At Google Cloud Next 2025, Google announced the launch of Committed Use Discounts (CUDs) for BigQuery, an update giving customers a new way to reduce costs on one of Google Cloud’s most widely used analytics services. While spend-based CUDs have long been available for services like Compute Engine, Cloud SQL, and more, this marks the first time they’re being extended to BigQuery.
For many organizations, BigQuery is a major contributor to cloud spend. Until now, rate optimization options were limited, as BigQuery slot commitments were the only way to reduce the cost below list prices. This recent update introduces a new way to improve cost efficiency — one that gives teams more flexibility without requiring major changes to how they use BigQuery.
In this article, we’ll explain how BigQuery CUDs work, what benefits they offer, and how to purchase them. We’ll also cover the best practices for getting the most from BigQuery CUDs.
What Are BigQuery Committed Use Discounts (CUDs)?
BigQuery CUDs are a pricing model that allows you to save on BigQuery compute costs by committing to a fixed usage over a one-year or three-year term. In exchange for this commitment, Google Cloud offers a discounted rate compared to Pay-As-You-Go (PAYG) pricing.
Discount percentages on spend-based BiqQuery CUDs range from 10% for one-year commitments to 20% for three-year commitments. Discounts apply to the portion of hourly Pay-As-You-Go (PAYG) compute spend that falls within a committed amount in a specific Google Cloud region.
It’s essential to recognize that spend-based CUDs differ from capacity-based commitments. Capacity commitments involve purchasing a fixed number of BigQuery slots for specific durations. For spend-based Committed Use Discounts, the commitment is based on dollar spend, not specific resource amounts, which gives you more freedom to optimize without restructuring queries.
BigQuery CUDs are best suited for teams with steady or predictable analytics workloads. However, it’s important to note that unused capacity within your commitment does not carry over, meaning accurate forecasting is key to maximizing savings.
How BigQuery Committed Use Discounts Work
BigQuery CUDs offer discounted pricing in exchange for a time-based usage commitment. Customers commit to a fixed hourly spend on BigQuery analysis for either one year or three years. In return, Google Cloud applies a lower rate compared to PAYG slot pricing.
These commitments are not tied to specific workloads. Instead, they apply across your BigQuery environment, making it easier to cover broad usage patterns without separate configurations. Discounts are applied automatically to eligible usage up to the committed amount.
If your actual usage exceeds the committed level, the overage is billed at standard PAYG rates. If usage falls short, the committed amount is still charged, so careful planning is essential to avoid underutilization.
Why Consider Spend-Based CUDs for BigQuery?
There are several reasons why businesses may want to consider purchasing spend-based CUDs for their BigQuery environments. Some of these include:
Predictable pricing for long-term workloads
When implementing sustained workloads in cloud environments, CUDs offer more pricing predictability by capitalizing on reduced billing rates over the long term.
Having this reduced pricing locked in for an extended period of time helps teams avoid the pricing fluctuations often associated with PAYG pricing. It also allows them to forecast their spending more accurately.
Cost savings in exchange for commitment
By committing to consistent usage, you gain access to discounted pricing that applies automatically to your existing workloads. This helps reduce spend on BigQuery analysis by up to 20%, without needing to re-architect pipelines or manage slot capacity commitments separate from your CUDs. For teams with predictable query volumes, it’s a low-effort way to improve cost efficiency over time.
Predictable budgeting
BigQuery CUDs help stabilize your analytics costs by replacing variable PAYG pricing with fixed hourly commitments. This makes it easier for finance and engineering teams to forecast budgets, track spend against targets, and avoid unexpected cost spikes — especially in environments with steady query workloads.
BigQuery CUDs vs. Capacity Commitments: When To Use Each
While BigQuery spend-based CUDs offer a new path to savings, they are not the only option for optimizing costs. Google Cloud has also offered capacity commitments, which allow you to purchase dedicated BigQuery slots over a fixed period at a discounted rate. Choosing between these two models depends on the services you use and the consistency of your workloads.
- Capacity commitments are typically a better fit for teams that run steady BigQuery analysis jobs and want to manage slot capacity directly. These commitments often offer higher discounts and are most effective when slot usage is the primary cost driver in Bigquery Enterprise or Entreprise Plus Editions.
- In contrast, spend-based CUDs apply more broadly across BigQuery services, including all BigQuery Editions, Cloud Composer 3 compute SKUs, and BigQuery Data Governance. If your team relies on a mix of these services with consistent usage throughout the day, spend-based CUDs may offer more flexibility and better coverage. Note that BigQuery CUDs do not cover On-demand analysis charges.
It’s important to analyze your hourly eligible spend by region using tools like the CUD Analysis Tool or the detailed billing export before making any purchasing decisions. If you already have existing capacity commitments, ensure you are doing analysis to identify if there is any additional usage to have BigQuery CUDs cover before making purchases.
In some cases, it may be beneficial to combine one-year and three-year CUDs and cover only a portion of your eligible spend. This approach can help balance savings with flexibility and reduce the risk of overcommitment.
How To Purchase a BigQuery Spend-Based CUD
Google Cloud makes it easy for users to acquire BigQuery CUDs based on their specific usage needs. To help with this process, below is a step-by-step guide for purchasing these commitments:
1. Analyze your usage patterns
Before purchasing a BigQuery CUD, it’s important to assess whether your usage patterns are stable enough to benefit from a long-term commitment. Google Cloud provides a CUD Analysis Tool within the Billing section of the Console, which helps identify consistent, eligible usage that could be covered by a commitment. Using this tool ensures you make data-driven decisions and avoid overcommitting to workloads with fluctuating demand.
2. Navigate to the Committed Use Discounts section
To purchase a spend-based CUD, go to the Google Cloud Console and log in to your business account.
Once you’re logged in, open the main navigation menu by clicking on the top-left corner represented by three horizontal lines. Select the Billing tab from this menu.
If you have multiple billing accounts, select the one where you would like to purchase the CUD. After selecting the appropriate account, click on Committed Use Discounts.
3. Choose the commitment type and term
In the Committed Use Discounts dashboard, select purchase. This will provide you with a list of product types.
Select the BigQuery option and make sure that you’ve selected spend-based commitment.
Next, choose the commitment term you want to purchase: one-year or three-year. Keep in mind that with a one-year term, you’ll receive a 10% discount on eligible usage versus 20% for a three-year commitment.
4. Select the region for your commitment
Select the region you want to purchase the spend-based CUD in. This should represent the location where your primary BigQuery workloads are running and where your PAYG spend typically occurs.
Keep in mind that any BigQuery CUDs you purchase are region-specific. You will not be able to cancel or modify it after you’ve completed a purchase.
5. Set your hourly spend commitment
In the designated field, enter the hourly commitment amount in U.S. dollars ($/hour). The number you select should reflect the average usage you’ve analyzed separately for that specific region.
After you provide a commitment amount, Google Cloud will display your calculated commitment fee. This is the discounted hourly rate you will pay, and your estimated savings, assuming you continue to meet or exceed your monthly commitments.
6. Review the terms and confirm purchase
Before finalizing your purchase, make sure you review all the details thoroughly. Review your commitment term, the selected region, the commitment amount, and the Service Specific Terms and Conditions.
Once you have read through all your purchasing details, including the non-cancellation clause, click Purchase. Your CUDs will activate and show on your account within one hour.
7. Monitor usage and costs over time
Once your BigQuery CUD is in place, keep a close eye on usage to ensure you’re staying within your committed spend. Underutilization means wasted budget, while frequent overages trigger standard pay-as-you-go rates that can leave money on the table. Regular monitoring helps you course-correct early and get the most value from your commitment.
How To Leverage BigQuery CUDs for Better Cost Savings
BigQuery CUDs offer a great opportunity to reduce cloud analytics costs, but realizing their full benefit requires thoughtful planning. Below are four practical ways to make the most of BigQuery CUDs and drive long-term savings:
Identify and target predictable usage patterns
Before purchasing a BigQuery CUD, it’s first important to understand your regular cloud usage trends. Use Google Cloud’s built-in billing reports and Google cloud cost management tools (or third-party FinOps platforms) to analyze historical query usage, slot consumption, and data processing volumes over time. Focus specifically on:
- Consistent daily jobs or data transformations
- Scheduled batch processing
- Long-running analytics workloads tied to core business processes
Once identified, group these predictable workloads by project, region, and billing account to understand where your most stable usage lives. CUDs are region-specific, so be sure to align each commitment with the appropriate geographic footprint. Targeting these stable, high-usage workloads will help you extract more value from your spend-based CUDs and avoid idle commitments tied to erratic or seasonal spikes.
Start with small commitments and layer strategically
Although it can be tempting to try to lock in as many potential savings as possible with a larger BigQuery CUD purchase, keep in mind you’ll still need to pay for your full commitment amounts even if your usage demands lower over time.
Rather than committing a large portion of your BigQuery spend upfront, begin with a conservative estimate. This gives you room to optimize without overcommitting, and it helps protect against unforeseen dips in demand.
Then, as you monitor your actual spend and feel more confident about your sustained usage, you can layer additional, smaller CUDs for broader coverage and increased savings.
This layering strategy helps scale your commitments gradually, improving your Effective Savings Rate (ESR) while keeping your Commitment Lock-in Risk (CLR) low. It also ensures that your discount coverage expands in line with actual growth, not overly optimistic projections.
Choose the right commitment term
When deciding which duration makes the most sense for your business, think about how likely it is that your current BigQuery usage patterns will remain consistent over more extended periods of time. Then choose the duration that aligns with both your technical predictability and budgetary risk tolerance.
If your workloads are highly stable and business plans are long-term, a three-year commitment may offer the highest cost-efficiency. For evolving workloads or uncertain growth trajectories, a one-year commitment helps balance savings and flexibility.
Track the right metrics
When measuring the potential benefits and risks associated with BigQuery CUDs, it’s essential to track relevant business metrics. Unfortunately, simply referencing the coverage %, utilization % or the “estimated savings” that Google Cloud calculates on your behalf is not enough to provide a comprehensive understanding of your cost optimization performance and potential exposure.
Two helpful metrics to track when deciding on BigQuery CUD purchases are Effective Savings Rate (ESR) and Commitment Lock-in Risk (CLR):
- Effective Savings Rate (ESR): This is a FinOps standard metric that measures the actual ROI of various cloud rate optimization efforts, including discount instruments, private rates, and Spot VMs or Instances. ESR serves as a key objective indicator of success for understanding cloud cost and rate optimization.
- Commitment Lock-in Risk (CLR): It is a companion metric to ESR which quantifies the time dimension risk of committing to a cloud provider in exchange for a discounted rate as measured in months. The lower the CLR, the lower the risk. Superior FinOps programs aim to both improve ESR and CLR.
To better understand the bigger picture, you can easily benchmark both the metrics and compare your performance with the peers.
How Do I Calculate Savings With BigQuery CUDs?
To calculate your potential BigQuery CUDs, you’ll need to compare your current BigQuery PAYG usage with the potential discount rate.
For example, let’s say you analyzed your BigQuery spend in a specific region and found that you’re paying an average of $10 per hour on BigQuery Enterprise Plus editions and $2 per hour on eligible Cloud Composer 3 SKUs. Assuming you decided to make a one-year commitment, you’d be eligible for a 10% discount on eligible spend.
You would then use the formula:
Potential Hourly Savings = Your Average Hourly Spend × Discount Rate
or
Potential Hourly Savings = ($10+$2) × 0.10 = $1.20 per hour
This means that with the CUD in place, you could save $1.20 for every $12 of your consistent hourly spend that’s covered by the commitment. Your new hourly cost for that committed portion would be $10.80.
Final Thoughts
BigQuery CUDs are a thoughtful addition to Google Cloud’s pricing model, offering meaningful savings for teams with predictable analytics workloads. However, realizing those savings isn’t straightforward. Accurate forecasting, ongoing usage tracking, and repeated purchasing all require time and precision, time that could be better spent on higher-value initiatives.
That’s where rate optimization automation becomes critical.
ProsperOps offers a dynamic approach to managing Google Cloud costs through autonomous discount instrument management. We automatically purchase CUDs in small, incremental “rungs” over time, rather than a single, batched commitment. This helps maximize your Effective Savings Rate (ESR) and reduce Commitment Lock-In Risk (CLR).
By removing the effort, latency, and financial risk associated with manually managing rigid, long-term discount instruments, ProsperOps simplifies cloud financial management.
ProsperOps support for BigQuery CUDs isn’t available yet, but stay tuned.
Schedule a demo today to see ProsperOps in action!
