The pay-as-you-go model of public cloud services initially seems ideal – until you realize that predicting and controlling costs can feel like navigating without a map. Even with a cloud cost management strategy in place, expenses won’t decrease on their own.
Effective cost optimization requires consistent engineering effort: adjusting resources daily, responding to usage spikes, and addressing shadow IT.
Is there a more efficient way to manage your cloud spending? Keep reading to find out.
Why is cloud cost management so challenging?
Most teams struggle to control cloud costs because they have never had so much leeway to spin up new instances and obtain compute resources in minutes.
Here are some common causes of cloud prices spiraling out of control:
- Companies often miss the cost risks of pay-per-use, lack visibility into real-time service pricing, and fail to budget for the cloud, leading to unexpected monthly bills.
- Teams are often stuck with cost visibility, allocation, and management dashboards that help to address some of these issues, but not all.
Cloud cost management analyzes, manages, and ultimately optimizes these expenses. An ideal balance is when granular control over cloud spending doesn’t impact availability or performance.
However, cloud cost management falls short if it’s done manually.
Where manual cloud cost management falls short
1. Resource demand changes all the time
Finding the balance between cost and performance is crucial when using on-demand cloud services. Traffic spikes can either result in a significant and unexpected cloud bill if you leave your application open, or they can cause your application to fail if you place strict limits on its resources.
Having visibility into your costs may help you forecast demand, but it doesn’t get you anywhere close to solving the problem. This is why teams often allocate more resources than applications actually need: only 10% of CPUs provisioned for Kubernetes clusters end up being used.
2. Cost visibility can be tricky
Large companies often decentralize cloud spending decisions, which makes achieving cost visibility and transparent cost allocation more difficult. With shadow IT projects cropping up all over the place, you’ll be dealing with costs that can’t be explained simply by looking at a dashboard or report.
3. Managing costs manually is too time-consuming
When managing cloud costs, you need to analyze your setup, allocate costs to teams, understand how much you’ve spent on which application, find the right cloud services, configure them, move your workloads there, and double-check that everything works.
This isn’t a one-time exercise but a continuous, highly complex, and time-consuming process. To uncover cost-saving options, you must monitor your application demands and available resources around the clock.
Here’s what Dan Udell, Director of Foundations Engineering at Bud, told us:
In my first two or three months at Bud, I manually went project by project, cluster by cluster, and managed to save tens of thousands per month. But this was more than a full-time job. That’s why we looked into solutions like Cast AI – to offload that work to something that could monitor and make smart decisions automatically, much faster than I could.
Bud initially aimed to gain more visibility into the allocation and spending of cloud costs. However, once the team started taking action to optimize costs, the focus shifted to preventing the need for manual checks in the future while increasing resource utilization.
That led to the idea of using an automation solution.
Automation is the answer
Automation solutions eliminate the need for engineers to carry out repetitive tasks associated with managing a cloud infrastructure.
Some things are just not meant to be managed manually. And cloud costs are one of them.
Let’s look at areas where automation can make the biggest impact.
Compute instance selection
If computing is your largest expense, choosing the correct virtual machine size can significantly reduce your bill. But how can a human engineer achieve that when AWS alone has over 700 different EC2 instances in various sizes?
PlayPlay, the video creation platform, used automation to move its Kubernetes workloads to more cost-efficient VMs. The screenshot below illustrates a rebalancing operation in which Cast AI replaced 13 nodes without impacting service availability, driving almost 62% cost savings.
Boosting resource utilization via bin-packing
Heureka Group has seen a 30% drop in compute costs in the Dev cluster, achieved by bin-packing Spot-friendly workloads (stateless workloads that tolerate interruptions) and quickly removing the empty Spot nodes to drive down the number of provisioned CPUs.
Autoscaling as demand fluctuates
Manually increasing your cloud capacity is difficult and time-consuming. You must monitor everything that occurs in the system, which may leave you with little time to investigate cloud cost savings.
Akamai Technologies – one of the world’s largest and most trusted cloud delivery platforms – was looking to optimize the costs of running its core infrastructure. The graph below shows how the Cast AI Autoscaler scales cloud resources up and down (both node number and size) with real-time demand, giving enough headroom to meet the application’s requirements.
Handling Spot Instance interruptions
Spot Instances offer great savings, but providers can reclaim these resources at any time. You must ensure that your application is prepared for this and that you have a backup plan in place in case your Spot Instance is interrupted.
Automation is a game-changer here.
Yotpo runs at least 80% of workloads on Spot Instances and integrated Cast AI to manage the entire Spot Instance lifecycle, including:
- Provisioning the most cost-effective instance type and size,
- Moving workloads back and forth between Spots and On-Demand instances when availability changes, and
- Autoscaling instances in line with fluctuating demand.
The graph above shows the efficiency of the production workload when running on Cast AI over 30 days. The autoscaler closely follows the workload’s changing demands, increasing and decreasing provisioned CPUs.
After integrating Cast AI, we didn’t have to do anything during Black Friday, which is amazing. We gained not just compute cost reduction but also a reduction in engineer workload.
No Spot Instances are available? No problem; the workload is automatically moved to On-Demand instances – and Cast AI makes sure to select the cheapest instance that matches the resource consumption of that workload. And once Spots are available again, the workload is moved back there. The whole spike on Black Friday was much lower, which is cool.
Achi Solomon, Director of DevOps at Yotpo
Automation saves both money and time
Cloud cost management is essential for visibility and control, but it doesn’t guarantee savings. Simply knowing where your money goes won’t automatically reduce your spend.
True savings come from automated optimization — continuous, intelligent adjustments that rightsize resources, eliminate waste, and react instantly to changing usage patterns.
By automating these actions, you move beyond manual monitoring and reactive fixes, ensuring that your cloud environment stays efficient without constant human intervention.
