GCP Cost Optimization: 7 Essential Tactics for 2025

Like other major cloud providers, Google Cloud Platform (GCP) offers a wide range of services that are easy to provision but challenging in terms of cost management. This is where GCP cost optimization tactics come in handy.

This article dives into best practices for controlling and reducing GCP costs to help you prevent any surprises on your cloud bill and maximize ROI from Google’s cloud offerings.

1. Understand GCP pricing

Pay-as-you-go

In this pricing scheme, you only pay for the resources you utilize. Google Cloud Platform will add each hour of computing capacity your team uses to the final monthly fee.

You’re not facing any long-term commitments or upfront fees, so there’s no risk of overcommitting to the cloud provider and paying for resources you won’t use. In addition, you can adjust your consumption at any moment.

Pay-as-you-go is flexible, but managing your team’s monthly resources is crucial to avoiding exceeding your budget.

Committed use discounts

Committed use discounts (CUDs) are available in two types: resource-based and spend-based.

Resource-based CUDs provide a discount if you agree to use a minimum amount of Compute Engine resources in a specified region for predictable and steady-state workloads. Furthermore, CUD sharing allows you to spread the discount across all projects associated with your billing account.

Spend-based CUDs, on the other hand, provide a discount to customers who agree to spend a minimum amount ($/hour) on a Google Cloud product or service. This solution was created to help teams generate predictable expenditures, measured in dollars per hour of similar on-demand spending. It operates similarly to AWS Savings Plans.

Limitations of CUDs

In a resource-based scenario, CUD will prompt you to commit to a specific instance or family.

In a spend-based CUD, you risk committing to a level of spending for resources that your organization may no longer require after a few months.

In both cases, you risk becoming locked in with the cloud provider and committing to pay for services that may no longer be relevant to your business in one or three years.

When your compute requirements change, you must commit to even more capacity or risk dealing with idle capacity. Committed use savings eliminate the flexibility and scalability that drove you to the cloud in the first place.

For further information, see GCP CUD: Are There Better Ways to Save on the Cloud?

Sustained use discounts

Sustained use discounts are automatic discounts that users receive for incremental consumption after operating Compute Engine resources for a significant portion of a billing month. The longer you run these resources constantly, the greater the potential discount on additional utilization.

Spot virtual machines

In this cost-effective pricing approach, you bid on compute resources that Google Cloud hasn’t sold to other customers, saving up to 91%. However, the provider can turn off the service with 30 seconds warning, so you must have a plan and tools to cope with such interruptions.

Spot VMs are a good pick for workloads that can withstand interruptions. 

2. Use Google’s Billing And Expense Management Tools

Because of the unpredictable nature of the cloud and technologies like Kubernetes, prices might sneak up on you if you stop paying attention, even for a moment.

This is why Google Cloud offers a comprehensive set of free invoicing and cost management tools, which provide you with the visibility and insights you need to manage your cloud setup.

The Cost Management tool helps you gain insight into your existing cost patterns, and controls your costs via financial governance policies and permits. Billing reports give you a quick overview of your charges and help answer questions like, “Which team generated this cost item?”

You should also learn how to use labels to ascribe costs to departments or teams and create custom dashboards for more detailed cost views. 

Quotas, budgets, and warnings are useful for monitoring and anticipating cost patterns over time, lowering the danger of overspending.

3. Track these four cost metrics

Before you invest in a cost-monitoring service, ensure that it includes the following metrics:

• Real-time costs – GCP, like other cloud providers, typically serves cost data with a delay. However, if your teams use dynamic cloud-native technologies like Kubernetes, you’ll require real-time cost data to make the right decision.
• Daily cloud spend – This metric is critical for swiftly assessing your budget burn rate and determining whether you’ll exceed your monthly budget. Imagine that you have set a $2,000 monthly budget. If your average daily cost is closer to $90 than $66.60 (30 days x $66.60 = $1998), your cloud bill will be bigger than budgeted for.
• Cost per provisioned vs. requested CPU – By examining the difference between these two metrics, you can see how much you’re spending per CPU and increase the accuracy of your cost reporting.
• Historical cost allocation – If you go over your cloud budget, you should know why. This report helps you determine the source of additional expenditures.

4. Select the appropriate VM type and size

Define your workload’s requirements

The first step is to determine how much capacity your workload requires across the following compute dimensions:

• CPU count and architecture
• Memory 
• Storage
• Network

You must guarantee that the virtual machine’s size meets your requirements. Did you find a cheap virtual machine? Consider what would happen if you started running a compute-intensive task on it and encountered performance issues that would affect your users.

Consider your use case as well. For example, choose a GPU-based virtual machine that accelerates the training process if you want to train a machine learning model.

Select the optimal VM type for the job 

Google Cloud provides a variety of VM types to suit a wide range of use cases, with completely different CPU, memory, storage, and networking configurations. Each category is available in one or more sizes, allowing you to scale your resources effortlessly.

However, providers deploy various machines for their VMs. Chips in those devices may have varying performance characteristics. As a result, you may find yourself selecting a type with exceptional performance that exceeds your resource requirements.

Understanding and quantifying all of this is difficult. Google Cloud includes four machine families with various machine series and types. Choosing the proper one is like sifting through a haystack to discover the needle you need.

Investigate your storage transfer limits

Data storage is a crucial GCP cost optimization factor since each application has unique storage requirements. Check that the machine you chose has the storage throughput your workloads require.

Also, avoid pricey disk solutions like premium SSDs unless you intend to use them to their maximum potential.

5. Use Spot virtual machines

Check if your workload is ready for Spot VMs

Spot VMs promise to save up to 91% on your GKE bill compared to pay-as-you-go pricing. 

But before you transfer all of your workloads to Spot VMs, determine whether your workloads can operate on them in the first place. You don’t want to be caught off guard when Google Cloud reclaims these compute resources, leaving you 30 seconds to devise plan B.

Here are some questions to ask while assessing your workload:

• How long does it take to do the job?
• Is it mission- or time-critical?
• Can it handle interruptions gracefully?
• Is there a tight coupling between nodes?
• How will you transfer your workload to another compute instance when Google pulls the plug?

Choose your Spot VMs

When choosing a Spot VM, look among the slightly less popular options. It’s simple: they’re less likely to be interrupted. Check the number of interruptions for your Spot VM candidate – it’s the pace at which this instance reclaimed capacity last month.

Use groups

Set up groups of Spot VMs to request many machine kinds at once and increase your chances of getting the ones you want. When more resources become available, managed instance groups generate or add new Spot VMs.

If you manage spot VMs manually, be prepared for an extensive configuration, setup, and maintenance effort.

Fortunately, there’s another option: automation.

PlayPlay, a video SaaS company, used our automation solution to handle the entire Spot VM lifecycle, from selection and provisioning to management and decommissioning. This allowed PlayPlay to reduce cloud costs by 40% on average across its workloads while increasing DevOps productivity.

6. Take advantage of autoscaling

Scaling resources up and down to match demand fluctuations is another smart move. If you use Kubernetes on Google Cloud, you’re already looking at three potential autoscaling mechanisms.

The tighter you set your Kubernetes scaling mechanisms, the less waste and expenses your application will generate. Check out this article for a more in-depth exploration: Guide to Kubernetes Autoscaling for Cloud Cost Optimization

Here are a few recommendations to help you get the most out of Kubernetes autoscaling:

Ensure that the HPA and VPA policies do notoverlap

Vertical Pod Autoscaler changes the number of pods and restrictions depending on the target average CPU use, cutting overhead and lowering costs. Horizontal Pod Autoscaler seeks to scale out rather than up.

Double-check that the VPA and HPA regulations aren’t in conflict. Also, keep an eye on your binning and packing density settings when creating clusters for a business—or purpose-class service tier.

Consider instance-weighted scores

When autoscaling, use instance weighting to allocate a certain amount of your resource pool to a specific task. This guarantees that the devices you design are ideal for the workload.

Use aixed-instance strategy

A mixed-instance strategy can help you achieve high availability and performance while being cost-effective. You may pick from various instance types, some of which may be less expensive and suitable for lower-throughput or low-latency tasks.

Mixing instances in this fashion can help you save money because each node requires Kubernetes installation, which adds some overhead.

But how can you scale mixed instances? In a mixed-instance environment, each instance makes use of a distinct resource. So, when you scale instances in autoscaling groups and utilize metrics such as CPU and network use, you may receive conflicting results from various nodes.

To avoid these discrepancies, utilize the Cluster Autoscaler to build an autoscaler configuration using custom metrics. Also, ensure all your nodes have identical CPU cores and memory capacity.

7. Use an automation tool for GCP cost optimization

Manual cost management and cloud monitoring are only effective up to a limit. They involve a lot of work hours, and human errors might jeopardize your availability or performance.

Teams serious about cost reduction go beyond cost monitoring and implement a fully automated GCP cost optimization solution that automatically maintains and deploys cloud resources while balancing cost and performance.

A good automation tool should include the following features:

• Automated virtual machine selection and rightsizing – The platform chooses the best-suited kinds from hundreds of VM types and sizes, reducing the time-consuming selection and provisioning processes.
• Autoscaling computing resources – It continuously assesses application demand and scales cloud resources up or down for optimal performance at the lowest cost, scaling down to zero and eliminating VMs when no work is required.
• Spot VM automation – With automation, GCP users can use Spot VMs with confidence that their workloads have a place to run on in case of interruptions.

Optimize GCP costs using automation

GCP cost optimization solutions that leverage automation save time and money by dramatically decreasing the effort necessary to control cloud expenses. These solutions allow you to add new features to your applications rather than micromanaging the cloud infrastructure.

Connect your cluster to the CAST AI platform and do a free cost study to receive a thorough cost breakdown and recommendations – while you dedicate your time to more creative problem-solving.