While it provides all the benefits of managed Kubernetes, Amazon Kubernetes Service (AKS) makes forecasting and managing costs challenging. This is where AKS cost optimization tactics come into play.
Learn how to understand, control, and optimize the costs your AKS clusters generate with these 10 battle-tested strategies.
1. Follow cost optimization design principles
A cost-effective workload hits business goals while staying within budget. AKS cost optimization principles outline critical design decisions, helping you assess and improve applications deployed on Azure.
These encompass choosing the correct resources, setting up budgets and constraints, dynamically allocating and deallocating resources, optimizing workloads, and monitoring and managing costs (read more here).
2. Pick the right pricing plan
Pay-as-you-go in AKS
Like other cloud services, Microsoft Azure offers pay-as-you-go prices. This means that you pay only for the costs of the resources that you use, such as:
- VMs
- associated storage
- networking resources
This model works well for workloads with changing demands, allowing you to use various services without long-term commitments. However, it’s also the most expensive option Azure offers, and prices vary depending on the region.
Check out the AKS Pricing Calculator to estimate how much you might end up paying if you leave a tab open and forget to set up cost checks.
Cost-saving options in AKS
AKS offers a few more economical options besides the default pay-as-you-go model.
Reserved VM instances
This option is suitable for steady-state usage. A long-term commitment lasting one or three years gives you more pricing predictability, monthly payment options, and prioritized compute capacity.
According to Azure, 1-year reserved can save you up to 48% on Virtual Machines Linux DSv2 compared to pay-as-you-go. The savings for Virtual Machines Linux DSv2 on a 3-year reserved plan can reach 65% compared to pay-as-you-go.
Limitations of reserved VM instances:
Potential savings related to reserved instances may seem impressive initially, but they come at a price. In the cloud world, a year of commitment is an eternity, not to mention three.
Forecasting your usage for the entire period is a tall order. On top of that, your requirements will most likely change, so you may need to commit to even more or get stuck with unused capacity.
Spot virtual machines
Another great cost savings source is Spot virtual machine instances. These are unused resources Azure offers for up to 90% off the on-demand prices. Spot VMs are great for workloads that can tolerate temporary disruptions like batch processing or machine learning training.
Limitations of spot VMs:
Spot VMs can quickly bring tangible savings but aren’t optimal for all workload types. You cannot guarantee how long they will stay available, as Azure can reclaim them at any time with a 30-second notice. You need a solid plan to handle potential interruptions and, ideally, automate the process.
3. Pick the right VMs for the job
Selecting the right VMs can reduce your AKS costs significantly because you’ll get just enough capacity for required performance.
However, the process can be challenging. How do you select the optimal compute instance for your workload?
Create your basic requirements
Ensure that all computational components are addressed, including memory, SSD, network connectivity, CPU (architecture, number, and kind of processor), and memory.
Choose the best instance type
Choosing the appropriate computing instance may make or break your cloud cost. AWS now provides over 500 distinct instances, making the work fairly difficult.
A wide range of CPU, memory, storage, and networking configurations are available, each packaged in instance types designed for a certain capability (for example, machine learning tasks like model training or inference).
Configure your instance’s size
Keep in mind that the instance must be large enough to accommodate the demands of your workload, and solutions such as bursting must be accessible if necessary.
Rightsizing compute instances involves a lot of work, but you can automate it. Platforms like CAST AI can pick the best instance types and sizes for your application’s requirements while still cutting your AKS costs.
3. Set the right limits and requests
Defining pod requests and limits is another great way to reduce your AKS cluster cost.
AKS integrates with Azure to provide centralized enforcement for built-in policies. It lets you specify CPU requests and memory resources to ensure their limits are defined on cluster containers.
Using an automation solution, you can push your AKS cluster savings even further by continuously reducing your cluster to the minimum number of nodes by bin-packing Pods. Once the node becomes empty, CAST AI’s mechanism deletes it from the cluster – here’s how it works.
4. Take advantage of autoscaling
Most cloud-based apps see consumption fluctuations, but balancing cost and performance remains difficult.
If you don’t set any resource limits, traffic spikes might cause a huge, unexpected cloud charge. If you set strict resource restrictions, a surge of traffic may cause it to fail.
Some cloud cost management tools monitor your consumption and inform you immediately if it exceeds predefined limitations or displays strange trends. These tools may offer useful ideas for modifying your cloud resources to match your current needs.
However, manually raising your cloud’s capacity requires effort and time.
In addition to monitoring everything that happens within the system, you usually need to:
- Adapt quickly to variations in traffic volume and resource utilization for each virtual machine across all of your services
- Ensure that changes made to one workload don’t negatively influence other workloads
- Create and manage autoscaling groups to ensure they have appropriate resources for your needs
All of these chores can be done automatically via autoscaling.
When combined with dynamic cloud-native systems like Kubernetes, autoscaling is a good strategy for minimizing cloud expenses. The tighter your scaling mechanisms are structured, the more effective your optimization efforts will be, and there will be less waste.
Real-time autoscaling systems employ business KPIs to calculate the optimal number of required instances. They can scale up, down, or to zero if no more work is required.
Check out this graph to examine how provisioned and utilized resources (CPU and RAM) differ and how this gap shrank when the team started using the CAST AI autoscaling feature:
6. Use preset AKS cluster configuration
Each workload has different needs. For example, a production environment requires higher spec VM SKU with redundancy across Azure AZs, while Dev/Test cluster can run with unnecessary features turned off.
Azure provides different preset configurations for distinct environments, highlighting their impact on cost. You can also get specific configuration recommendations for your cluster that will improve your performance while reducing AKS costs by 50% or more.
Kubernetes cost monitoring
View your Kubernetes costs in one place and monitor them in real time.
7. Stop clusters that don’t need to be running
Not all clusters need to be running all the time. For instance, you could easily turn off a Dev/Test environment when not in use.
AKS lets you stop a cluster to avoid unnecessary charges from piling up. By shutting down its node pools, you can save on compute costs while maintaining objects and cluster states when you start it again.
And if you don’t want to keep doing it manually (because why would you?), check out CAST AI’s cluster scheduler. It will automatically turn your cluster off and on as required.
8. Get rid of orphaned resources
The cloud makes launching an instance easy – but so is forgetting to shut it off. As a result, many organizations deal with orphaned instances that don’t belong to anyone but still appear on your Azure bill. This problem is especially acute in large organizations with no centralized resource visibility and several applications running simultaneously.
How do you identify and shut down such instances? This is another case in which automation is beneficial.
Automated cloud optimization solutions continually analyze your use for inefficiencies and cut resources when appropriate. They can also terminate idle instances and processes to help you save money on cloud computing.
9. Automate Spot VMs
Spot VMs (Spot instances) enable you to tap into unutilized capacity in Azure at a much lower cost than on-demand pricing. This solution is only suitable for workloads that can handle potential interruptions.
You can reap the pricing benefits of spot VMs and still use them safely, but to do so, you will need an automation solution. It will help you identify spot-friendly workloads, pick the right VMs, bid the price, and move your workload automatically to on-demand instances in case of interruptions.
10. Use an automation tool for other cost optimization tactics
Teams that take cost reduction seriously can go beyond cost monitoring. CAST AI will automatically manage and provision cloud resources to balance cost and performance.
The broad range of optimization features combined with AI-powered automation and dedicated support brings CAST AI to the top of cloud cost optimization platforms:
- Automated virtual machine selection and rightsizing – the platform picks the best matching types from hundreds of VM types and sizes, removing the time-consuming process of selection and provisioning
- Autoscaling compute resources – CAST AI continuously analyzes the application demands and scales cloud resources up or down for optimal performance and minimum cost, scaling down to zero and removing VMs when no work needs to be done
- Spot VM automation – thanks to automation, AKS users can leverage Spot virtual machines to cut their costs further without worrying about interruptions or lack of availability.
CAST AI delivers results to AKS customers at every stage of their adoption journey. For example, Akamai achieved 40-70% cost savings on Kubernetes workloads with zero downtime or incidents, generating massive time savings and enhancing engineer productivity.
Automate AKS cost optimization
These strategies are bound to positively impact your next AKS bill. But if you want long-lasting and significant results, you must move beyond manual cloud cost management efforts and embrace automation.
Teams using automated AKS cost optimization solutions can reduce their expenses while improving performance and unlocking new opportunities.
Connect your cluster to the CAST AI platform and run a free AKS cost analysis to see custom recommendations you can apply automatically.
You can access CAST AI from the Azure Marketplace; it’s directly accessible for easier installation within the AKS ecosystem.
