How to reduce cloud costs? 8 proven strategies you should try

Published: January 29th, 2021
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Many organizations move their applications to the cloud with the idea of reducing their IT infrastructure costs

But once they get their cloud bill at the end of the month, they often face surprising expenses. It's challenging to keep track of costs when cloud resources are so easy to provision. However, it is essential that you identify the areas in your cloud infrastructure that are causing cost overruns. 

And don't worry, if you’ve experienced this before you're not alone. Many companies are struggling with this. 

According to the Flexera 2020 State of the Cloud Report, cost optimization is the primary initiative for organizations that migrated to the cloud. Cloud users spanning from large enterprises to startups, small and mid-sized businesses are now focusing on optimizing their cloud usage. 

Just to give you some perspective:  A typical large enterprise, averages their cloud budget around $3.5 million per year. Smaller companies usually pay up to $1 million for their cloud usage. Most of those costs are made up of Software-as-a-Service (SaaS) and Infrastructure-as-a-Service (IaaS). 

Developing a cloud cost optimization strategy is a key step to gaining control of your cloud bills. You need to identify potential money leaks and get rid of them as soon as possible. Here are 8 proven strategies that help to reduce and optimize the costs of public cloud services.


1. Right-size resources to what you actually need

One of the greatest advantages of the cloud is the ability to scale resources up or down as your business needs change. That way, you don't need to spend a lot of capital upfront and can buy extra capacity just when you need it. 

However, since the cloud is so easy to scale, you need to be extra careful about overprovisioning your applications. The idea is to achieve the performance you need at the lowest possible cost. 

To get there, you need to maintain optimal compute, storage, and network settings. If you fail to properly-size your cloud environment on a regular basis, you will quickly get a bill filled with unnecessary costs of unknown origin. Strike a balance to maximize your workloads without overspending. Make sure to regularly review your requirements and resize your resources to match them.


2. Buy resources upfront to get discounts

Companies turn to cloud service providers (CSPs) for on-demand resources. But you can get your hands on cheaper alternatives if you're willing to commit. There are several discounted pricing schemes you'll find across the 3 primary cloud providers Amazon Web Services, Microsoft Azure, and Google Cloud. 


Reserved instances

If you make a commitment to use a given amount of capacity over a 1 or 3-year period, you stand to save a lot on cloud resources. This scheme is called reserved instances. Such resources might come even at 1/3 of the on-demand instances pricing, depending on the platform. 

Every major CSP offers this pricing option – AWS EC2 Reserved Instances, Azure Reserved VM Instances, and the Google Cloud Committed Use program. And even here, you can look for some alternatives. For example, AWS offers a Savings Plan that at a similar discount level but with added flexibility. 

Review the terms carefully before committing to this type of plan – you're likely to commit to one instance type or a family. And this could become a problem when your workloads require more compute or memory a few months into your plan. This trade-off has a name: vendor lock-in. If you have workloads with consistent and predictable capacity, reserved instances are a good choice. 

Spot instances

You can access unused capacity for a discounted price. Amazon EC2 Spot Instances, Azure Spot VM, and Google Cloud Preemptible VMs all refer to this type of cloud resources. The pricing model will depend on the type of resource you purchase and its price at the time – and the cost of spot instances changes even on an hourly basis! 

For low priority instances, you can achieve up to 90% of savings. But they're subject to abrupt interruptions, depending on the overall capacity requirements in your region. Spot instances that exceed your maximum price point will be terminated. That's why they're a good match for stateless workloads, batch jobs, and any other workloads that tolerate disruption.


3. Use automation tools 

The market is full of cost optimization and cost management tools that offer automation tools. Such solutions can install, configure, and manage your computing services automatically whenever possible. You can automate tasks such as backup or storage, CI/CD deployment strategies, environment settings and configurations. Automation tools can also manage security and compliance to minimize the number of working hours required to keep your cloud in shape. They also lower the rate of manual errors and allow your staff to focus on mission-critical business activities. 

For example, you can implement an automation tool that optimizes security checks, verifies code and identifies potential problems before going into production, while notifying you of potential or existing threats. Cloud orchestration tools can configure resources to bring together automated workflows into a single process, which is then executed automatically. A dynamic resource allocation solution can balance loads to avoid over-utilization.


4. Scale your resources (reactively and proactively)

Companies looking to save up on cloud costs turn to two types of scalability: reactive autoscaling and predictive autoscaling based on insights from AI. 

Reactive autoscaling is relatively easy to implement. However, it only works for stateless applications that don't require long startup and warm-up times. This type of autoscaling is based on the runtime metrics, so it doesn't work well with sudden bursts of traffic either. In such a case, you run the risk of provisioning too many instances when they're in fact not needed. Also, the instances requiring resources might be provisioned too late and, as a result in poor performance. Applications configured for autoscaling should be designed and implemented to start and warm up quickly. 


Predictive AI-based autoscaling, on the other hand, works for all kinds of applications. This includes databases and other stateful components, as well as applications that take a lot of time to build and warm-up. Predictive scaling relies on artificial intelligence and machine learning algorithms that analyze the past traffic, performance, and utilization to deliver predictions about the required infrastructure footprint that can handle the upcoming surges or slowdowns in traffic. 


When judging which type of scalability is a good choice for you, note that many CSPs offer discounts for stable and continuous usage of CPU capacity, memory, and other cloud resources. So, if scalability doesn't give you better savings than cloud discounts, you might not need it at all.


5. Examine your data transfer fees

Moving data to and from the public cloud can become very expensive. Cloud providers often charge a lot for moving data away from their services or even moving it between different availability zones or regions. 

That's why you need to limit data transfers to the minimum. Start by taking a closer look at your cloud provider's transfer fees. Then adjust your architecture to lower the number of required data transfers. 

For example, on-premises applications that often access cloud-hosted data can be moved to the cloud to greatly reduce or completely eliminate these costs. Evaluate different fees for different transfer methods designed to accelerate and secure the movement of data between a private data center and public cloud. For instance, check the cost of using a dedicated network connection service such as Azure ExpressRoute, AWS Direct Connect, or Google Cloud Interconnect to the cost of a physical transfer device such as Azure Data Box or AWS Snowball.


6. Review your cloud storage costs

You can do many things to reduce the high cost generated by your cloud storage. For example, you can take advantage of storage tiers. In AWS, you can choose from several tiers such as AWS S3, 3 Standard – Infrequent Access, S3 One Zone, and S3 Glacier. Don't hesitate to ask your team to use auto-tiering and schedule data movement from tiers that are expensive to inexpensive ones. 

Also, do your best to eliminate technologies that are redundant. Is your team using native cloud snapshots and a backup product to back up all the cloud-based IaaS and PaaS components? Get rid of this redundancy by using a specialized data management tool that can manage snapshots and cloud backups. 

Avoid using block storage for basic things. Block storage tends to be far more expensive than object storage, even 5-10 times, depending on the cloud provider. A data management tool can help you deal with that too.


7. Avoid cloud sprawl


Cloud sprawl happens more often than you expect. It means a proliferation of cloud resources used in an organization that is completely out of control. If you fail to eliminate cloud services that aren't needed anymore, you'll continue paying for them, and this will drive your costs up. 

It's not just shadow IT projects that are to blame here. Developers might sometimes terminate a virtual machine but leave the resources attached to it dangling. 

To avoid this, use a tool that offers optimal visibility into your cloud environment with infrastructure and application monitoring. Set up policies on when to decommission cloud resources that are no longer needed. This is how you avoid having to pay for orphaned resources. Carefully monitor your cloud bills and contracts to instantly see whether you're paying for services you're not actually using.


8. Monitor cloud costs in detail

You can't manage what you can't measure. This is true for cloud costs too. Use specialized cloud tools to monitor your cloud infrastructure and measure costs by the environment, application, microservice, team, and department.


How to measure cloud infrastructure costs? 

  • Organize your cloud project by department, team, application, and environment,

  • Associated costs and billing of the project with the given budget of the department or team

  • Tag cloud resources with tags related to department, team, application, and environment. 

  • Use cost analysis and optimization tools that provide efficiency features to measure, report on, and optimize infrastructure costs.


Conclusion

As more companies migrate their applications to the cloud due to its flexibility, resiliency, and scalability, they're soon going to discover that costs are still the most important pain point. But it doesn't have to be. 

If you work with a partner with experience in cloud computing, you can be sure that all of your cost-related problems are solved before amounting to a high cloud bill. 


At Maxima Consulting, we have been working with organizations at every stage of their cloud journey. Our experts have the experience and follow industry best practices you need to make your digital transformation as smooth as possible. Get in touch with us to see how we could help you save up on cloud expenses.

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