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 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	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

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

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

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

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 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.

Vertex AI Vizier

Vertex AI Vizier is a black-box optimization service inside Vertex AI. The Vertex AI Vizier pricing model consists of the following:

There is no charge for trials that use RANDOM_SEARCH and GRID_SEARCH. Learn more about the search algorithms.
The first 100 Vertex AI Vizier trials per calendar month are available at no charge (trials using RANDOM_SEARCH and GRID_SEARCH do not count against this total).
After 100 Vertex AI Vizier trials, subsequent trials during the same calendar month are charged at $1 per trial (trials that use RANDOM_SEARCH or GRID_SEARCH incur no charges).

Vertex AI Matching Engine

Pricing for Vertex AI Matching Engine Approximate Nearest Neighbor service consists of:

Per node hour pricing for each VM used to host a deployed index.
A cost for building new indexes, updating existing indexes, and using streaming index updates.

Data processed during building and updating indexes 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 AI Matching Engine charges $3.00 per gibibyte (GiB) of data processed in all regions. Vertex AI Matching Engine charges $0.45/GB ingested for Streaming Update inserts.

The following tables summarize the pricing of an index serving in each region where the matching engine is available. The price corresponds to the machine type, by region, and is charged per node hour.

The Americas

Region	e2-standard-2	e2-standard-16	e2-highmem-16	n2d-standard-32	n1-standard-16	n1-standard-32
us_central1	0.094	0.75	1.012	1.893	1.064	2.128
us_east1	0.094	0.75	1.012	1.893	1.064	2.128
us_east4	0.10	0.845	1.14	2.132	1.198	2.397
us_west1	0.094	0.75	1.012	1.893	1.064	2.128
us_west4	0.106	0.845	1.14	2.132	1.198	2.397
us_south1	0.111	0.886	1.195	N/A	N/A	N/A
southamerica_east1	0.149	1.191	1.607	3.004	1.69	3.38

Europe

Region	e2-standard-2	e2-standard-16	e2-highmem-16	n2d-standard-32	n1-standard-16	n1-standard-32
europe_west1	0.103	0.826	1.114	2.082	1.171	2.343
europe_west9	0.131	1.051	1.417	2.195	1.05	2.1

Asia Pacific

Region	e2-standard-2	e2-standard-16	e2-highmem-16	n2d-standard-32	n1-standard-16	n1-standard-32
asia_south1	0.113	0.901	1.216	1.249	1.278	2.556
asia_southeast1	0.116	0.926	1.249	2.335	1.313	2.625
asia_northeast3	0.12	0.963	1.298	N/A	1.367	2.733
australia_southeast1	0.133	1.065	1.436	2.686	1.51	3.02

Matching engine pricing examples

Vertex AI Matching Engine pricing is determined by the size of your data, the amount of queries per second (QPS) you want to run, and the number of nodes you use. To get your estimated serving cost, you need to calculate your total data size. Your data size is the number of your embeddings/vectors* the number of dimensions you have* 4 bytes per dimension. After you have the size of your data you can calculate the serving cost and the building cost. The serving cost plus the building cost equals your monthly total cost.

Serving cost: # replicas/shard * # shards (~data size/shard size) * hourly cost * 730 hours
Building cost: data size(in GB) * $3/GB * # of updates/month

The monthly index building cost is the size of the data * 3.00 per gigabyte. The update frequency does not affect serving cost, just the building cost. If you use Streaming Index Updates, your index is rebuilt when streamed data reaches 1GB, or after 3 days, whichever comes first. The full index rebuilding job for Streaming Updates is charged at the batch index building price of $3/GB.

Number of embeddings/vectors	Number of dimensions	Queries per second (QPS)	Machine Type	Nodes	Estimated monthly serving cost
2 million	128	100	e2-standard-2	1	$68
20 million	256	1,000	e2-standard-16	1	$547
20 million	256	3,000	e2-standard-16	3	$1,642
100 million	256	500	e2-highmem-16	2	$1,477
1 billion	100	500	e2-highmem-16	8	$5,910

All examples are based on machine types in us-central1. The cost you incur will vary with recall rate and latency requirements. The estimated monthly serving cost is directly related to the number of nodes used in the console. To learn more about configuration parameters that affect cost, see Configuration parameters which affect recall and latency.

If you have high queries per second (QPS), batching these queries can reduce total costs up to 30%-40%.

Vertex AI Model Registry

The Vertex AI Model Registry is a central repository which tracks and lists your models and model versions. You can import models into Vertex AI and they appear in the Vertex AI Model Registry. There is no cost associated with having your models in the Vertex AI Model Registry. Cost is only incurred when you deploy the model to an endpoint or perform a batch prediction on the model. This cost is determined by the type of model you are deploying.

To learn more about pricing for deploying custom models from the Vertex AI Model Registry, see Custom-trained models. To learn more about pricing for deploying AutoML models, see Pricing for AutoML models.

Vertex AI Model Monitoring

Vertex AI enables you to monitor the continued effectiveness of your model after you deploy it to production. For more information, see Introduction to Vertex AI Model Monitoring.

When you use Vertex AI Model Monitoring, you are billed for the following:

$3.50 per GB for all data analyzed, including the training data provided and prediction data logged in a BigQuery table.
Charges for other Google Cloud products that you use with Model Monitoring, such as BigQuery storage or Batch Explain when attribution monitoring is enabled.

Vertex AI Model Monitoring is supported in the following regions: us-central1, europe-west4, asia-east1, and asia-southeast1. Prices are the same for all regions.

Data sizes are measured after they are converted to TfRecord format.

Training datasets incur a one-time charge when you set up a Vertex AI Model Monitoring job.

Prediction Datasets consist of logs collected from the Online Prediction service. As prediction requests arrive during different time windows, the data for each time window is collected and the sum of the data analyzed for each prediction window is used to calculate the charge.

Example: A data scientist runs model monitoring on the prediction traffic belonging to their model.

The model is trained from a BigQuery dataset. The data size after converting to TfRecord is 1.5GB.
Prediction data logged between 1:00 - 2:00 p.m. is 0.1 GB, between 3:00 - 4:00 p.m. is 0.2 GB.
The total price for setting up the model monitoring job is:

(1.5 GB * $3.50) + ((0.1 GB + 0.2 GB) * $3.50) = $6.30

Vertex AI Workbench

Pricing is composed of the compute and storage resources that you use, management fees for your Vertex AI Workbench instances, and any additional Google Cloud resources that you use. See the following sections for more details.

Compute and storage resources

Compute and storage resources are charged at the same rate you currently pay for Compute Engine and Cloud Storage.

Management fees

There are Vertex AI Workbench management fees in addition to your infrastructure usage, captured in the tables below.

Select either managed notebooks or user-managed notebooks for pricing information.

Managed notebooks

SKU	Management fee per hour
vCPU	$0.05 per vCPU
T4, K80, and P4 (Standard GPU)	$0.35 per GPU
P100, V100, and A100 GPU (Premium GPU)	$2.48 per GPU

User-managed notebooks

SKU	Management fee per hour
vCPU	$0.005 per vCPU
T4, K80, and P4 (Standard GPU)	$0.035 per GPU
P100, V100, and A100 GPU (Premium GPU)	$0.25 per GPU

Additional Google Cloud resources

In addition to the costs mentioned previously, you also pay for any Google Cloud resources that you use. For example:

Data analysis services: You incur BigQuery costs when you issue SQL queries within a notebook (see BigQuery pricing).
Customer-managed encryption keys: You incur costs when you use customer-managed encryption keys. Each time your managed notebooks or user-managed notebooks instance uses a Cloud Key Management Service key, that operation is billed at the rate of Cloud KMS key operations (see Cloud Key Management Service pricing).

Deep Learning Containers, Deep Learning VM, and AI Platform Pipelines

For Deep Learning Containers, Deep Learning VM Images, and AI Platform Pipelines, pricing is calculated based on the compute and storage resources that you use. These resources are charged at the same rate you currently pay for Compute Engine and Cloud Storage.

In addition to the compute and storage costs, you also pay for any Google Cloud resources that you use. For example:

Data analysis services: You incur BigQuery costs when you issue SQL queries within a notebook (see BigQuery pricing).
Customer-managed encryption keys: You incur costs when you use customer-managed encryption keys. Each time your managed notebooks or user-managed notebooks instance uses a Cloud Key Management Service key, that operation is billed at the rate of Cloud KMS key operations (see Cloud Key Management Service pricing).

Data labeling

Vertex AI enables you to request human labeling for a collection of data that you plan to use to train a custom machine learning model. Prices for the service are computed based on the type of labeling task.

For regular labeling tasks, the prices are determined by the number of annotation units.
- For an image classification task, units are determined the number of images and the number of human labelers. For example, an image with 3 human labelers counts for 1 * 3 = 3 units. The price for single-label and multi-label classification are the same.
- For an image bounding box task, units are determined by the number of bounding boxes identified in the images and the number of human labelers. For example, if an image with 2 bounding boxes and 3 human labelers counts for 2 * 3 = 6 units. Images without bounding boxes will not be charged.
- For an image segmentation/rotated box/polyline/polygon task, units are determined in the same way as a image bounding box task.
- For a video classification task, units are determined by the video length (every 5 seconds is a price unit) and the number of human labelers. For example, a 25 seconds video with 3 human labelers counts for 25 / 5 * 3 = 15 units. The price for single-label and multi-label classification are the same.
- For a video object tracking task, unit are determined by the number of objects identified in the video and the number of human labelers. For example, for a video with 2 objects and 3 human labelers, it counts for 2 * 3 = 6 units. Video without objects will not be charged.
- For a video action recognition task, units are determined in the same way as a video object tracking task.
- For a text classification task, units are determined by text length (every 50 words is a price unit) and the number of human labelers. For example, one piece of text with 100 words and 3 human labelers counts for 100 / 50 * 3 = 6 units. The price for single-label and multi-label classification is the same.
- For a text sentiment task, units are determined in the same way as a text classification task.
- For a text entity extraction task, units are determined by text length (every 50 words is a price unit), the number of entities identified, and the number of human labelers. For example, a piece of text with 100 words, 2 entities identified, and 3 human labelers counts for 100 / 50 * 2 * 3 = 12 units. Text without entities will not be charged.
For image/video/text classification and text sentiment tasks, human labelers may lose track of classes if the label set size is too large. As a result, we send at most 20 classes to the human labelers at a time. For example, if the label set size of a labeling task is 40, each data item will be sent for human review 40 / 20 = 2 times, and we will charge 2 times of the price (calculated above) accordingly.
For a labeling task that enables the custom labeler feature, each data item is counted as 1 custom labeler unit.
For an active learning labeling task for data items with annotations that are generated by models (without a human labeler's help), each data item is counted as 1 active learning unit.
For an active learning labeling task for data items with annotations that are generated by human labelers, each data item is counted as a regular labeling task as described above.

The table below provides the price per 1,000 units per human labeler, based on the unit listed for each objective. Tier 1 pricing applies to the first 50,000 units per month in each Google Cloud project; Tier 2 pricing applies to the next 950,000 units per month in the project, up to 1,000,000 units. Contact us for pricing above 1,000,000 units per month.

Data type	Objective	Unit	Tier 1	Tier 2
Image	Classification	Image	$35	$25
	Bounding box	Bounding box	$63	$49
	Segmentation	Segment	$870	$850
	Rotated box	Bounding box	$86	$60
	Polygon/polyline	Polygon/Polyline	$257	$180
Video	Classification	5sec video	$86	$60
	Object tracking	Bounding box	$86	$60
	Action recognition	Event in 30sec video	$214	$150
Text	Classification	50 words	$129	$90
	Sentiment	50 words	$200	$140
	Entity extraction	Entity	$86	$60
Active Learning	All	Data item	$80	$56
Custom Labeler	All	Data item	$80	$56

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 Vertex AI lifecycle. This storage is subject to the Cloud Storage pricing policy.

Required use of Cloud Storage includes:

Staging your training application package for custom-trained models.
Storing your training input data.
Storing the output of your training jobs. Vertex AI does not 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 AI Platform are available free of charge. The AI Platform quota policy does limit some of these operations.

Resource	Free operations
models	create, get, list, delete
versions	create, get, list, delete, setDefault
jobs	get, list, cancel
operations	get, list, cancel, delete

Google Cloud costs

If you store images to be analyzed in Cloud Storage or use other Google Cloud resources in tandem with Vertex AI, then you will also be billed for the use of those services.

To view your current billing status in the Google Cloud console, including usage and your current bill, see the Billing page. For more details about managing your account, see the Cloud Billing Documentation or Billing and Payments Support.

What's next

Read the Vertex AI documentation.
Learn about Vertex AI solutions and use cases.

Request a custom quote

With Google Cloud's pay-as-you-go pricing, you only pay for the services you use. Connect with our sales team to get a custom quote for your organization.
Contact sales