Google Cloud Pricing Guide: Calculate & Optimize Costs

Google Cloud is a collection of services including storage and compute resources, Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), provided by Google. The company offers several pricing options catering to different demographics and use cases, including a free tier, pay-as-you-go options, and discounted long-term reservations. Because there are so many pricing options, it can be difficult for organizations to predict how much their cloud usage will cost them.

The Google Cloud Pricing Calculator is a powerful tool to help organizations estimate costs and better manage their usage. In this guide, we’ll look at Google’s pricing models, cost optimization strategies, and common pitfalls when it comes to Google Cloud cost management.

If you’re using Google Cloud for any of your organization’s applications, take a look at Veeam Backup for Google Cloud. This solution offers end-to-end secure backups and fast, reliable recovery tools for Google Cloud instances.

Understanding Google Cloud Pricing

Google Cloud offers a tiered pricing structure, including:

  • A free tier: This offers a small selection of “always free” cloud services for users with low usage requirements, including IoT, AI, database, compute and storage products.
  • Pay-as-you-go options: The on-demand pricing model lets users pay for their services based on their usage. For example, compute engine products are charged based on the time they’re run for, while storage fees are calculated based on metrics such as the volume of storage used, network usage, and operations performed on that storage. The pay-as-you-go option offers the most flexibility but costs more than long-term reservations.
  • Long-term pricing: Organizations that plan to use the cloud to run their operations in the long term may benefit from a long-term pricing plan. This plan requires the organization to commit to a long-term cloud deployment of between one and three years. In return, they receive a committed use discount, which can be as high as 70% compared to pay-as-you-go pricing.

Overview of Google Cloud’s Pay-as-You-Go Pricing Model

The pay-as-you-go model offered by Google Cloud can be confusing for some users because the pricing structure differs across products. There are several things measured as a part of the usage-based pricing, including computing time, data storage, data egress and ingress, operations performed on storage, instance size and even how the data is transferred. 

If you’re making use of multiple services on Google Cloud, it’s a good idea to read the pricing terms for each one and take advantage of the pricing calculator to estimate fees based on your projected usage. We’ll break down some of the factors that influence Google Cloud costs below.

Factors Influencing Google Cloud Costs

Google Cloud pricing is based on several factors, including:

  • Virtual machines and instance types: Machines are categorized by their workloads, such as general purpose, accelerator-optimized, compute-optimized, and memory-optimized. Each of these is priced at a different rate. Fees are calculated based on how long the machine is in use, and sustained use discounts are available for users who run a VM for more than a certain amount of time per month.
  • Storage options (snapshots on volumes vs. object storage): Persistent disks are charged at a flat rate per GB/month, depending on whether they’re a magnetic disk or an SSD. Google Cloud Storage buckets are priced differently, based on the volume of the data retained in the bucket, the volume of data moved between buckets, data replicated across regions and operations performed on a bucket.
  • Data egress and ingress: Outgoing network traffic (egress) is chargeable in most cases. It’s important to be aware that if you transfer data between two VMs, the traffic is classed as egress traffic when it leaves the first VM and ingress traffic when received by the second VM. Ingress traffic is not charged. However, the resources processing the traffic may be chargeable. It’s important to plan data transfers carefully to take into account the charging rules. For example, if you transfer data between two VMs in the same zone and use the internal IPs, you won’t be charged. If you use an external (internet-facing) IP, you’ll be charged because the data will be routed over the internet.
  • Data egress when transferring to users: Google uses a tiered pricing system for transferring data between users and Google Cloud instances. The Premium tier, which is active by default, uses Google’s points of presence to provide the fastest service but costs more. The Standard tier uses public internet infrastructure, making it less costly but meaning Google has less control over the speed of the transfer.
  • Cross-regional transfer fees: Transferring data to Google Cloud instances in a different region is chargeable by volume.
  • Databases, CDNs, and other services: CDN services are priced based on the amount of data sent to/from the cache and the number of requests. The way databases are priced depends on the type of instance. Some are priced based on the number of CPUs and total memory. Shared-core instances are priced based on the length of time the instance is running. 

The above assumes the resources being used are on the pay-as-you-go tier. Some of the resources listed above are available on the free tier. However, usage limits on the free tier are very low. 

Importance of Cost Optimization in Cloud Usage

The complexity of Google Cloud’s pricing and the difficulty of estimating your usage on a month-to-month basis means it’s important to carefully monitor your usage and take steps to optimize your cloud deployments. Simple changes, such as setting usage limits and shutting down idle resources, can save your organization a lot of money in the long term.

Security breaches, software bugs or simple administrative errors can easily cause issues that can lead to higher-than-expected bills. Google provides tools to help you avoid going over budget, giving you the chance to diagnose the cause of any cost anomalies and fix them. 

Introducing the Google Cloud Pricing Calculator

The Google Cloud Pricing Calculator is a free tool provided by Google that’s kept up to date with the latest pricing changes. The pricing calculator is an invaluable resource for anyone who hosts data or applications on Google Cloud.

You can access the pricing calculator online, and you don’t need to be an active Google Cloud user to take advantage of it, so you can use the tool to estimate the cost of running your chosen services on Google Cloud versus other cloud platforms or even on-premises.

  • The pricing calculator is easy to use and offers several handy features, including:
    • Instant estimates for pricing based on instance type, size and configuration
    • Coverage for all services, including storage, compute, databases, machine learning, etc.
    • Easy access to alter usage patterns, add and remove VMs and licenses, and see how that affects your bills
    • Estimates in your local currency
    • Regular updates as pricing changes
    • The ability to compare costs across regions
    • API or export estimates to be used as a CSV file

Using these tools you can get an at-a-glance overview of the likely costs of porting your workloads to the cloud or how any changes to your workloads or Google’s pricing will impact you.

Navigating the Google Cloud Pricing Calculator

The Google Cloud Pricing Calculator is split into several tabs representing different types of service, such as Compute, Cloud Workstations, and Storage. Each tab then asks for details about the service in question and how you’re expecting to use it. The information you provide here will be used to calculate cost estimates.

Step-by-Step Guide to Using the Pricing Calculator

  1. Open the calculator and select the service you’re interested in from the list of icons at the top.
  2. You’ll be shown a list of questions relating to the service. In this example, we’ve selected Cloud TPU:
    1. Choose the number of chips, generation, and class.
    2. Select the location for the instance.
    3. Enter the number of hours per day you expect the instance to be running.
    4. Click “Add to Estimate.”
  3. If you wish to add another service, such as storage, repeat this process.
  4. Each time you click “Add to Estimate,” the estimated costs for the service will be shown on the right-hand side of the screen.
  5. If you’d like to change the details of any estimate, click the “Edit” button under the product’s listing in the Estimate selection.
  6. You can change the currency the estimate is provided in by selecting the currency dropdown at the top of the estimate table.
  7. Some products offer sustained use discounts or are included in the free tier. Where this is applicable, there will be a checkbox asking whether you wish to include these discounts in your cost estimates.
  8. If there are forthcoming pricing changes, you’ll be alerted to this and shown an estimate for the price under the revised pricing structure as well.

The more information you provide, the more accurate the estimates will be. Try to have information about your usage habits available before sitting down to use the calculator.

How to Create and Save Different Scenarios for Comparison

Once you’ve picked the products you’re considering and configured them, you can save or share the estimate for future use. At the bottom of the pricing estimate list on the right-hand side of the screen, you’ll find links to print or email the estimate, copy the URL, or download the estimate as a CSV, which you can then use in other applications to create charts/compare costs in a spreadsheet. If you’re comparing prices for different products or configurations, saving the URL of each estimate makes it easy to flip between them in different browser tabs.

Exploring Advanced Settings for Accurate Estimates

As you work through the options in the pricing calculator, you’ll see that you’re required to provide answers to many of the questions, such as the number of instances and the machine types for the Compute Engine product. However, there are many details that can be left blank or on the default option.

To get the most accurate estimates, it’s important to fill out the pricing calculator with as much information as possible. If you’re planning a migration to the cloud you may not know exactly how many requests you’re likely to send or how much data you’ll transfer, but try to use your best guess. If you already have workloads, you can take information from your admin panel to provide a clearer picture of your usage.

Real-World Examples and Use Cases

Let’s consider some real-world examples of projects that might operate in the cloud.

Example 1: Launching a Web Application

Let’s consider the example of a company that wants to beta test a relatively small browser game in the cloud. The application uses a nginx and a PostgreSQL database.

  • The developers think a single, general-purpose E2 compute engine with 64GB RAM, running Ubuntu Server would suffice for nginx. Because they’re just testing the app, they decided not to add persistent disks at this stage.
  • They choose a db-standard-1 Cloud SQL instance with the minimum option of 10GB storage.
  • The app is in the beta testing phase, and the users are all from within Europe, so they purchase some cloud CDN egress within Europe.

With these relatively small requirements, the pricing tool gives estimated fees of $448.46 per month. The bulk of this cost is for the compute engine. This is more than the small development team expected to pay, but upon reviewing the results of their local stress test, they see they don’t stress the processor or memory of the server even with a relatively high number of user actions being performed per second.

They halve the size of the E2 Compute Engine, and the estimated costs fall to $252.78 per month, giving them some room in the budget to add persistent storage and additional Cloud CDN usage for other regions. Because it’s easy to scale cloud deployments, the team doesn’t have to worry about over-provisioning the nginx server machine during the early stages of the project.

Example 2: Data Analytics Project

Google Cloud offers many tools and resources for data analytics, and at Veeam, we’ve worked with a variety of organizations that process huge volumes of data, including major international retailer Carrefour. This retailer uses a hybrid cloud setup including on-premises and Google Cloud systems to manage everything from customer data to product data and logistics, and uses Veeam tools for backup and recovery, as well as IT monitoring and analytics. Switching to Veeam for their backup services shortened their backup window and offered cost savings compared to using snapshots for long-term storage.

Google makes it possible for organizations in a variety of industries to carry out analytics tasks, using tools such as BigQuery or Dataflow. BigQuery is a serverless analytics tool that can ingest data from a variety of sources, including cloud storage buckets. BigQuery On-Demand charges based on the volume of streaming performed, and there are fees for active storage, based on the amount of data being stored and modified in any given 90-day period. BigQuery BI is charged based on memory capacity and usage time.

Using the Google Cloud Pricing Calculator, organizations can determine the best options for where and how to store the data they’re using with BigQuery and any machine learning or BI tools they choose to use alongside it. Because many of these tools are charged based on the number of queries or operations performed, finding ways to optimize your code, such as loading a block of data once and performing actions in a table in RAM, could result in significant cost savings.

Tips for Cost Optimization

Optimizing your Google Cloud workloads requires careful planning:

  • Choose the correct instance for the workload you’re running. Scale up when, and only when, you need more computing power/storage.
  • Use preemptible instances for noncritical workloads.
  • If you know you’re going to be running an instance long term, take advantage of the sustained use discounts, which can be up to 70%.
  • Use compression to minimize storage/transfers where possible.
  • Optimize any code you run in the cloud to keep chargeable operations at a minimum, and ensure processes shut down gracefully once they’ve completed.
  • Instead of storing snapshots for long periods, create a backup from a snapshot, and store the backup on a cheaper object storage tier.
  • Use Google Cloud’s Cost Management tools to automate resource management.
  • Set alerts and alarms for usage patterns outside of the norm.
  • Review your instances regularly and downsize/shut down anything that’s not actively being used.
  • Review your cloud workloads when prices change to see if there are any changes you could make that would save money.
  • Try to route traffic within Google’s network rather than the public internet to reduce data transfer costs where appropriate.

Keeping Up With Pricing Changes

The prices for Google Cloud services are reviewed regularly. Google offers notice of pricing changes to give users time to adapt their usage if required. To avoid being caught out by any pricing changes, it’s a good idea to regularly review current prices and cost estimates. Even if Google hasn’t changed its prices recently, your usage may have changed, especially for user-facing applications.

Subscribing to the official Google Cloud Newsletter is a good way to get the latest news about any changes to the platform. In addition to the official newsletter, the independent GCP Weekly newsletter is a good source of news and information about the platform, providing highlights of recent events and important news.

Case Studies: Real-World Cost Savings With the Pricing Calculator

Even major tech-focused brands often find it difficult to manage the costs of their cloud deployments. Zero-code app development company Apxor cut their costs by 30% when they made the switch from their previous cloud partner to Google Cloud while also benefiting from some significant performance improvements. The company used the switch as a chance to optimize its workloads, which include Compute Engine, Dataflow, and TensorFlow tools.

Arabesque AI, a financial asset management company, saw even greater savings. The company slashed its cloud bills by 75% through the use of preemptible instances and dynamic scaling. This allowed them to use pay-as-you-go instances for maximum flexibility, maintaining high performance but without paying more than they needed to for the workloads they were running.

Conclusion

Google Cloud is a useful platform for organizations looking to migrate to the cloud. The variety of IaaS and PaaS products available on the platform makes it versatile and powerful. However, the pricing structure can be confusing. Learning how to estimate pay-as-you-go pricing with the Google Cloud Pricing Tool can help you manage your costs and decide whether a move to long-term pricing makes sense for your organization. 

With careful planning, it’s possible to optimize your resource usage and keep your bills to a minimum. If you choose to go this route, make an effort to stay up to date with any announcements from Google regarding pricing changes. Try to follow best practices for security to reduce the risk of a breached account running up a significant bill. In addition, be sure to try Veeam Backup for Google Cloud, which offers free backups for up to 10 instances and powerful tools for hybrid and multi-cloud environments, helping you protect your valuable data.

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