A top choice among teams looking for enterprise-grade cloud services, Azure comes with just as many cost-related complexities as other major cloud providers. How do you control and reduce your cloud costs? Here are seven strategies for Azure cost optimization to help you get started.
1. Use Azure cost management tools
Similar to the other major cloud service providers, Azure comes with multiple tools to manage cloud costs:
- Azure pricing calculator – The Azure pricing calculator lets you estimate the cost of Azure services and resources such as compute, storage, databases, and managed services like Azure Kubernetes Service (AKS).
- Cost analysis tool – The cost analysis tool provides a detailed breakdown of your spending on the Azure cloud platform. You can see where your money is going grouped by resources, tags, and other features.
- Budgets – Setting a budget lets you align Azure spending with your requirements, ensuring you don’t spend a penny more than you intended.
- Azure cost optimization alerts – You can set alerts for credit and department spending quotas. Credit alerts are automatically set to notify you when you reach 90-100% of your balance, ensuring you don’t capsize your budget.
- Azure Advisor – A handy tool that analyzes resource use. It also suggests alternative solutions to help improve cloud performance, security, and cost-effectiveness.
Use these tools to stay within your budget and maintain control of your finances. Better yet, you may uncover a few unnecessary expenses or other areas in which you can save on your cloud costs.
Note: These tools might not be enough if you have a larger cloud footprint – or if you need real-time cost insights because of technologies like Kubernetes.
2. Increase cost visibility
Having a clear picture of your current cloud expenses is a key first step to understanding the real utilization of resources your team provisions and ultimately reducing costs.
Track these four metrics
Before you spend money on a cost-monitoring solution, make sure it measures the following metrics:
- Real-time costs – cloud providers usually serve cost data with a delay, and Azure is no exception. But if your teams use dynamic cloud-native approaches like Kubernetes, you need access to cost data in real-time.
- Daily cloud spend – this metric is essential for quickly checking your budget burn rate to understand if you’re going to meet your estimation or go beyond the budget you’ve set for the month.
Example: Imagine that you have set a $2,000 monthly budget. If your average daily spend is closer to $90 than $66.6 (30 days x $66.6 = $1998), your cloud bill is likely to be higher than you planned.
- Cost per provisioned vs. requested CPU – by analyzing the difference between these two numbers, you’ll be able to calculate how much you’re actually paying per requested CPU to improve the accuracy of your cost reporting.
- Historical cost allocation – if you go over your cloud budget, you need to know why. This report helps to do that and see where the extra costs come from.
Set up budget alerts and notifications
Cloud costs can quickly get out of control. Setting up alerts and notifications when certain areas of your cloud application reach or surpass set thresholds allows you to act immediately.
Implement tagging and resource organization
Tags are the sole mechanism for understanding the cost of your cloud environment. Cloud tagging is very important for governance and security as well.
It pays to build a cloud tagging strategy that describes the rules and processes teams must follow and implement. Ensure your strategy explains how to use tags effectively (including proper formatting), who should create them, and how tagging decisions will be made.
Check out this guide to learn more about tagging: Build A Cloud Tagging Strategy In 5 Steps
3. Rightsize your VMs
Define your application’s requirements
Identify your application’s minimal requirements and make sure that the instance type you choose can fulfill them across all dimensions, such as CPU count (or GPU), memory, SSD storage, and network architecture.
A low-cost instance may appear appealing, but it might soon experience performance challenges while executing CPU-intensive applications.
Azure offers a wide range of options for virtual machines optimized for different workloads, such as compute-optimized, memory-optimized, or accelerated computing for machine learning applications.
Once you determine the instance type that works best for your application, it’s time to choose the right size for the machine. Imagine doing all of this manually for the 650 different VMs Azure offers! Hint: you can use an automated engine to do this job for you ensuring decent Azure cost optimization.
Examine storage performance constraints
Another item to think about while optimizing your cost reductions is data storage. Every application has different storage requirements. When selecting a virtual machine, ensure that it has the storage throughput and IOPS that your application requires.
Also, avoid expensive disk choices like premium SSDs unless you intend to use them extensively.
Think about network bandwidth
Pay attention to the size of the network connection between your instance and the consumers allocated to it if you’re dealing with a large data migration or a high volume of traffic.
In some cases, you can boost transfer speeds to 10 or 20 Gbps. The catch is that only those instances will be able to sustain this amount of network traffic.
4. Take advantage of Spot VMs
Buying idle capacity from cloud providers is a smart decision because it may save you up to 90% on on-demand resources. However, Azure can reclaim Spot VMs at any time, giving you a 30-second window of opportunity to locate another location for your application to execute.
Teams often use the following sequence when employing Spot instances:
- Checking to see if the workload is Spot-on – Can you put up with interruptions? How long will it take to finish the project? Is this a time-sensitive task?
- Pick the right Spot VM – Going through the available Spot instances, look for less popular instances that are less likely to be interrupted and can run for longer periods (interrupt frequency rate).
- Bid on your Spot VM – Set the maximum sum you are willing to pay for your preferred Spot instance. The rule of thumb here is to set this at the level of pay-as-you-go prices.
- Manage Spot VMs in groups – This enables you to request many instance types at the same time, enhancing your chances of securing a Spot instance.
- Prepare for interruptions – Create a backup plan for your application in case your Spot instances are reclaimed.
As you can see, running Spot VMs requires a lot of energy and time invested in configuration, setup, and maintenance tasks.
Good news: you can automate this. The mobile marketing company Branch.io saved several millions of dollars per year by leveraging Spot instance automation.
5. Set a schedule for your virtual machines
You might already be using a start/stop schedule on your virtual machines. But if not, this is a great Azure cost optimization technique to further save on your cloud computing bill.
Before setting a schedule, carry out a usage analysis. See when you’re using any Azure resource, usage patterns will determine when to pause and resume it.
Pausing virtual machines when not in use is an easy way to save resources and money. This start/stop function works especially well for testing, where the machines don’t need to be on constantly.
On the other hand, stopping and starting is likely not an option if you’re using autoscaling or a service that needs to run continuously. Assess your needs and see whether it makes sense for your computing needs.
6. Autoscale compute resources
Most cloud-based apps see demand fluctuations, and striking a balance between cost and performance is always a challenge.
If you keep your tab open, traffic spikes can cause a huge, unexpected cloud charge. If you set high resource restrictions for your app, a large number of new visitors may cause it to fail.
There are cloud cost management tools that monitor your usage and inform you immediately if it exceeds predetermined limitations or exhibits strange patterns. These tools may provide you with useful suggestions for modifying your cloud resources to match your current needs.
However, manually increasing your cloud capacity requires effort and time. You’re also running against the risk of underutilized resources – for example, only 13% of provisioned CPUs end up being utilized.
This is where autoscaling comes in.
When combined with dynamic cloud-native systems like Kubernetes, autoscaling is a great strategy for optimizing cloud costs. Your optimization efforts will be more effective, and there will be less waste when running the program, the tighter your scaling mechanisms are structured.
Check out how the company Jisr used an automation solution to handle the spiky usage pattern of its production cluster by providing a rapid scaling capability:
CPU usage over the period of 30 days
Memory usage over the period of 30 days
7. Get the right tool for the job
To manage cloud expenses, teams can choose from a variety of Azure cost optimization tools:
- Cost visibility and allocation: Cost allocation, monitoring, and reporting technologies can help you identify the source of your spending. Real-time spending tracking is useful since it alerts you when you exceed a specific level.
- Cost forecasting and budgeting: If you’ve processed enough historical data and have a clear feel of your future needs, you can plan your budget and estimate how many resources your teams will require.
- Legacy cost optimization solutions: To gain a comprehensive view of your cloud spending and find potential areas for improvement, combine the data from the two aboves. Many products on the market, like VMware’s CloudHeath and Cloudability, can help here. However, most of the time, all they provide are static suggestions that engineers must implement manually, which takes time and effort.
- Automated, cloud-native cost optimization: Even if you’ve been doing fantastic manual optimization, automation produces even better results while demanding less work from teams. The optimal technique in this scenario is a wholly autonomous, fully managed system capable of responding quickly or cost-effectively to changes in resource demand.
Akamai achieved 40-70% cost savings on Kubernetes workloads without any downtime or incidents using an automated cost optimization solution. As a result, the company benefited from massive time savings and enhanced engineer productivity – as well as a much lower cloud bill.
Wrap up
Azure cost optimization is a continuous process, not a one-time task. To save money on Azure services and resources, you must regularly monitor our cloud resource utilization, compare it to your bill, estimate future requirements, identify idle resources, and allocate resources that provide the optimum cost-performance ratio. Or you can have an automation system do it for you.
Cloud cost optimization
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