313` per GB hour

C2D Series

vCPU

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0393656` per vCPU hour
Singapore (`asia_southeast1`)	`$0.0419417` per vCPU hour

RAM

Location	Price per hour
Taiwan (`asia_east1`)	`$0.0052716` per GB hour
Singapore (`asia_southeast1`)	`$0.0056166` per GB hour

A2 Series

A2 Prediction machine type SKUs

vCPU

Location	Price per hour
Tokyo (`asia-northeast1`)	`$0.0467107` per vCPU hour
Seoul (`asia-northeast3`)	`$0.0467107` per vCPU hour
Singapore (`asia-southeast1`)	`$0.0448488` per vCPU hour

RAM

Location	Price per hour
Tokyo (`asia-northeast1`)	`$0.00623185` per GB hour
Seoul (`asia-northeast3`)	`$0.0062318` per GB hour
Singapore (`asia-southeast1`)	`$0.0060099` per GB hour

GPU

Location	Price per hour
Tokyo (`asia-northeast1`)	`$3.5673` per GPU hour
Seoul (`asia-northeast3`)	`$3.5673` per GPU hour
Singapore (`asia-southeast1`)	`$3.5673` per GPU hour

SKU pricing for Middle East

N2 Series

vCPU

Location	Price per hour
Tel Aviv (`me_west1`)	`$0.0399879` per vCPU hour

RAM

Location	Price per hour
Tel Aviv (`me_west1`)	`$0.0053598` per GB hour

N2D Series

vCPU

Location	Price per hour
Tel Aviv (`me_west1`)	`$0.03479` per vCPU hour

RAM

Location	Price per hour
Tel Aviv (`me_west1`)	`$0.0046628` per GB hour

Some machine types allow you to add optional GPU accelerators for prediction. Optional GPUs incur an additional charge, separate from those described in the previous table. View each pricing table, which describes the pricing for each type of optional GPU.

The Americas

Accelerators - price per hour

`NVIDIA_TESLA_K80`
Iowa (`us-central1`)	$0.5175
South Carolina (`us-east1`)	$0.5175

`NVIDIA_TESLA_P4`
Iowa (`us-central1`)	$0.6900
N. Virginia (`us-east4`)	$0.6900
Montréal (`northamerica-northeast1`)	$0.7475

`NVIDIA_TESLA_P100`
Oregon (`us-west1`)	$1.6790
Iowa (`us-central1`)	$1.6790
South Carolina (`us-east1`)	$1.6790

`NVIDIA_TESLA_T4`
Oregon (`us-west1`)	$0.4025
Iowa (`us-central1`)	$0.4025
South Carolina (`us-east1`)	$0.4025

`NVIDIA_TESLA_V100`
Oregon (`us-west1`)	$2.8520
Iowa (`us-central1`)	$2.8520

Europe

Accelerators - price per hour

`NVIDIA_TESLA_K80`
Belgium (`europe-west1`)	$0.5635

`NVIDIA_TESLA_P4`
Netherlands (`europe-west4`)	$0.7475

`NVIDIA_TESLA_P100`
Belgium (`europe-west1`)	$1.8400

`NVIDIA_TESLA_T4`
London (`europe-west2`)	$0.4715
Netherlands (`europe-west4`)	$0.4370

`NVIDIA_TESLA_V100`
Netherlands (`europe-west4`)	$2.9325

Asia Pacific

Accelerators - price per hour

`NVIDIA_TESLA_K80`
Taiwan (`asia-east1`)	$0.5635

`NVIDIA_TESLA_P4`
Singapore (`asia-southeast1`)	$0.7475
Sydney (`australia-southeast1`)	$0.7475

`NVIDIA_TESLA_P100`
Taiwan (`asia-east1`)	$1.8400

`NVIDIA_TESLA_T4`
Tokyo (`asia-northeast1`)	$0.4255
Singapore (`asia-southeast1`)	$0.4255
Seoul (`asia-northeast3`)	$0.4485

`NVIDIA_TESLA_V100`
Not available

Pricing is per GPU. If you use multiple GPUs per prediction node (or if your version scales to use multiple nodes),the costs scale accordingly.

AI Platform Prediction serves predictions from your model by running a number of virtual machines ("nodes"). By default, Vertex AI automatically scales the number of nodes running at any time. For online prediction, the number of nodes scales to meet demand. Each node can respond to multiple prediction requests. For batch prediction, the number of nodes scales to reduce the total time it takes to run a job. You can customize how prediction nodes scale.

You are charged for the time that each node runs for your model, including:

When the node is processing a batch prediction job.
When the node is processing an online prediction request.
When the node is in a ready state for serving online predictions.

The cost of one node running for one hour is a node hour. The table of prediction prices describes the price of a node hour, which varies across regions and between online prediction and batch prediction.

You can consume node hours in fractional increments. For example, one node running for 30 minutes costs 0.5 node hours.

Cost calculations for Compute Engine (N1) machine types

The running time of a node is billed in 30-second increments. This means that every 30 seconds, your project is billed for 30 seconds worth of whatever vCPU, RAM, and GPU resources that your node is using at that moment.

More about automatic scaling of prediction nodes

Online prediction

Batch prediction

The priority of the scaling is to reduce the latency of individual requests. The service keeps your model in a ready state for a few idle minutes after servicing a request.

The priority of the scaling is to reduce the total elapsed time of the job.

Scaling affects your total charges each month: the more numerous and frequent your requests, the more nodes will be used.

Scaling should have little effect on the price of your job, though there is some overhead involved in bringing up a new node.

You can choose to let the service scale in response to traffic (automatic scaling) or you can specify a number of nodes to run constantly to avoid latency (manual scaling).

If you choose automatic scaling, the number of nodes scales automatically. For AI Platform Prediction legacy (MLS1) machine type deployments, the number of nodes can scale down to zero for no-traffic durations. Vertex AI deployments and other types of AI Platform Prediction deployments cannot scale down to zero nodes.
If you choose manual scaling, you specify a number of nodes to keep running all the time. You are charged for all of the time that these nodes are running, starting at the time of deployment and persisting until you delete the model version.

You can affect scaling by setting a maximum number of nodes to use for a batch prediction job, and by setting the number of nodes to keep running for a model when you deploy it.

Batch prediction jobs are charged after job completion

Batch prediction jobs are charged after job completion, not incrementally during the job. Any Cloud Billing budget alerts that you have configured aren't triggered while a job is running. Before starting a large job, consider running some cost benchmark jobs with small input data first.

Example of a prediction calculation

A real-estate company in an Americas region runs a weekly prediction of housing values in areas it serves. In one month, it runs predictions for four weeks in batches of 3920, 4277, 3849, and 3961. Jobs are limited to one node and each instance takes an average of 0.72 seconds of processing.

First calculate the length of time that each job ran:

3920 instances * (0.72 seconds / 1 instance) * (1 minute / 60 seconds) = 47.04 minutes
4277 instances * (0.72 seconds / 1 instance) * (1 minute / 60 seconds) = 51.324 minutes
3849 instances * (0.72 seconds / 1 instance) * (1 minute / 60 seconds) = 46.188 minutes
3961 instances * (0.72 seconds / 1 instance) * (1 minute / 60 seconds) = 47.532 minutes

Each job ran for more than ten minutes, so it is charged for each minute of processing:

($0.0909886 / 1 node hour) * (1 hour / 60 minutes) * 48 minutes * 1 node = $0.0632964
($0.0909886 / 1 node hour) * (1 hour / 60 minutes) * 52 minutes * 1 node = $0.0685711
($0.0909886 / 1 node hour) * (1 hour / 60 minutes) * 47 minutes * 1 node = $0.061977725
($0.0909886 / 1 node hour) * (1 hour / 60 minutes) * 48 minutes * 1 node = $0.0632964

The total charge for the month is $0.26.

This example assumed jobs ran on a single node and took a consistent amount of time per input instance. In real usage, make sure to account for multiple nodes and use the actual amount of time each node spends running for your calculations.

Charges for Vertex Explainable AI

Vertex Explainable AI comes at no extra charge to prediction prices. However, explanations take longer to process than normal predictions, so heavy usage of Vertex Explainable AI along with auto-scaling could result in more nodes being started, which would increase prediction charges.

Vertex AI Neural Architecture Search

The following tables summarize the pricing in each region where Neural Architecture Search is available.

Prices

The following tables provide the price per hour of various configurations.

You can choose a predefined scale tier or a custom configuration of selected machine types. If you choose a custom configuration, sum the costs of the virtual machines you use.

Accelerator-enabled legacy machine types include the cost of the accelerators in their pricing. If you use Compute Engine machine types and attach accelerators, the cost of the accelerators is separate. To calculate this cost, multiply the prices in the following table of accelerators by the number of each type of accelerator you use.

Machine types

Americas

Europe

Asia Pacific

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Prices for a2-highgpu instances include the charges for the attached NVIDIA_TESLA_A100 Accelerators.

Accelerators

Americas

Europe

Asia Pacific

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Disks

Americas

Europe

Asia Pacific

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Notes:

All use is subject to the Neural Architecture Search quota policy.

You are required to store your data and program files in Cloud Storage buckets during the Neural Architecture Search lifecycle. See more about Cloud Storage usage.

For volume-based discounts, contact the Sales team.

The disk price is only charged when you configure the disk size of each VM to be larger than 100 GB. There is no charge for the first 100 GB (the default disk size) of disk for each VM. For example, if you configure each VM to have 105 GB of disk, then you are charged for 5 GB of disk for each VM.

Required use of Cloud Storage

In addition to the costs described in this document, you are required to store data and program files in Cloud Storage buckets during the Neural Architecture Search lifecycle. This storage is subject to the Cloud Storage pricing policy.

Required use of Cloud Storage includes:

Staging your training application package.
Storing your training input data.

Note: You can use another Google Cloud service to store your input data, such as BigQuery, which has its own associated pricing.
Storing the output of your jobs. Neural Architecture Search doesn't require long-term storage of these items. You can remove the files as soon as the operation is complete.

Free operations for managing your resources

The resource management operations provided by Neural Architecture Search are available free of charge. The Neural Architecture Search quota policy does limit some of these operations.

Resource	Free operations
jobs	get, list, cancel
operations	get, list, cancel, delete

Vertex AI Pipelines

Vertex AI Pipelines charges a run execution fee of $0.03 per Pipeline Run. You are not charged the execution fee during the Preview release. You also pay for Google Cloud resources you use with Vertex AI Pipelines, such as Compute Engine resources consumed by pipeline components (charged at the same rate as for Vertex AI training). Finally, you are responsible for the cost of any services (such as Dataflow) called by your pipeline.

Vertex AI Feature Store

Prices for Vertex AI Feature Store are based on the amount of feature data in online and offline storage as well as the availability of online serving. A node hour represents the time a virtual machine spends serving feature data or waiting in a ready state to handle feature data requests.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

When you enable feature value monitoring, billing includes applicable charges above in addition to applicable charges that follow:

$3.50 per GB for all data analyzed. With snapshot analysis enabled, snapshots taken for data in Vertex AI Feature Store are included. With import feature analysis enabled, batches of ingested data are included.
Additional charges for other Vertex AI Feature Store operations used with feature value monitoring include the following:
- The snapshot analysis feature periodically takes a snapshot of the feature values based on your configuration for the monitoring interval.
- The charge for a snapshot export is the same as a regular batch export operation.

Snapshot Analysis Example

A data scientist enables feature value monitoring for their Vertex AI Feature Store and turns on monitoring for a daily snapshot analysis. A pipeline runs daily for the entity types monitoring. The pipeline scans 2GB of data in Vertex AI Feature Store and exports a snapshot containing 0.1GB of data. The total charge for one day's analysis is:

(0.1 GB * $3.50) + (2 GB * $0.005) = $0.36

Ingestion Analysis Example

A data scientist enables feature value monitoring for their Vertex AI Feature Store and turns on monitoring for ingestion operations. An ingestion operation imports 1GB of data into Vertex AI Feature Store. The total charge for feature value monitoring is:

(1 GB * $3.50) = $3.50

Vertex ML Metadata

Metadata storage is measured in binary gigabytes (GiB), where 1 GiB is 1,073,741,824 bytes. This unit of measurement is also known as a gibibyte.

Vertex ML Metadata charges $10 per gibibyte (GiB) per month for metadata storage. Prices are pro-rated per megabyte (MB). For example, if you store 10 MB of metadata, you are charged $0.10 per month for that 10 MB of metadata.

Prices are the same in all regions where Vertex ML Metadata is supported.

Vertex AI TensorBoard

To use Vertex AI TensorBoard, request that the IAM administrator of the project assign you to the role "Vertex AI TensorBoard Web App User". The Vertex AI Administrator role also has access.

Vertex AI TensorBoard charges a monthly fee of $300 per unique active user even if they only use Vertex AI TensorBoard for one day in a given month. Active users are measured through the Vertex AI TensorBoard UI. You also pay for Google Cloud resources you use with Vertex AI TensorBoard, such as TensorBoard logs stored in Cloud Storage.

Beginning in August 2023, Vertex AI TensorBoard pricing will change from a per-user monthly license of $300/month