The cost of the infrastructure depends on the following factors:
- The number of machines that you use. You can set associated parameters during model training, batch inference, or online inference.
- The type of machines that you use. You can set this parameter during model training, batch inference, or online inference.
- The length of time for which the machines are in use.
- If you are training a model or making batch inferences, this is a measure of the total processing time of the operation.
- If you are making online inferences, this is a measure of the time that your model is deployed to an endpoint.
Tabular Workflows runs multiple dependent services in your project on your behalf: Dataflow, BigQuery, Cloud Storage, Vertex AI Pipelines, Vertex AI Training. You will be charged by these services directly.
Examples of training cost calculation
Example 1: 110MB dataset in CSV format, trained for one hour with default hardware configuration.
The cost breakdown for the default workflow with Architecture Search and Training is as follows:
| Service | Cost |
|---|---|
| Dataflow example and stats generation | $2 (Dataflow ran 7 min) |
| Dataflow data and feature transformations | $3 (Dataflow ran 10 min) |
| Vertex AI Training | 0.8hr x $20 + 0.2hr x $20 + $3.3 SSD cost + pipeline container cost = $24 (48min tuning, 12min training) |
| Vertex AI Pipelines | 1 run x $0.03 = $0.03 |
| Total excluding model distillation | $27.03 |
Optionally, you can enable model distillation to reduce resulting model size. The cost breakdown is as follows:
| Service | Cost |
|---|---|
| Total excluding model distillation | $27.03 |
| Vertex AI Training for model distillation | $1 |
| Dataflow data, feature transformations for model distillation | $3 (Dataflow ran 10 min) |
| Batch inference for model distillation | $7 |
| Total including model distillation | $38.03 |
Example 2: 1.84TB dataset in BigQuery, trained for 20 hours with hardware override.
The hardware configuration for this example is as follows:
| Hardware Configuration Name | Value |
|---|---|
| stats_and_example_gen_dataflow_machine_type | n1-standard-16 |
| stats_and_example_gen_dataflow_max_num_workers | 100 |
| stats_and_example_gen_dataflow_disk_size_gb | 40 |
| transform_dataflow_machine_type | n1-standard-16 |
| transform_dataflow_max_num_workers | 100 |
| transform_dataflow_disk_size_gb | 200 |
| distill_batch_predict_machine_type | n1-standard-2 |
| distill_batch_predict_starting_replica_count | 200 |
| distill_batch_predict_max_replica_count | 200 |
The cost breakdown for the default workflow with Architecture Search and Training is as follows:
| Service | Cost |
|---|---|
| Dataflow example and stats generation | $518 (Dataflow ran 6 hours) |
| Dataflow data, feature transformations | $471 (Dataflow ran 6 hours) |
| Vertex AI Training | 17hr x $20 + 3hr x $20 + $41.5 SSD cost + pipeline container cost = $555 (17 hours tuning, 3 hours training) |
| Vertex AI Pipelines | 1 run x $0.03 = $0.03 |
| Total | $1544.03 |