Cloud Dataflow pricing

This page describes pricing for Cloud Dataflow. To see the pricing for other products, read the Pricing documentation.

Pricing overview

While the rate for pricing is based on the hour, Cloud Dataflow service usage is billed in per second increments, on a per job basis. Usage is stated in hours (30 minutes is 0.5 hours, for example) in order to apply hourly pricing to second-by-second use. Workers and jobs may consume resources as described in the following sections.

Workers and worker resources

Each Cloud Dataflow job uses at least one Cloud Dataflow worker. The Cloud Dataflow service provides two worker types: batch and streaming. There are separate service charges for batch and streaming workers.

Cloud Dataflow workers consume the following resources, each billed on a per second basis.

Batch and streaming workers are specialized resources that use Compute Engine. However, a Cloud Dataflow job will not emit Compute Engine billing for Compute Engine resources managed by the Cloud Dataflow service. Instead, Cloud Dataflow service charges will encompass the use of these Compute Engine resources.

You can override the default worker count for a job. If you are using autoscaling, you can specify the maximum number of workers to be allocated to a job. Workers and respective resources will be added and removed automatically based on autoscaling actuation.

In addition, you can use pipeline options to override the default resource settings (machine type, disk type, and disk size) that are allocated to each worker.

Cloud Dataflow services

The Cloud Dataflow Shuffle operation partitions and groups data by key in a scalable, efficient, fault-tolerant manner. By default, Cloud Dataflow uses a shuffle implementation that runs entirely on worker virtual machines and consumes worker CPU, memory, and Persistent Disk storage.

Cloud Dataflow also provides an optional highly scalable feature, Cloud Dataflow Shuffle, which is available only for batch pipelines and shuffles data outside of workers. Shuffle charges by the volume of data processed. You can instruct Cloud Dataflow to use Shuffle by specifying the Shuffle pipeline parameter.

Similar to Shuffle, the Cloud Dataflow Streaming Engine moves streaming shuffle and state processing out of the worker VMs and into the Cloud Dataflow service backend. You instruct Cloud Dataflow to use the Streaming Engine for your streaming pipelines by specifying the Streaming Engine pipeline parameter. Streaming Engine usage is billed by the volume of streaming data processed, which depends on the volume of data ingested into your streaming pipeline and the complexity and number of pipeline stages. Examples of what counts as a byte processed include input flows from data sources, flows of data from one fused pipeline stage to another fused stage, flows of data persisted in user-defined state or used for windowing, and output messages to data sinks, such as to Cloud Pub/Sub or BigQuery.

Cloud Dataflow also provides an option with discounted CPU and memory pricing for batch processing. Flexible Resource Scheduling (FlexRS) combines regular and preemptible VMs in a single Cloud Dataflow worker pool, giving users access to cheaper processing resources. FlexRS also delays the execution of a batch Cloud Dataflow job within a 6-hour window to identify the best point in time to start the job based on available resources. While Cloud Dataflow uses a combination of workers to execute a FlexRS job, you are billed a uniform discounted rate compared to regular Cloud Dataflow prices, regardless of the worker type. You instruct Cloud Dataflow to use FlexRS for your autoscaled batch pipelines by specifying the FlexRS parameter.

Additional job resources

In addition to worker resource usage, a job might consume the following resources, each billed at its own pricing, including but not limited to:

Pricing details

Future releases of Cloud Dataflow may have different service charges and/or bundling of related services.

See the Compute Engine Regions and Zones page for more information about the available regions and their zones.

1 Batch worker defaults: 1 vCPU, 3.75 GB memory, 250 GB Persistent Disk

2 FlexRS worker defaults: 2 vCPU, 7.50 GB memory, 25 GB Persistent Disk per worker, with a minimum of two workers

3 Streaming worker defaults: 4 vCPU, 15 GB memory, 420 GB Persistent Disk

4 Cloud Dataflow Shuffle is currently available for batch pipelines in the following regions:

  • us-central1 (Iowa)
  • us-west1 (Oregon)
  • europe-west1 (Belgium)
  • europe-west4 (Netherlands)
  • asia-east1 (Taiwan)
  • asia-northeast1 (Tokyo)

It will become available in other regions in the future.

5 Cloud Dataflow Streaming Engine uses the Streaming Data Processed pricing unit. Streaming Engine is currently available in the following regions:

  • us-central1 (Iowa)
  • us-west1 (Oregon)
  • europe-west1 (Belgium)
  • europe-west4 (Netherlands)
  • asia-east1 (Taiwan)
  • asia-northeast1 (Tokyo)
It will become available in other regions in the future.

6 Prior to May 3, 2018, Cloud Dataflow Shuffle was billed by the amount of data shuffled times the time it took to shuffle the data and keep it in Shuffle’s memory; the price was $0.0216 per Gigabyte per Hour. After May 3, 2018, Shuffle is priced exclusively by the amount of data that the Cloud Dataflow service infrastructure reads and writes in the process of shuffling your dataset; the pricing unit is Gigabytes with the time dependency removed from billing consideration. Users with large or very large datasets should expect to see significant reductions in their total Shuffle costs.
To further encourage the adoption of service-based Shuffle, the first 5 Terabytes of Shuffle Data Processed are charged at rates reduced by 50%. For example, if your pipeline results in 1 TB of actual Shuffle Data Processed, you are charged only for 50% of that data volume (0.5 TB). If your pipeline results in 10 TB of actual Shuffle Data Processed, you are charged for 7.5 TB, because the first 5 TB of that volume are charged at 50% reduced rates.

Viewing usage

You can view the total vCPU, memory, and Persistent Disk resources associated with a job either in the Google Cloud Platform Console or via the gcloud command line tool. You can track both the actual and chargeable Shuffle Data Processed and Streaming Data Processed metrics in the Cloud Dataflow Monitoring Interface. You can use the actual Shuffle Data Processed to evaluate the performance of your pipeline and the chargeable Shuffle Data Processed to determine the costs of the Cloud Dataflow job. For Streaming Data Processed, the actual and chargeable metrics are identical.

Pricing calculator

Use the Google Cloud Platform Pricing Calculator to help you understand how your bill is calculated.

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