If you’ve been running your workloads on Google’s managed Kubernetes service, Google Kubernetes Engine (GKE), you probably know how hard it is to forecast, monitor, and manage costs. GKE cost optimization initiatives only work if you combine Kubernetes know-how with a solid understanding of this cloud provider.
Keep reading to get the scoop on the best practices from the Kubernetes ecosystem and optimize your GKE costs.
1. Understand GKE pricing
Pay-as-you-go
How it works: In this model, you’re only charged for the resources that you use. For example, Google Cloud will add every hour of compute capacity your team uses to the final monthly bill.
No long-term binding contracts or upfront payments exist, so you’re not overcommitting. Plus, you can increase or reduce your usage just in time.
Limitations:
- You risk overrunning your budget if you don’t control the scale of resources your team burns each month.
- Flexible pay-as-you-go VMs work well for unpredictable workloads that experience fluctuating traffic spikes. Otherwise, look into alternatives.
Committed use discounts
How it works: committed use discounts (CUDs) are similar to AWS Reserved Instances but they don’t require advance payments. You can choose from two types of committed use discounts: resource- and spend-based.
Resource-based CUDs offer a discount if you commit to using a minimum level of compute engine resources in a specific region, targeting predictable and steady-state workloads. Moreover, CUD sharing lets you share the discount across all projects tied to your billing account.
Spend-based CUDs, on the other hand, deliver a discount to those who commit to spending a minimum amount ($/hour) for a Google Cloud product or service. This offering was designed to help companies generate predictable spend measured in $/hr of equivalent on-demand spend. They work similarly to AWS Savings Plans.
Limitations:
- In the resource-based scenario, CUD will ask you to commit to a specific instance or family.
- In the spend-based CUD, you risk committing to a level of spend for resources that your company might not need six months from now.
In both examples, you risk locking yourself in with the cloud vendor and committing to pay for resources that might make little sense for your company in one or threeyears.
When your compute requirements change, you’ll have to commit even more capacity or be stuck with unused capacity. Committed use discounts remove the flexibility and scalability that made you turn to the cloud in the first place.
Take a look here to learn more: GCP CUD: Are There Better Ways to Save Up on the Cloud?
Sustained use discounts
How it works: sustained use discounts are automated discounts users get on incremental usage after running compute engine resources for a large part of a billing month. The longer you run these resources continuously, the bigger your potential discount on incremental usage.
Spot virtual machines
How it works: In this cost-effective pricing model, you bid on resources Google Cloud isn’t using and can save between 60% and 90%. However, the provider can pull the plug with a 30-second notice, so you need a strategy and tooling for dealing with such interruptions.
Limitations:
- Make sure you pick Spot VMs for workloads that can handle interruptions.
2. Pick the right VM type and size
Define your workload’s requirements
Your first step is to understand how much capacity your application needs across the following compute dimensions:
- CPU count and architecture
- Memory
- Storage
- Network
You need to ensure the VM’s size can support your needs. See an affordable VM? Consider what will happen if you start running a memory-intensive workload on it and face performance issues affecting your brand and customers.
Consider your use case as well. For example, if you’re looking to train a machine learning model, it’s smarter to choose a GPU-based virtual machine because training models are much faster.
Choose the best VM type for the job
Google Cloud offers various VM types to match a wide range of use cases, with entirely different combinations of CPU, memory, storage, and networking capacity. Each type comes in one or more sizes, so you can easily scale your resources.
However, providers roll out different computers for their VMs. The chips in those machines may have different performance characteristics. So, you may easily end up picking a type with a strong performance going way beyond your resource requests.
Understanding and calculating all of this is hard. Google Cloud has four machine families with multiple machine series and types. Choosing the right one is like combing through a haystack to find the needle you need.
Check your storage transfer limitations
Data storage is a key GKE cost optimization aspect since each application has unique storage needs. Verify that the machine you choose can support your workloads needs.
Also, avoid expensive drive options such as premium SSD unless you plan to use them to the fullest.
3. Use Spot virtual machines
Check if your workload is spot-ready
Spot VMs offer an amazing opportunity to save on your GKE bill – even by 91% off the pay-as-you-go pricing!
But before you move all your workloads to Spot VMs, you need to develop a strategy and check if your workload can run on them.
Here are a few questions you need to ask when analyzing your workload:
- How much time does it take to finish the job?
- Is it mission- and/or time-critical?
- Can it handle interruptions gracefully?
- Is it tightly coupled between nodes?
- When Google pulls the plug, what solution will you use to move your workload?
Choose your spot VMs
When picking a spot VM, go for the slightly less popular ones. It’s simple – they’re less likely to get interrupted. Check the frequency of interruption of your Spot VM candidate. This is the rate at which this instance reclaimed capacity during the trailing month.
Use groups
Set up groups of spot instances to request multiple machine types simultaneously. This will boost your chances of getting the spot machines you need. Managed instance groups create or add new Spot VMs when additional resources are available.
If you choose to manage Spot VMs manually, prepare for a massive configuration, setup, and maintenance effort.
Luckily, there’s another way: automation.
The video SaaS company PlayPlay used our automation solution to manage the entire Spot VM lifecycle – from selection and provisioning to management and decommissioning. The company achieved 40% cloud cost savings on average across its workloads, boosting DevOps team productivity.
4. Take advantage of autoscaling
The tighter your Kubernetes scaling mechanisms are configured, the lower the waste and costs of running your application. Read this for a more detailed guide to these three autoscaling mechanisms: Guide To Kubernetes Autoscaling For Cloud Cost Optimization
Here are a few tips to help you make the most of Kubernetes autoscaling:
Make sure that HPA and VPA policies don’t clash
Vertical Pod Autoscaler automatically adjusts the number of pods and limits based on a target average CPU utilization, reducing overhead and achieving cost savings. Horizontal Pod Autoscaler aims to scale out more than up.
So, double-check that the VPA and HPA policies don’t interfere with each other across your GKE clusters. Review your binning and packing density settings when designing clusters for business- or purpose-class service tier.
Consider instance weighted scores
When autoscaling, use instance weighting to determine how much of your chosen resource pool you want to dedicate to a particular workload. This ensures that the machines you create are best suited for the work at hand.
Reduce costs further with a mixed-instance strategy
A mixed-instance strategy can help you achieve excellent availability and performance at a reasonable cost. You choose from various instance types, some of which may be cheaper and suitable for lower-throughput or low-latency workloads.
Mixing instances in this way could potentially result in cost savings because each node requires Kubernetes to be installed,which adds a little overhead.
But how do you scale mixed instances? In a mixed-instance situation, every instance uses a different type of resource. So, when you scale instances in autoscaling groups withmetrics like CPU and network utilization, you might get inconsistent metrics from different nodes.
To avoid these inconsistencies, use the Cluster Autoscaler to create a configuration based on custom metrics. Also, ensure all your nodes share the same CPU cores and memory capacity.
5. Use an automation tool for GKE cost optimization
Using these best practices is bound to impact your next GKE bill. But manual cost management will only get you to a certain point. It requires many work hours, potentially leading to mistakes that compromise your availability or performance if you miscalculate..
Teams that are serious about cost reduction go beyond cost monitoring and towards a fully automated Kubernetes cost optimization setup. They pick tools that automatically manages and deploys cloud resources, balancing cost and performance.
A good automation tool should include the following features:
- Automated virtual machine selection and rightsizing – the platform selects the best-matched types from hundreds of VM types and sizes, eliminating the time-consuming process of selection and provisioning.
- Autoscaling computing resources – the tool continually evaluates application demand and scales cloud resources up or down for maximum performance and minimal cost, scaling down to zero and deleting VMs when no work is required.
- Spot VM automation – With automation, GKE users can employ Spot VMs to reduce expenses even more without worrying about interruptions or lack of availability.
Reduce your GKE costs using automation
GKE cost optimization solutions that use automation help save time and money by dramatically reducing the work required to manage cloud costs, allowing you to enrich your Kubernetes applications with new features instead of micromanaging the cloud infrastructure.
Connect your cluster to the CAST AI platform and run a free cost analysis to see a detailed cost breakdown and recommendations – or use automation to do the job for you.
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