[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-08-18。"],[[["\u003cp\u003eDataflow committed use discounts (CUDs) offer reduced prices on Dataflow streaming compute resources in exchange for a commitment to use a certain amount for one to three years.\u003c/p\u003e\n"],["\u003cp\u003ePurchasing a one-year CUD provides a 20% discount, while a three-year CUD offers a 40% discount from the on-demand rate, and you're billed for your committed expenditure monthly.\u003c/p\u003e\n"],["\u003cp\u003eCUDs apply to Dataflow streaming jobs' worker CPU and memory, Streaming Engine data processed and compute units, and Data Compute Units (DCUs), across all regions and projects associated with the billing account, but not to batch jobs.\u003c/p\u003e\n"],["\u003cp\u003eYour commitment amount should represent your minimum hourly expenditure on Dataflow streaming jobs, and any usage exceeding the commitment is billed at the on-demand rate.\u003c/p\u003e\n"],["\u003cp\u003eAfter purchasing a Dataflow CUD, you can't cancel it, and you must ensure that the size and duration of the commitment aligns with your expected minimum expenditure, considering factors like region, projects, and types of jobs.\u003c/p\u003e\n"]]],[],null,["# Committed use discounts\n\nThis document describes spend-based committed use discounts (CUDs) for Dataflow.\n\nCommitted use discounts (CUDs) for Dataflow provide\ndiscounted prices in exchange for your commitment to continuously spend a\nminimum hourly amount on Dataflow capacity for a year or longer.\n\nDataflow spend-based CUDs are ideal when your spending on\nDataflow capacity involves a predictable minimum that you can\ncommit to for at least a year.\n| **Important:** This page explains the new and improved committed use discounts (CUDs) program, which applies to any customers who purchase their first CUDs on or after **July 15, 2025** . Customers can opt in now to get the new CUD experience. For more information, see [Spend-based CUDs program improvements](/docs/cuds-multiprice).\n\nDataflow CUD pricing\n--------------------\n\nDataflow CUDs offer two levels of discounts, depending on the commitment period:\n\n- **20% discount**: You get this by committing to a 1-year term. For the duration of your term, you pay the Dataflow CUD 1-year price (consumption model ID 75D9-38E7-870F) as your committed hourly spend amount.\n- **40% discount**: You get this by committing to a 3-year term. For the duration of your term, you pay the Dataflow CUD 3-year price (consumption model ID 9E06-4EF0-37D8) as your committed hourly spend amount.\n\nWhen you purchase a commitment, you agree to pay a fixed hourly fee for a one\nor three-year term. Your monthly invoice shows usage charges using the\nCUD [consumption model](/billing/docs/resources/multiprice-cuds)\nprices for usage that falls within your commitment. You're charged $1 for\n$1 worth of commitment fees, and a corresponding credit applies so that the\ncommitment fee is offset for any utilized portion of your commitment.\nFor a full example, see\n[An example Dataflow CUD](#example).\n\nFor any unused portion of your commitment, the fee applies. The result\nis that you pay the flat commitment fee every hour, whether you use the\nservices or not, but commitment fees are then credited back to you for the\nused portions within the commitment amount.\n\nAny expenditure beyond the commitment gets billed at the on-demand rate.\nAs your usage grows, you can purchase additional commitments to receive\ndiscounts on increased expenditures not covered by previous commitments.\n\nThe CUD discount applies to any eligible usage in projects\nassociated with the Cloud Billing account.\n\nIf the on-demand rates change after you purchase a commitment,\nyour commitment fee doesn't change.\n\nThe discount applies to any eligible usage in Dataflow projects\nassociated with the Cloud Billing account used to purchase the commitment,\nregardless of instance configuration or region. All CUDs apply to both regional\nand multi-region configurations.\n\nResources eligible for Dataflow CUDs\n------------------------------------\n\n| **Note:** While Dataflow Committed Use Discounts (CUDs) don't apply to GPUs and TPUs, you can apply resource-based CUDs for these accelerators by purchasing Compute Engine [resource-based commitments](/compute/docs/instances/signing-up-committed-use-discounts) with *specifically targeted* reservations, and using these reservations with Dataflow. For more information, see [Use Compute Engine reservations with\n| Dataflow](/dataflow/docs/guides/compute-engine-reservations).\n\nDataflow committed use discounts automatically apply to your\nspending on the Dataflow compute capacity used by streaming jobs\nacross projects. This flexibility helps you achieve a high utilization rate of\nyour commitment across regions and projects without manual intervention, saving\nyou time and money. Dataflow CUDs apply to your spending on the\nfollowing resources:\n\n- Worker CPU and memory for streaming jobs\n- Streaming Engine data processed\n- Streaming Engine compute units\n- Data Compute Units (DCUs) for Dataflow Prime streaming jobs\n\nDataflow CUDs don't apply to your spending on the following resources:\n\n- Worker CPU and memory for batch and FlexRS jobs\n- Dataflow Shuffle data processed\n- Data Compute Units (DCUs) for Dataflow Prime batch jobs\n- Persistent Disk storage\n- GPUs and TPUs\n- Snapshots\n- Confidential VMs\n\nFor a list of applicable SKUs, see [Dataflow CUD Eligible SKUs](/skus/sku-groups/dataflow-cud-eligible-skus).\n\nPurchase a Dataflow commitment\n------------------------------\n\n| After your purchase a CUD, you can't cancel your commitment. Make sure the size and duration of your commitment aligns with both your historical and your expected minimum expenditure on Dataflow capacity. For more information, see [Canceling commitments](/docs/cuds-spend-based#canceling_commitments).\n\nTo purchase or manage Dataflow committed use discounts for your\nCloud Billing account, follow the instructions at\n[Purchasing spend-based\ncommitments](/docs/cuds-spend-based#purchasing).\n\nAn example Dataflow CUD scenario\n--------------------------------\n\nIdeally, your commitment represents at least your expected minimum hourly\nexpenditure on Dataflow streaming jobs across your projects over\nthe next one or three years.\n\nAs an example, say that you run Dataflow streaming jobs in two\ndifferent regions: `us-central1` and `us-west2`.\n\nThe streaming job in `us-central1` uses the following resources:\n\n- 10 nodes of instance type `n1-standard-1` (vCPUs: 1, RAM: 3.75 GB)\n- 20 Streaming Engine Compute Units per hour\n\nThe streaming job in `us-west2` uses the following resources:\n\n- 10 nodes of instance type `n1-standard-1` (vCPUs: 1, RAM: 3.75 GB)\n- 20 Streaming Engine Compute Units per hour\n\n| **Note:** The prices in this section are examples. For current pricing, see the [Dataflow Pricing](/dataflow/pricing) page.\n\nFrom the [pricing page](/bigtable/pricing), see the price in the column labeled **1-year commitment** to calculate the approximate hourly commitment cost:\n\n- Total expenditure in `us-central1` = $2.08271 per hour\n - 10 nodes \\* 1 streaming vCPU per node \\* $0.0552 per streaming vCPU per hour = $0.552 per hour\n - 10 nodes \\* 3.75GB per node \\* $0.0028456 per GB per hour = $0.10671 per hour\n - 20 Streaming Engine Compute Units \\* $0.0712 per compute unit per hour = $1.424 per hour\n- Total expenditure in `us-west2`= $2.5024 per hour\n - 10 nodes \\* 1 streaming vCPU per node \\* $0.06624 per streaming vCPU per hour = $0.6624 per hour\n - 10 nodes \\* 3.75GB per node \\* $0.00341472 per GB per hour = $0.128 per hour\n - 20 Streaming Engine Compute Units \\* $0.0856 per compute unit per hour = $1.712 per hour\n- Total expenditure across all regions = $4.585 per hour\n\nIf you expect to spend that minimum of $4.585 per hour continuously for the next\nyear or more, then you can make a commitment for that amount. When purchasing\nthe commitment, you enter `$4.585` as the hourly commitment amount.\n| In the legacy CUDs program, your commitment amount is the on-demand price instead. For more information about the differences between the legacy and new spend-based CUDs program, see [Spend-based CUDs program improvements](/docs/cuds-multiprice).\n\nIf you expect to scale down your clusters sometimes, you can make a commitment\nfor a lower amount. Any expenditure above the commitment amount is charged at\nthe on-demand rate.\n\nAs a basis for comparison, compute the on-demand cost of Dataflow capacity, without the application of any commitment discounts:\n\n- Monthly cost based on on-demand pricing: $5.73 per hour \\* 730 hours = $4,182.9 per month.\n\nFrom here, you can calculate the monthly costs and savings that you would see\nunder a one-year commitment with a 20% discount compared to a year of paying the\nfull rates:\n\n- Monthly cost of a one-year, $4.585/hour commitment \\* 730 hours = $3,346.32 per month\n- Total savings per month: $4,182.90 - $3,346.32 = $836.58\n- Total savings with a one-year, $5.73/hour commitment: $836.58 per month \\* 12 months = **$10,038.96**\n\nYou can apply similar math to calculating the costs and savings of a three-year CUD, with its 40% discount compared to on-demand rates:\n\n- Monthly cost of a three-year commitment: $3.438 per hour \\* 730 hours = $2,509.74 per month\n- Total savings per month: $4,182.90 - $2,509.74 = $1,673.16\n- Total savings with a three-year, $5.73/hour CUD: $1,673.16 per month \\* 36 months = **$60,233.76**\n\nA commitment that covers your expected minimum Dataflow streaming\nusage over the years to come can lead to significant savings.\n\nRecommendations for choosing a commitment\n-----------------------------------------\n\nWhen considering the purchase of Dataflow CUDs, keep in mind\nthe following:\n\n- **Projects**: Determine the consistent baseline expenditure per project while calculating total commitment. Consider that production loads usually run 100% of the time, while development or staging environments might run intermittently.\n- **Resources**: If you frequently scale your resources up or down, consider purchasing CUDs only for their baseline predictable usage. If you have instances that you run only for bursts or brief durations, exclude them from your calculations.\n- Your commitment fee applies to every hour during the term of the commitment, regardless of actual usage. Choose your CUD's commitment amount carefully, based on both your historical Dataflow usage and your future expectations. As long as your use of Dataflow capacity stays higher than your committed expenditure level, you will enjoy the maximum possible discount for the length of that commitment.\n\nWhat's next\n-----------\n\n - Read [an overview of Dataflow pricing](/dataflow/pricing).\n - Learn [more about Google Cloud spend-based CUDs](/docs/cuds).\n - Learn how to [view your CUD reports](/billing/docs/how-to/cud-analysis).\n - Understand savings with [cost breakdown reports](/billing/docs/how-to/cost-breakdown)."]]
