Overprovisioning and leaving cloud resources on are two enormous sources of wasted spend.
Wasted spend drags down IT budgets – of particular importance as we enter 2021. The Flexera 2021 State of Tech Spend report found that the biggest change in key IT initiatives from 2020 to 2021 was in cost savings, with the percent of respondents ranking cost savings as a top initiative tripling year-over-year.
It’s important that this is being recognized. Based on data collected by Gartner, we estimate that wasted spend will exceed $26.6 billion this year.
Where the Wasted Cloud Spend is Coming From
Gartner estimates a total market spend of $304 billion on public cloud services end-user spending in 2021, as broken out in the table below. Their estimate for the proportion of that spent on Infrastructure as a Service (IaaS) is $65.3 billion. While wasted spend can be found in any area of cloud spend, customers tend to see the largest amount in these two areas, as well as finding it easiest to identify.
Cloud resources can be considered “idle” when they are running while not being used. For example, when development servers are left running overnight and on weekends when they’re not needed. Since compute resources are paid for by the minute or second, that’s a large portion of the week they’re being paid for but not used (and yes, this applies even if you have reservations.)
Our data shows that about 44% of compute spend is on non-production resources. If we estimate that non-production resources are only needed during a 40-hour work week, the other 128 hours (76%), the resources are sitting idle.
Applying that to the Gartner IaaS number, we estimate that up to $14.5 billion will be wasted on idle resources this year.
Overprovisioning occurs when a larger resource size is selected than is actually needed. There is a mindset of safety behind this, as of course, no one wants their applications to be under-resourced.
But the overprovisioning occurring is far beyond what is necessary, given the elasticity of the cloud. About 40% of instances are sized at least one size larger than needed for their workloads. The cost can be cut in half by reducing an instance by one size, while downsizing by two sizes saves 75%.
Many of our customers show a large percentage of their resources are oversized, but bringing this to a conservative estimate of 40% of resources oversized by one size, giving us a savings per resource of 50%, we estimate that up to $8.7 billion is wasted due to overprovisioning.
Orphaned Volumes and Snapshots
Another significant source of waste is orphaned volumes and snapshots. These are resources that have been detached from the infrastructure they were created to support, such as a volume detached from an instance or a snapshot with no volume attachment.
Our customers spend approximately 15% of their bills on storage, and we found that about 35% of that spend is on unattached volumes and snapshots. Applying that to the Gartner spending numbers, we estimate that up to $3.4 billion could be wasted this year on orphaned volumes and snapshots.
Reducing Wasted Spend
Altogether, this gives us an estimate of $26.6 billion to be wasted on unused cloud resources in 2021. This waste estimate is just based on the three prominent sources of cloud waste. It does not include wasted spend on Platform as a Service (PaaS), which makes up $55 billion in cloud spend according to Gartner’s estimates, nor from SaaS, unused reservation commitments, inefficient containerization, and other areas of the bill.
Attacking the three problem areas above is a great area to start for nearly all public cloud users. Here at ParkMyCloud, we’re on a mission to do just that. See how and try it out today, to do your part in reducing wasted cloud spend.
If you’re looking to break into the cloud computing space, or just continue growing your skills and knowledge, there are an abundance of resources out there to help you get started, including free Google Cloud training. If you know where to look, open-source learning is a great way to get familiar with different cloud service providers.
With the combined knowledge from our previous blog posts on free training resources for AWS and Azure, you’ll be well on your way to expanding your cloud expertise and finding your own niche. No matter where you are in the learning process, there are training resources for every experience level and learning type – get started learning now with these 7 free Google Cloud Platform training resources:
1. Google Cloud Free Program
For free, hands-on training there’s no better place to start than with Google Cloud Platform itself. Within the Google Cloud free program you’ll have two options – sign up for a free trial or free tier. As a new Google Cloud customer, you can get started with a 90-day free trial. If you’re already a Google Cloud customer and are looking for a free option, you can sign up for Google Cloud’s free tier. GCP’s free program option is a no-brainer thanks to its offerings.
Access to all GCP products. You’ll have everything you need to experiment with building and running apps, sites, and services. Firebase and the Google Maps API are included with your free trial.
$300 credit is yours to spend for the next 90-days, an expansion from their previous 60-day period and a sizable offer in comparison to Azure’s $200 for 30 days, so take advantage.
No autocharges after the trial period ends – a rarity for most free trials, and a guarantee that this training resource is 100% free.
An always-free option. GCP’s free tier takes the cake with this an always-free tier that gives you enough power to run a small app despite limitations on product and usage. Free tier customers can use select Google Cloud products free of charge, with specified monthly usage limits, making this a perfect option for learning purposes.
For help with navigating the platform as you use it, check out GCP’s documentation for a full overview, comparisons, tutorials, and more.
On the Google Cloud training page, you’ll find plenty of classes tailored to your interests or role so you can get technical skills and learn best practices for using the platform. As another free Google Cloud training option, Google has also teamed up with Coursera, an online learning platform founded by Stanford professors, to offer courses online so you can “skill up from anywhere.”
Coursera includes a number of free courses including topics in Machine Learning, Architecting, Data Engineering, Developing Applications, and the list goes on.
In conjunction with Coursera, Google Cloud offers hands-on training with specialized labs available via Qwiklabs, a learning lab environment for developers. Choose a “quest” from their catalog and get started with 50+ hands-on labs from beginner to expert level. Here you’ll learn new skills in a GCP environment and earn cloud badges along the way. Get started with GCP Essentials and work your way into more advanced, niche topics like Managing Cloud Infrastructure with Terraform, Machine Learning APIs, IoT in Google Cloud, and so on.
4. Plural Sight
Pluralsight is a technology skills platform that offers a full breadth of Google Cloud courses, learning paths, and skills assessments. You’ll find several Google Cloud resources to help level up your skills. If you’re looking to dive deeper into Google Cloud, this is a great option – get started learning with a free trial and make sure to keep an eye out for training discounts offered by Google.
GitHub provides users a number of materials that can help further your Google Cloud training. The great thing about this platform is collaboration among the users, this community brings together people from all different backgrounds so they are able to provide knowledge about their own specialties and experiences. Here’s a great list of Google Cloud training resources that can help you.
You can never go wrong with YouTube. With an endless amount of free videos, YouTube offers an abundance of Google Cloud training options for those of you who prefer to watch the movie instead of reading the book (you know who you are). Some of the most popular YouTube channels for free Google Cloud Platform training include:
Google Cloud Platform (640k subscribers) – “helping you build what’s next with secure infrastructure, developer tools, APIs, data analytics and machine learning.”
Edureka (2.29M subscribers) is a full-service, online learning platform with curated content in Big Data and Hadoop, DevOps, Blockchain, AI, Data Science, AWS, Google Cloud, and more. Their YouTube channel is a “gateway to high-quality videos, webinars, sample classes and lectures from industry practitioners and influencers.” If you’re jumping into GCP with no prior knowledge or experience, the What is Google Cloud Platform tutorial will help get you started.
7. Blogs & Forums
Blogs are a great way to keep your mind flowing with new insights, ideas, and the latest on all things cloud computing. Google Cloud and Qwiklabs have blogs of their own, perfect for supplemented reading with their trainings. For a more well-rounded blog with content on other service providers, check out Cloud Academy.
Take Advantage of These Free Google Cloud Training Resources
It is clear that cloud computing is here to stay and as cloud technology continues to grow and advance, free training resources only continue to emerge so it’s important to stay up to date on new resources. We picked the 7 above for their reliability, variety, quality, and range of information. With the current working remote culture, this is the perfect time to take advantage of free google cloud training online. Whether you’re new to Google Cloud or consider yourself an expert, these resources will expand your knowledge and keep you up to date with what’s latest in the platform.
Every year, an exorbitant amount of money is wasted on idle cloud resources. That is – resources that are provisioned, and being paid for, but not actually being used. This is a huge problem that clogs up cloud environments and drains budgets.
Note: a version of this blog was originally published in 2018. It has been completely updated and rewritten for 2020.
Even the Cloud Providers are Talking About It
The issue of idle resources is something that is recognized even by the cloud providers themselves. This may sound counterintuitive. Doesn’t AWS just want as much money from you as it can get? Well, maybe, yes: but the best way for them to do this is by providing you with a positive experience and the most value for your money.
Case in point: at the AWS re:Invent keynote this week, Andy Jassy spoke about a few core guidelines for organizations to follow to ensure organizations are on the path for successful technology financial management. “Start early and start small…The key is to start experimenting with what matters the most to your organization” Jassy said. He shared that a great place to start is by deleting or stopping idle resources in your cloud environment. Small changes like this can have huge impacts and benefits can increase as time goes on. Idle resources are eating at your cloud budget causing you to spend money on resources that aren’t even being used.
AWS’s cloud financial management framework mentions this among the myriad ways your organization can improve practices to reduce usage waste and optimize costs.
The Cost of Idle Resources
The typical “idle resources” that come to mind are instances purchased On Demand that are being used for non-production purposes like development, testing, QA, staging, etc. These resources can be “parked” when they’re not being used, such as on nights and weekends, saving 65% or more per resource each month. In order to fully understand the problem of idle cloud resources, we have to expand this scope beyond just your typical virtual machine.
Most non-production resources can be parked about 65% of the time, that is, parked 12 hours per day and all day on weekends (this is confirmed by looking at the resources parked in ParkMyCloud – they’re scheduled to be off just under 65% of the time.) We see that our customers are paying their cloud providers an average list price of $220 per month for their instances. If you’re currently paying $220 per month for an instance and leaving it running all the time, that means you’re wasting $143 per instance per month.
Maybe that doesn’t sound like much. But if that’s the case for 10 instances, you’re wasting $1,430 per month. One hundred instances? You’re up to a bill of $14,300 for time you’re not using. And that’s just a simple micro example. At a macro level that’s literally billions of dollars in wasted cloud spend.
4 Types of Idle Cloud Resources
So what kinds of resources are typically left idle, consuming your budget? Let’s dig into that, looking at the big three cloud providers — Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
On Demand Instances/VMs – this is the core of the conversation, and what we’ve addressed above. On demand resources – and their associated scale groups – are frequently left running when they’re not being used, especially those used for non-production purposes.
Databases – there’s no doubt that databases are frequently left running when not needed as well, in similar circumstances to the On Demand resources, particularly non-production. The problem is whether you can park them to cut back on wasted spend. AWS allows you to park certain types of its RDS services like Neptune and Redshift databases, RDS instances and Google Cloud SQL. Make sure you review your database infrastructure regularly and terminate anything unnecessary – or change to a smaller size if possible.
Load Balancers – AWS Elastic Load Balancers (ELB) cannot be stopped (or parked), so to avoid getting billed for the time you need to remove it. The same can be said for Azure Load Balancer and GCP Load Balancers. Alerts can be set up in Cloudwatch/Azure Metrics/Google Stackdriver when you have a load balancer with no instances, so be sure to make use of those alerts.
Containers – optimizing container use is a project of its own, but there’s no doubt that container services can be a source of waste. It’s important that you regularly review the usage of your containers and the utilization of the infrastructure, especially in non-production environments. In the last few months, ParkMyCloud has released support for Amazon EKS, Azure AKS and Google Cloud GKE so customers can make sure their idle resources are parked.
Cloud waste is a billion-dollar problem facing businesses today. Make sure you’re turning off idle cloud resources in your environment, by parking those that can be stopped and eliminating those that can’t, to do your part in optimizing cloud spend.
Among the many ways to purchase and consume Azure resources are Azure low priority VMs and Spot VMs. These virtual machines are compute instances allocated from spare capacity, offered at a highly discounted rate compared to “on demand” VMs. This means they can be a great option for cost savings – for the right workloads. And we love cost savings! Here’s what you need to know about these purchasing options.
How Interruptible VMs Work
The great part about both of these options is the price. Depending on the options selected, you can get a discount of up to 90% compared to the pay-as-you-go price. However, this is in exchange for the possibility that these VMs will be “evicted” when Azure needs the capacity, which makes them suitable for fault-tolerant applications such as batch processing, rendering, testing, some dev/test workloads, containerized applications, etc.
Azure Low Priority VMs
There are two key things to know about Low Priority VMs. The first is that they are only available through Azure Batch, Azure’s tool for running large-scale parallel and high-performance computing jobs through a pool of compute nodes (VMs). Through Azure Batch, you can run jobs and tasks across compute pools called “batch pools”. Since batch jobs consist of discrete tasks run using multiple VMs, they are a good fit to take advantage of low priority VMs.
Note that prior to February 2020, Low Priority VMs were available in Azure Scale Sets, but that option has been discontinued, with Spot VMs now available in Azure Scale Sets instead.
The second highlight is that Low Priority VM pricing is at a fixed discount of 60-80% compared to pay-as-you-go.
Azure Spot VMs
As of May 2020, Azure offers Spot instances/VMs in addition to Low Priority VMs. Like Low Priority, the Spot option allows you to purchase spare capacity at a deeply discounted price in exchange for the possibility that your VM may be evicted. You can choose whether or not to have a cap on the price you’re willing to pay for Spot VMs. Unlike Low Priority, you can use the Azure Spot option for single VMs and scale sets. VM scale sets scale up to meet demand, and when used with Spot VMs, will only allocate when capacity is available.
Your Spot VMs can be evicted when Azure needs the capacity, or when the price goes above your maximum price. You can choose to get a 30-second eviction notice and attempt to redeploy.
The other key difference is that Azure Spot pricing is variable, and based on the capacity for size or SKU in an Azure region. Prices change slowly to provide stabilization. The price will never go above pay-as-you-go rates.
When it comes to eviction, you have two policy options to choose between:
Stop/Deallocate (default) – when evicted, the VM is deallocated, but you keep (and pay for) underlying disks. This is ideal for cases where the state is stored on disks.
Delete – when evicted, the VM and underlying disks are deleted.
While similar in idea, there are a few key differences between these two purchasing options:
Single VMs, VM scale sets
Variable pricing; ability to set maximum price
Preempted when Azure needs the capacity. Tasks on preempted node VMs are requeued and run again.
Evicted when Azure needs the capacity or if the price exceeds your maximum. If evicted for price and afterward the price goes below your maximum, the VM will not be automatically restarted.
Azure Extra Capacity Options vs. AWS Spot Instances
So are low priority VMs the same as AWS Spot Instances? In some ways, yes: both options allow you to purchase excess capacity at a discounted rate.
However, there are a few key differences between these options:
Fixed vs. variable pricing – AWS spot instances have variable pricing while Azure low priority VMs have a fixed price as listed on the website
Integration & flexibility – AWS’s offering is better integrated into their general environment, while Azure offers limited options for low priority VMs (for example, you can’t launch a single instance) with limited integration to other Azure services.
Visibility – AWS has broad availability of spot instances as well as a Spot Instance Advisor to help users predict availability and interruptibility. On the other hand, Azure has lower visibility into the available capacity, so it’s hard to predict if/when your workloads will run.
Should You Use Azure Low Priority VMs?
If you have fault-tolerant batch processing jobs, then yes, low priority VMs are worth a try to see if they work well for you. If you’ve used these VMs, we’re curious to hear your feedback. Have you had issues with availability? Does the lack of integrations cause any problems for you? Are you happy with the cost savings you’re getting? Let us know in the comments below.
Microsoft Azure recently announced an addition designed to help with Azure chargeback: cost allocation, now in preview in Azure Cost Management + Billing. We’re always glad to see cloud providers making an effort to improve their native cost management capabilities for customers, so here’s a quick look at this update.
Chargeback for Cost Accountability
Cost allocation for cloud services is an ongoing challenge. Depending on organizational structure and decisions about billing and budgets, every organization will handle it a bit differently. In some cases, separating by Azure subscription can make this easier, but in others, your organization may have shared costs such as networking or databases that need to be divided by business unit or customer. However, it is an obstacle that must be addressed in order for organizations to gain visibility, address inefficiencies, and climb up the cloud spend optimization curve to actually take action to reduce and optimize costs.
Many IT organizations address this via an Azure chargeback setup, in which the IT department provisions and delivers services, and each department or group submits internal payment back to IT based on usage. Thus, it becomes an exercise in determining how to tag and define “usage”.
In some cases, showback can be used as an alternative or stepping stone toward chargeback. The content and dollar amounts are the same – but without the accountability driven by chargeback. For this reason, it can be difficult to motivate teams to reduce costs with a showback. We have heard teams using variation on showback – ”shameback”. IT can take the costs they’re showing back and gamify savings, coupled with a public shame/reward mechanism, to drive cost-saving behavior.
What Azure Added with the Preview Cost Allocation Capabilities
The cost allocation capabilities are currently in preview for Enterprise Agreement (EA) and Microsoft Customer Agreement (MCA) accounts. It allows users to identify the costs that need to be split by subscription, resource group, or tag. Then, you can choose to move them, and allocate in any of the following ways: distribute evenly, distribute proportional to total costs, distribute proportional to either network, compute, or storage costs, or choose a custom distribution percentage.
Cost allocation does not affect your Azure invoice, and costs must stay within the original billing account. So, Azure did not actually add chargeback, but they did add visualization and reporting tools to facilitate chargeback processes within your organization, outside of Azure.
Improvements in the Right Direction – or Too Little, Too Late?
Azure and AWS are slowly iterating and improving on their cost visibility, reporting, and management capabilities – but for many customers, it’s too little, too late. The lack of visibility and reporting within the cloud providers’ native offerings is what has led to many of the third-party platforms in the market. We suspect there is still a way to go before customers’ billing and reporting needs are fully met by the CSPs themselves.
And of course, for organizations with a multi-cloud presence, the cloud costs generally need to be managed separately or via a third-party tool. There are some movements within the CSPs to at least acknowledge that their customers are using multiple providers, particularly on the part of Google Cloud. Azure Cost Management has done so in part as well, with the AWS connector addition to the platform, but it’s unclear whether the 1% charge of managed AWS spend is worth the price – especially when you may be able to pay a similar amount for specialized tools that have more features.
Like other cloud providers, the Google Cloud Platform (GCP) charges for compute virtual machine instances by the amount of time they are running — which may lead you to search for a Google Cloud instance scheduling solution. If your GCP instances are only busy during or after normal business hours, or only at certain times of the week or month, you can save money by shutting these instances down when they are not being used. So can you set up this scheduling through the Google Cloud console? And if not – what’s the best way to do it?
This post was originally written by Bill Supernor in 2018. I have revised and updated it for 2020.
Why bother scheduling a Google VM to turn off?
As mentioned, depending on your purchasing option, Google Cloud pricing is based on the amount of time an instance is running, charged at a per-second rate. We find that at least 40% of an organization’s cloud resources (and often much more) are for non-production purposes such as development, testing, staging, and QA. These resources are only needed when employees are actively using them for those purposes — so every second that they are left running when not being used is wasted spend. Since non-production VM instances often have predictable workloads, such as a 7 AM to 7 PM work week, 5 days a week, the other 64% of spend is completely wasted. Inconceivable!
The good news is, that means these resources can be scheduled to turn off during nights and weekends to save money. So, let’s take a look at a couple of cloud scheduling options.
Scheduling Option 1: GCP set-scheduling Command
If you were to do a Google search on “google cloud instance scheduling,” hoping to find out how to shut your compute instances down when they are not in use, you would see numerous promising links. The first couple of references appear to discuss how to set instance availability policies and mention a gcloud command line interface for “compute instances set-scheduling”. However, a little digging shows that these interfaces and commands simply describe how to fine-tune what happens when the underlying hardware for your Google virtual machine goes down for maintenance. The options in this case are to migrate the VM to another host (which appears to be a live migration), or to terminate the VM, and if the instance should be restarted if it is terminated. The documentation for the command goes so far as to say that the command is intended to let you set “scheduling options.” While it is great to have control over these behaviors, I feel I have to paraphrase Inigo Montoya – You keep using that word “scheduling” – I do not think it means what you think it means…
Scheduling Option 2: GCP Compute Task Scheduling
The next thing that looks schedule-like is the GCP Cron Service. This is a highly reliable networked version of the Unix cron service, letting you leverage the Google App Engine service to do all sorts of interesting things. One article describes how to use the Cron Service and Google App Engine to schedule tasks to execute on your Compute Instances. With some App Engine code, you could use this system to start and stop instances as part of regularly recurring task sequences. This could be an excellent technique for controlling instances for scheduled builds, or calculations that happen at the same time of a day/week/month/etc.
While very useful for certain tasks, this technique really lacks flexibility. Google Cloud Cron Service schedules are configured by creating a cron.yaml file inside the app engine application. The GCP Cron Service triggers events in the application, and getting the application to do things like start/stop instances are left as an exercise for the developer. If you need to modify the schedule, you need to go back in and modify the cron.yaml. Also, it can be non-intuitive to build a schedule around your working hours, in that you would need one event for when you want to start an instance, and another when you want to stop it. If you want to set multiple instances to be on different schedules, they would each need to have their own events. This brings us to the final issue, which is that any given application is limited to 20 events for free, up to a maximum of 250 events for a paid application. Those sound like some eel-infested waters.
Scheduling Option 3: ParkMyCloud Google Cloud Instance Scheduling
Google Cloud Platform and ParkMyCloud – mawwage – that dweam within a dweam….
Given the lack of other viable instance scheduling options, we at ParkMyCloud created a SaaS application to automate instance scheduling, helping organizations cut cloud costs by 65% or more on their monthly cloud bill with AWS, Azure, and, of course, Google Cloud.
We aim to provide a number of benefits that you won’t find with, say, the GCP Cron Service. ParkMyCloud’s cloud management software:
Automates the process of switching non-production instances on and off with a simple, easy-to-use platform – more reliable than the manual process of switching GCP Compute instances off via the GCP console. Automatic on/off schedules make resource states easy to manage.
Provides a single-pane-of-glass view, allowing you to consolidate multiple clouds, multiple accounts within each cloud, and multiple regions within each account, all in one easy-to-use interface.
Does not require a developer background, coding, or custom scripting. It is also more flexible and cost-effective than having developers write scheduling scripts.
Avoids the hard-coded schedules of the Cron Service. Users of ParkMyCloud’s GCP scheduler can temporarily override schedules if they need to use an instance on short notice.
Supports Teams and User Roles (with optional SSO), ensuring users will only have access to the resources you grant.
Helps you identify idle instances by monitoring instance performance metrics, displaying utilization heatmaps, and automatically generating utilization-based “SmartParking” schedule recommendations, which you can accept or modify as you wish...
Provides “rightsizing” recommendations to identify resources that are routinely underutilized and can be converted to a different Google Cloud server size to save 50-75% of the cost of the resource. These recommendations incorporate custom GCP sizes, so you can adjust specifics around memory and CPU independent of each other.
Has a 14-day free trial, so you can try the GCP cloud scheduler platform out in your own environment. There’s also a free-forever tier, useful for startups and those on the Google Cloud free tier, as well as paid tiers with more advanced options for enterprises with a larger Google Cloud footprint.
Supports multiple GCP products, including Virtual Machines, CloudSQL databases, Autoscaling Groups, and GKE clusters and nodes.
Notifies users and admins of resource shutdowns, startups, and actions taken via Google Hangouts, Slack, Microsoft Teams, or Email.
How Much Can You Save with Google Cloud Scheduling?
While it depends on your exact schedule, many non-production Google Cloud VMs – those used for development, testing, staging, and QA – can be turned off for 12 hours/day on weekdays, and 24 hours/day on weekends. For example, the resource might be running from 7 AM to 7 PM Monday through Friday, and “parked” the rest of the week. This comes out to about 64% savings per resource.
Currently, the average savings per scheduled VM in the ParkMyCloud platform is about $245/month. That does not account for any additional savings gained from rightsizing.
How Enterprises Are Benefitting from ParkMyCloud’s Google Cloud Scheduler + Optimizer
If you’re not quite ready to start your own trial, check out this interview with Workfront, a work management software provider. Workfront uses both AWS and Google Cloud Compute Engine, and needed to coordinate cloud management software across both public clouds. They required automation in order to optimize and control cloud resource costs, especially given users’ tendency to leave resources running when they weren’t being used.
Workfront found that ParkMyCloud would meet their automatic scheduling needs. Now, 200 users throughout the company use ParkMyCloud to:
Get recommendations of resources that are not being used 24×7, and use policies to automatically apply on/off schedules to them
Get notifications and control the state of their resources through Slack
Easily report savings to management
Save hundreds of thousands per year
Ways to Save on Google Cloud VMs, Beyond Scheduling
Google has done a great job of creating offerings for customers to save money through regular cloud usage. The two you’ll see mentioned the most are sustained use discounts and committed use discounts. Sustained use discounts give Google Cloud users automatic discounts the longer an instance is run. This post outlines the break-even points between letting an instance run for the discount vs. parking it. Sustained use discounts have also been expanded with resource-based pricing, which allows the sustained use to be applied based on your use of individual vCPUs and GB of memory regardless of the machine type you use.
Committed use discounts, on the other hand, require an upfront commitment for 1 or 3 years’ usage. We have found that they’re best applicable for predictable workloads such as production environments. There are also the pre-emptible VMs, which are offered at a discount from on demand VMs in exchange for being short-lived – up to 24 hours.
In addition to these discounts, you can also save money by rightsizing your instances. Provisioning your resources to be larger than you need is another form of cloud waste. On average, rightsizing a resource reduces the cost by 50%. Google Cloud makes it easy to change the CPU or the Memory amounts using custom instance sizes. If you’d rather use standard sizing, they offer that as well. By keeping an eye on the usage patterns of your servers, you can make sure that you’re getting the most use of the resources you are paying for.
How to Create a Google Cloud Schedule with ParkMyCloud
Getting started with ParkMyCloud is easy. Simply register for a free trial with your email address and connect to your Google Cloud Platform to allow ParkMyCloud to discover and manage your resources. A 14-day free trial free gives your organization the opportunity to evaluate the benefits of ParkMyCloud while you only pay for the cloud computing power you use. At the end of the trial, there is no obligation on you to continue with our service, and all the money your organization has saved is, of course, yours to keep.
If you do choose to continue, our Google Cloud scheduler/optimizer pricing is straightforward. You will choose a functionality tier and pay per resource per month. There is a free forever tier available – so have at it.