Cloud infrastructure cost management becomes an increasingly problematic year by year. IT specialists and companies are looking for the most efficient ways of optimizing costs related to cloud, on-premise, and SaaS systems. This article discusses five ways of cutting operational costs in the cloud.

 

Set up metadata

 

 

Metadata enables you to monitor all kinds of loads and resources, as well as configure system access rights and monitor resource costs. This way, you can pinpoint the causes for service cost increases and direct inquiries to relevant departments of your company.

 

Integration of metadata can be substantially simplified with additional software. For one, CloudCheckr analyzes tagged resources and offers recommendations for cost optimization.

 

To make metadata use as efficient as possible, you need to keep it up to date.
Metadata enables you to avoid unpredictable cloud-related expenses, find the project’s bottlenecks and evaluate its status in more detail.

 

Eliminate zombie resources

Sometimes, your project may order computing resources that remain unused, e.g., those allocated for testing but are then forgotten about. Even if you stop an EC2 instance, you will still be charged for the EBS storage it uses. Moreover, you might have hundreds or even thousands of those idle EBSs.

 

 

To optimize costs, consider allocating only the computing resources you require and removing them once you no longer need them. This is hard to do manually, so it’s better to use additional tools to scan your infrastructure. They will enable timely detection of zombie processes and notify you about cost optimization opportunities.

 

One such tool is Hyperglance, which identifies unused resources and processes and conveniently marks them on the cloud infrastructure map.

 

Use spot or reserved instances

Cloud services might offer more favorable terms for the users of spot and reserved instances.

 

Spot instances. These are best suited for stateless workloads, batch tasks, and other tasks that may fail. Should the need arise, these instances can be instantly shut down.

 

Azure and Google Virtual VMs and Amazon EC2 spot instances offer access to this kind of resource at a lower price.

 

Reserved instances. If a company agrees to use a set amount of resources over a long period (1–3 years), it can get a service package at a lower price. Such instances are best suited for predictable workloads.

 

This kind of pricing is available for Amazon EC2, Azure and AWS VMs, and Google’s Committed use discounts.
Reserved instances can offset up to 80% of expenses against on-demand ones, depending on the platform and other variables.

 

Manage computing capacity

It is essential to formulate an optimal resource consumption strategy. It needs to provide for traffic peaks without over-usage that would inflate project costs.

 

One way of avoiding this is to use cloud autoscaling. Cloud providers provide dynamic autoscaling services that monitor application performance and automatically scale capacity as needed. The adjustment is based on the project’s needs and priorities, be it costs, performance, or accessibility.

 

 

Serverless computing is another option for tackling scaling issues. With proper planning, this can help set up queues and caching for maximum reliability in case of unexpected traffic peaks while eliminating the need to pay for idle resources.

 

Localized computing

One of the most effective ways of optimizing cloud costs is using different kinds of storage for your data depending on its accessibility, latency, and storage period. Storage costs are very much dependent on the type of storage you use. For instance, data that needs to be accessed regularly and with the lowest latency possible is best held in hot storage, which entails higher costs. Meanwhile, rarely accessed data can be put into cheaper “cold” storage.

 

Cloud computing costs can also be cut by migrating workloads to service regions where prices and demand are lower. Common storage services support each location, and workloads will see the difference between service regions.

 

 

Bear in mind that migrating workloads to some regions might be prohibited for security and regulatory reasons.

 

Nevertheless, some processes (e.g., testing, software development) can be freely migrated between cloud platforms should they become economically viable.