Google BigQuery is a fully managed data warehousing tool that abstracts you from any form of physical infrastructure so you can focus on tasks that matter to you. Hence, understanding Google BigQuery Pricing is pertinent if your business is to take full advantage of the Data Warehousing tool’s offering.
However, the process of understanding Google BigQuery Pricing is not as simple as it may seem. The focus of this blog post will be to help you understand the Google BigQuery Pricing setup in great detail. This would, in turn, help you tailor your data budget to fit your business needs.
Table of Contents
What is to Google BigQuery?
It is Google Cloud Platform’s enterprise data warehouse for analytics. Google BigQuery performs exceptionally even while analyzing huge amounts of data & quickly meets your Big Data processing requirements with offerings such as exabyte-scale storage and petabyte-scale SQL queries. It is a serverless Software as a Service (SaaS) application that supports querying using ANSI SQL & houses machine learning capabilities.
Some key features of Google BigQuery:
- Scalability: Google BigQuery offers true scalability and consistent performance using its massively parallel computing and secure storage engine.
- Data Ingestions Formats: Google BigQuery allows users to load data in various formats such as AVRO, CSV, JSON etc.
- Built-in AI & ML: It supports predictive analysis using its auto ML tables feature, a codeless interface that helps develop models having best in class accuracy. Google BigQuery ML is another feature that supports algorithms such as K means, Logistic Regression etc.
- Parallel Processing: It uses a cloud-based parallel query processing engine that reads data from thousands of disks at the same time.
For further information on Google BigQuery, you can check the official site here.
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What are the Factors that Affect Google BigQuery Pricing?
Google BigQuery uses a pay-as-you-go pricing model, and thereby charges only for the resources they use. There are mainly two factors that affect the cost incurred on the user, the data that they store and the amount of queries, users execute.
You can learn about the factors affecting Google BigQuery Pricing in the following sections:
Effect of Storage Cost on Google BigQuery Pricing
Storage costs are based on the amount of data you store in BigQuery. Storage costs are usually incurred based on:
- Active Storage Usage: Charges that are incurred monthly for data stored in BigQuery tables or partitions that have some changes effected in the last 90 days.
- Long Time Storage Usage: A considerably lower charge incurred if you have not effected any changes on your BigQuery tables or partitions in the last 90 days.
- BigQuery Storage API: Charges incur while suing the BigQuery storage APIs based on the size of the incoming data. Costs are calculated during the ReadRows streaming operations.
- Streaming Usage: Google BigQuery charges users for every 200MB of streaming data they have ingested.
Data Size Calculation
Once your data is loaded into BigQuery you start incurring charges, the charge you incur is usually based on the amount of uncompressed data you stored in your BigQuery tables. The data size is calculated based on the data type of each individual columns of your tables. Data size is calculated in Gigabytes(GB) where 1GB is 230 bytes or Terabytes(TB) where 1TB is 240 bytes(1024 GBs). The table shows the various data sizes for each data type supported by BigQuery.
| Datatype | Size |
| Int 64/ Integer | 8 bytes |
| Float64/ Float | 8 bytes |
| Numeric | 16 bytes |
| Boolean | 1 byte |
| String | 2 bytes + the UTF-8 encoded string size |
| Bytes | 2 bytes + the number of bytes in the value |
| Date/ DateTime | 8 bytes |
| Timestamp | 8 bytes |
| Struct/ Record | 0 + the bytes of the contained fields |
| Null value | 0 bytes |
For example, let’s say you have a table called New_table saved on BigQuery. The table contains 2 columns with 100 rows, Column A and B. Say column A contains integers and column B contains DateTime data type. The total size of our table will be (100 rows x 8 bytes) for column A + (100 rows x 8 bytes) for column B which will give us 1600 bytes.
BigQuery Storage Pricing
Google BigQuery pricing for both storage use cases is explained below.
- Active Storage Pricing: Google BigQuery pricing for active storage usage is as follows:
| Region (U.S Multi-region) | | |
| Storage Type | Pricing | Details |
| Active Storage | $0.020 per GB | BigQuery offers free tier storage for the first 10 GB of data stored each month |
So if we store a table of 100GB for 1 month the cost will be (100 x 0.020) = $2 and the cost for half a month will $1.
Be sure to pay close attention to your regions. Storage costs vary from region to region. For example, the storage cost for using Mumbai (South East Asia) is $0.023 per GB, while the cost of using the EU(multi-region) is $0.020 per GB.
- Long-term Storage Pricing: Google BigQuery pricing for long-term storage usage is as follows:
| Region (U.S Multi-Region) | | |
| Storage Type | Pricing | Details |
| Long-term storage | $0.010 per GB | BigQuery offers free tier storage for the first 10 GB of data stored each month |
The price for long term storage is considerably lower than that of the active storage and also varies from location to location. If you modify the data in your table, it 90 days timer reverts back to zero and starts all over again. Be sure to always keep that in mind.
- BigQuery Storage API: Storage API charge is incurred during ReadRows streaming operations where the cost accrued is based on incoming data sizes, not on the bytes of the transmitted data. BigQuery Storage API has two tiers for pricing they are:
- On-demand pricing: These charges are incurred per usage. The charges are:
| Pricing | Details |
| $1.10 per TB data read | BigQuery Storage API is not included in the free tier |
- Flat rate pricing: This Google BigQuery pricing is available only to customers on flat-rate pricing. Customers on flat-rate pricing can read up to 300TB of data monthly at no cost. After exhausting 300TB free storage, the pricing reverts to on-demand.
- Streaming Usage: Pricing for streaming data into BigQuery is as follows:
| Operation | Pricing | Details |
| Ingesting streamed data | $0.010 per 200MB | Rows that are successfully ingested are what you are charged for |
Effect of Query Cost on Google BigQuery Pricing
This involves costs incurred for running SQL commands, user-defined functions, Data Manipulation Language (DML) and Data Definition Language (DDL) statements. DML are SQL statements that allow you to update, insert, delete data from your BigQuery tables. DDL statements, on the other hand, allows you to create, modify BigQuery resources using standard SQL syntax.
BigQuery offers it’s customers two tiers of pricing from which they can choose from when running queries. The pricing tiers are:
- On-demand Pricing: In this Google BigQuery pricing model you are charged for the number of bytes processed by your query, the charges are not affected by your data source be it on BigQuery or an external data source. You are not charged for queries that return an error and queries loaded from cache. On-demand pricing information is given below:
| Operation | Pricing | Details |
| Queries (on demand) | $5 per TB | 1st 1TB per month is not billed |
Prices also vary from location to location.
- Flat-rate Pricing: This Google BigQuery pricing model is for customers who prefer a stable monthly cost to fit their budget. Flat-rate pricing requires its users to purchase BigQuery Slots. All queries executed are charged to your monthly flat rate price. Flat rate pricing is only available for query costs and not storage costs. Flat rate pricing has two tiers available for selection
- Monthly Flat-rate Pricing: The monthly flat-rate pricing is given below:
| Monthly Costs | Number of Slots |
| $ 10,000 | 500 |
- Annual Flat-rate Pricing: In this Google BigQuery pricing model you buy slots for the whole year but you are billed monthly. Annual Flat-rate costs are quite lower than the monthly flat-rate pricing system. An illustration is given below:
| Monthly Costs | Number of Slots |
| $8,500 | 500 |
How to Check Google BigQuery Cost?
Now that you have a good idea of what different activities will cost you on BigQuery, the next step would be to estimate your Google BigQuery Pricing. For that operation, Google Cloud Platform(GCP) has a tool called the GCP Price Calculator.
In the next sections, let us look at how to estimate both Query and Storage Costs using the GCP Price Calculator:
Using the GCP Price Calculator to Estimate Query Cost
On-demand Pricing:
For customers on the on-demand pricing model, the steps to estimate your query costs using the GCP Price calculator are given below:
- Login to your BigQuery console home page.
- Enter the query you want to run, the query validator(the green tick) will verify your query and give an estimate of the number of bytes processed. This estimate is what you will use to calculate your query cost in the GCP Price Calculator.
From the image above, we can see that our Query validator will process 3.1 GB of data when the query is run. This value would be used to calculate the query cost on GCP Price calculator.
- The next action is to open the GCP Price calculator to calculate Google BigQuery pricing.
- Select BigQuery as your product and choose on-demand as your mode of pricing.
- Populate the on-screen form with all the required information, the image below gives an illustration.
From the image above the costs for running our query of 3.1GB is $0, this is because we have not exhausted our 1TB free tier for the month, once it is exhausted we will be charged accordingly.
Flat-rate Pricing:
The process for on-demand and flat-rate pricing is very similar to the above steps. The only difference is – when you are on the GCP Price Calculator page, you have to select the Flat-rate option and populate the form to view your charges.
How much does it Cost to Run a 12 GiB Query in BigQuery?
In this pricing model, you are charged for the number of bytes processed by your query. Also, you are not charged for queries that return an error and queries loaded from the cache.
BigQuery charges you $5 per TB of a query processed. However, 1st 1TB per month is not billed. So, to run a 12 GiB Query in BigQuery, you don’t need to pay anything if you have not exhausted the 1st TB of your month.
So, let’s assume you have exhausted the 1st TB of the month. Now, let’s use the GCP Price Calculator to estimate the cost of running a 12 GiB Query. Populate the on-screen form with all the required information and calculate the cost.
According to the GCP Calculator, it will cost you around $0.06 to process 12 GiB Query.
How much does it Cost to Run a 1TiB Query in BigQuery?
Assuming you have exhausted the 1st TB of the month. Now, let’s use the GCP Price Calculator to estimate the cost of running a 1 TiB Query. Populate the on-screen form with all the required information and calculate the cost.
According to the GCP Calculator, it will cost you $5 to process 1 TiB Query.
How much does it Cost to Run a 100 GiB Query in BigQuery?
Assuming you have exhausted the 1st TB of the month. Now, let’s use the GCP Price Calculator to estimate the cost of running a 100 GiB Query. Populate the on-screen form with all the required information and calculate the cost.
According to the GCP Calculator, it will cost you $0.49 to process 100 GiB Query.
Using the GCP Price Calculator to Estimate Storage Cost
The steps to estimating your storage cost with the GCP price calculator are as follows:
- Access the GCP Price Calculator home page.
- Select BigQuery as your product.
- Click on the on-demand tab (BigQuery does not have storage option for Flat rate pricing).
- Populate the on-screen form with your table details and size of the data you want to store either in MB, GB or TB. (Remember the first 10GB of storage on BigQuery is free)
- Click add to estimate to view your final cost estimate.
BigQuery API Cost
The pricing model for the Storage Read API can be found in on-demand pricing. On-demand pricing is completely usage-based. Apart from this, BigQuery’s on-demand pricing plan also provides its customers with a supplementary tier of 300TB/month.
Although, you would be charged on a per-data-read basis on bytes from temporary tables. This is because they aren’t considered a component of the 300TB free tier. Even if a ReadRows function breaks down, you would have to pay for all the data read during a read session. If you cancel a ReadRows request before the completion of the stream, you will be billed for any data read prior to the cancellation.
BigQuery Custom Cost Control
If you dabble in various BigQuery users and projects, you can take care of expenses by setting a custom quote limit. This is defined as the quantity of query data that can be processed by users in a single day.
Personalized quotas set at the project level can constrict the amount of data that might be used within that project. Personalized User Quotas are assigned to service accounts or individual users within a project.
BigQuery Flex Slots
Google BigQuery Flex Slots were introduced by Google back in 2020. This pricing option lets users buy BigQuery slots for short amounts of time, beginning with 60-second intervals. Flex Slots are a splendid addition for users who want to quickly scale down or up while maintaining predictability of costs and control.
Flex Slots are perfect for organizations with business models that are subject to huge shifts in data capacity demands. Events like a Black Friday Shopping surge or a major app launch make perfect use cases. Right now, Flex Slots cost $0.04/slot, per hour. It also provides you with the option to cancel at any time after 60 seconds. This means you will only be billed for the duration of the Flex Slots Deployment.
How to Stream Data into BigQuery without Incurring a Cost?
Loading data into BigQuery is entirely free, but streaming data into BigQuery adds a cost. Hence, it is better to load data than to stream it, unless quick access to your data is needed.
Tips for Optimizing your BigQuery Cost
The following are some best practices that will prevent you from incurring unnecessary costs when using BigQuery:
- Avoid using SELECT * when running your queries, only query data that you need.
- Sample your data using the preview function on BigQuery, running a query just to sample your data is an unnecessary cost.
- Always check the prices of your query and storage activities on GCP Price Calculator before executing them.
- Only use Streaming when you require your data readily available. Loading data in BigQuery is free.
- If you are querying a large multi-stage data set, break your query into smaller bits this helps in reducing the amount of data that is read which in turn lowers cost.
- Partition your data by date, this allows you to carry out queries on relevant sub-set of your data and in turn reduce your query cost.
With this, we can conclude the topic of BigQuery Pricing.
Conclusion
This write-up has exposed you to the various aspects of Google BigQuery Pricing to help you optimize your experience when trying to make the most out of your data. You can now easily estimate the cost of your BigQuery operations with the methods mentioned in this write-up. In case you want to export data from a source of your choice into your desired Database/destination like Google BigQuery, then Hevo Data is the right choice for you!
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We are all ears to hear about any other questions you may have on Google BigQuery Pricing. Let us know your thoughts in the comments section below.
Bukunmi is curious about learning on complex concepts and latest trends in data science and combines his flair for writing to curate content for data teams to help them solve business challenges.