Google Compute Engine (GCE) : Pricing & Cost Optimization Guide

Google Compute Engine (GCE) is a powerful and flexible cloud computing platform that provides scalable solutions for businesses of all sizes. With a wide range of services, GCE can cater to different needs and budgets.

However, with great power comes great complexity, and GCE is no exception. The multitude of services and pricing options can make it challenging for organizations to fully understand and utilize the platform’s potential.

This article aims to provide an overview of GCE pricing, including the different factors that impact costs, and offer some cost-optimization recommendations that organizations can implement to manage expenses effectively. Whether you are a small startup or a large enterprise, understanding the GCE pricing model is critical for making informed decisions about your cloud infrastructure needs.

What is Google Compute Engine?

Google Compute Engine (GCE) is a service offered by Google Cloud Platform (GCP) that enables users to launch and manage virtual machine instances on Google’s infrastructure. In simpler terms, it’s a cloud computing platform that allows users to create and run virtual machines in the cloud.

GCE is designed to solve the problem of scalable computing for businesses of all sizes. It provides access to powerful virtual machines that can be launched quickly and scaled up or down as needed. GCE allows users to create custom machine configurations, choose from a variety of preconfigured images, or bring their own custom images to run in the cloud.

What is Google Compute Engine used for?

Google Compute Engine is a powerful tool that can be used for a variety of purposes. Here are some common use cases:

  1. Running applications in the cloud: GCE allows users to run their applications in the cloud, which means they can access their applications from anywhere with an internet connection.
  2. Development and testing: GCE provides an environment where users can develop and test their applications without having to worry about managing infrastructure.
  3. Big data analytics: GCE can be used to run big data analytics workloads, allowing businesses to process large amounts of data quickly and efficiently.
  4. Gaming and media streaming: GCE can be used to host online games and media streaming services, providing a high-performance and scalable platform for these types of applications.

Google Compute Engine, GCE, Pricing, Cost Optimization, Tiers, GCP, Google Cloud Platform, Services, Features

Google Compute Engine Pricing Tiers

Google Compute Engine (GCE) offers various pricing options to its users. This includes on-demand pricing, spot pricing, and resource-based commitment pricing. These pricing options provide flexibility for organizations to choose the best pricing model that meets their needs and budget.

On Demand Pricing
The on-demand price is a flexible pricing model that allows users to pay for only the resources they use on an hourly basis. There is no upfront cost, and users can stop or start the service at any time. This pricing model is ideal for organizations that need to quickly scale their computing resources up or down. This model works best for workloads with unpredictable traffic patterns, such as development and testing environments.

  • Suppose a user needs to run a virtual machine (VM) for a short period, say 4 hours, and chooses the n1-standard-4 instance type that costs $0.160 per hour. With on-demand pricing, the total cost would be $0.160 x 4 = $0.64.

Spot Pricing (Spot VMs)

The spot price model is designed for businesses that have flexible workloads and can afford to have their workloads interrupted if the market price for resources exceeds a specified price limit. With this model, users can bid on unused resources, and if the bid is accepted, they can use those resources at a reduced rate. The spot price is usually significantly lower than the on-demand price.

  • Now, let’s say the same user wants to run the same VM for 24 hours, but they are willing to tolerate some downtime. If they opt for the spot pricing option, they can bid for an idle capacity that Google has available and get a significant discount. Suppose the current spot price for the n1-standard-4 instance is $0.060 per hour. If the user bids for $0.080 per hour, they can save $0.080 – $0.060 = $0.020 per hour, which adds up to $0.480 for 24 hours

Committed Use Discounts (CUDs)

Long-term committed use discounts offer cost savings for organizations that can commit to using computing resources for one to three years. This pricing model requires a commitment to use a certain amount of computing resources for a specified period. The longer the commitment and the more resources committed to, the greater the discount. Long-term committed use discounts are ideal for organizations with stable workloads, such as production environments.

  • Finally, suppose the user knows that they will need a VM of the same type for the next year, and they want to commit to using it. They can choose the 1-year committed use option and get a discount of up to 57% off the on-demand price. For example, if they commit to using the n1-standard-4 instance for 24 hours per day, 7 days a week, for the next year, the total cost would be $0.068 x 24 x 365 = $596.16, which is a savings of $820.64 compared to on-demand pricing.

Google Compute Engine Pricing Table

The GCE pricing table is a straightforward way for users to get an idea of the cost of using different machine types on the platform. However, it is important to note that the table does not include additional cost factors (storage, network, data transfer) that may impact the overall cost of using GCE.

Machine Type vCPUs Memory On-demand (hourly) Spot price (hourly) 1-year commitment price 3-year commitment price
C3 (General Purpose)
vCPUs $0.03827 $0.008798 $0.022907 $0.01531
Memory $0.005129 $0.001179 $0.00307 $0.002052
C2 (Compute Optimized)
vCPUs $0.03827 $0.00879 $0.0229 $0.01531
Memory $0.00512 $0.00117 $0.00307 $0.00205
vCPUs $0.0392322 $0.00784 $0.02315 $0.01177
Memory $0.0057592 $0.00116 $0.0034 $0.00173
a2-highgpu-1g 12 85GB $3.673477 $1.101843 $2.313845 $1.285841
a2-highgpu-2g 24 170GB $7.346954 $2.203686 $4.62769 $2.571681
a2-highgpu-4g 48 340GB $14.693908 $4.407372 $9.25538 $5.143362
a2-highgpu-8g 96 680GB $29.387816 $8.814745 $18.51076 $10.286724
a2-megagpu-16g 96 1360GB $55.740976 $16.719093 $35.10968 $19.5113
e2-micro 2 1GB $0.009433 $0.002829 $0.00594 $0.00425
  • To view GCE pricing in more detail, users can visit the pricing page on the Economize website. This page includes more detailed information about GCE pricing, as well as additional cost factors that users should consider.|
  • For GCP users, it is recommended to use the pricing calculator to get an accurate estimate of the entire project cost. The pricing calculator takes into account additional cost factors and can provide a more accurate estimate of the overall cost of using GCE.

Google Compute Engine (GCE) Cost Optimization Recommendations

While GCE offers several benefits such as scalability, reliability, and performance, it is important for businesses to keep an eye on their expenses to ensure that they are not overspending on resources. The following recommendations can help you save costs on your GCE usage.

  1. Right-size your resources: One of the most effective ways to optimize costs on GCE is to right-size your resources. This means selecting the appropriate machine type for your workload, ensuring that you are not paying for resources that you don’t need. You can use the GCE machine types calculator to estimate the costs of different machine types and choose the one that best fits your needs.
  2. Use preemptible VMs: Preemptible VMs are a cost-effective option for running short-lived and batch processing jobs. These VMs are available at a significantly reduced price compared to regular VMs, but they can be terminated at any time if Google needs the resources for other workloads. If your application can tolerate occasional interruptions, preemptible VMs can save you a considerable amount of money.
  3. Use Auto Scaling: Auto scaling is a feature that allows you to automatically adjust the number of VMs based on the demand of your workload. This ensures that you are not paying for idle resources, and you can avoid the cost of over-provisioning. You can set up auto scaling policies to adjust the number of VMs based on CPU utilization, network traffic, or other metrics.
  4. Use Sustained Use Discounts (SUDs): GCE offers sustained use discounts for VMs that run for a significant portion of the billing month. If you run VMs for more than 25% of the month, you can get up to a 30% discount on the hourly usage cost. This discount applies automatically and can help you save money on long-running workloads.
  5. Use Managed Instance Groups: Managed instance groups allow you to group multiple instances together and manage them as a single entity. This makes it easy to scale instances up or down, apply updates, and distribute traffic. Using managed instance groups can help you optimize costs by allowing you to run instances only when needed and avoiding over-provisioning.
  6. Use Preemptible GPUs: Similar to preemptible VMs, preemptible GPUs are available at a reduced cost compared to regular GPUs. If your application can tolerate occasional interruptions, using preemptible GPUs can be a cost-effective way to run workloads that require GPU resources.
  7. Use committed use discounts: GCE offers committed use discounts for customers who commit to using a certain amount of resources for a one or three-year term. This can be a good option if you have predictable usage patterns and want to save money on your overall compute costs.
  8. Use Google Cloud Platform credits: Google offers free credits to new customers who sign up for their cloud platform. You can use these credits to try out different GCP services, including Compute Engine, and get a feel for the platform before committing to long-term usage.


Google Compute Engine provides a flexible and scalable cloud computing platform that allows businesses to run their applications and workloads with ease. It offers a wide range of virtual machine instances, storage options, and networking capabilities that cater to the diverse needs of businesses of all sizes.

However, it is important to note that cost optimization is an ongoing process that requires constant monitoring and adjustment. By following the best practices outlined in this article, businesses can reduce their cloud infrastructure costs and maximize their ROI on Google Compute Engine