FinOps teams are under constant pressure to control cloud costs while ensuring applications maintain high availability and performance. Cast AI takes that burden off their shoulders with automation that continuously optimizes spending, maximizes resource utilization, and eliminates waste – all without manual effort.
How does Cast AI support FinOps teams on their mission to balance cost and performance? Keep reading to find out.
Cost monitoring: optimal cloud cost visibility and management
Cast AI’s monitoring, designed with cloud-native teams in mind, displays all expenses in one place and allows you to break them down by Kubernetes categories such as cluster, workload, and namespaces. The functionality enables you to analyze your cluster’s efficiency, resource usage, and cost fluctuations over time.
Here’s what a cluster cost dashboard looks like:
What can you check using these cost monitoring reports?
- Insights into your most expensive workload, efficiency, and lost resources.
- Your daily and monthly cluster spending and its efficiency.
- Spend for On-Demand, Spot, and fallback nodes.
- Cost per CPU and memory.
- Monthly spending forecasts and estimates for your end-of-month bill.
- And much more.
Let’s take a look at the main reports you can find in the cost monitoring part of Cast AI
Main cost monitoring reports
Cast’s cost monitoring consists of five primary areas with varying levels of detail. Here’s what you’ll find in each one:
Cluster report
It provides a summary of cluster expenses, including compute expenditure, cost per supplied resource, average daily cost, and daily compute spend information, such as cost per CPU and memory. You also get a projection of your final monthly bill and the total change from the previous month.
Workloads report
It displays the compute cost for each task, along with the controller type and namespace, as well as the total cost per CPU and memory. Your results can be further filtered by labels and namespaces. Furthermore, Workload Efficiency shows the difference between the requested and utilized resources for each workload, enabling you to quantify the value of wasted resources.
Namespaces report
This report includes information about the compute cost for each namespace, such as the average CPU and MEM requirements per hour and the overall cost per resource.
Allocation Groups report
In this report, you can see details about the allocation groups you add to your cluster. These custom workload groups allow you to allocate costs by categorizing workloads into namespaces or labels.
Cost comparison
This allows you to evaluate the cost of the required CPUs over different time periods to determine the level of delivered savings.
Why FinOps teams use Cast’s cost monitoring
- Free for unlimited clusters – Your clusters can be any number or size.
- Available immediately – You receive cost information immediately after connecting your cluster, and Cast updates data in real-time every 60 seconds.
- Evaluate your Kubernetes efficiency – Clearly distinguish between the provisioned and requested resources.
- Billing access is not necessary – Cast uses public pricing, so you do not need to provide billing information.
- Unlimited access to historical data – You can analyze months of historical data for free.
Success story: Impactful cost management with full visibility
Iterable, a top-rated AI-powered customer communication platform, was looking to optimize its cloud costs. However, the team lacked the necessary mechanisms for measuring or reducing them.
With reliability as its critical priority, the team was hesitant to shrink the cluster’s size without full clarity on costs and potential overprovisioning. This was when the team turned to Cast.
There are a lot of offerings on the marketplace, a lot of services. But one of the things about Cast AI was that I could use it in a read-only mode, which tells me how much we’re spending on our cluster at any given time.
Once I know how much we’re spending at any given time, I can run tests on adding new node types or moving workloads and see if that delivers the savings I’m looking for. Before that, it was all shots in the dark.”
Jason Sanghi, Staff Software Engineer, SRE at Iterable
Cost visibility and monitoring were just the starting points, as Iterable needed a solution combining these capabilities with automated cost optimization. This is where Cast delivered tremendous value and significant cost savings of 60%.
During rebalancing, suboptimal nodes are automatically replaced with new ones that are more cost-efficient and run the most up-to-date node configuration settings. As a result, Cast removes around 2000 CPUs, reducing the number of nodes by 10% and generating significant savings. During low-demand periods, such as nighttime, rebalancing can achieve up to 100% savings by draining and removing the nodes altogether.
Cost optimization: Tangible cost savings via automation
Gaining visibility into cloud spending is just the first step. FinOps teams want to understand the spending patterns, but also reduce these costs. This is where automation comes in.
Cast delivers a number of automation features that, when combined with its in-depth cost monitoring, offer FinOps teams a one-stop solution to controlling their cloud spend.
Cast automation features for FinOps teams
- Automated Instance Selection – Cast continuously chooses the most cost-efficient compute instances across providers for optimal performance and minimal cloud cost.
- Intelligent Cluster Autoscaling – Scales clusters up or down automatically based on real-time workload demand, eliminating overprovisioning and idle spend.
- Pod Bin-Packing – Packs pods onto fewer nodes through smart scheduling, improving utilization and reducing unused capacity.
- Spot Instance Automation – Dynamically provisions and replaces Spot Instances with fallback to on-demand for uninterrupted operation and maximum savings.
- Automated Workload Rightsizing – Adjusts CPU and memory requests in real time without downtime, ensuring workloads run efficiently and at the lowest possible cost.
- Savings Plan & RI Utilization – Automatically routes workloads to use existing AWS Savings Plans and Reserved Instances first, so no prepaid capacity goes to waste.
Success story: FinOps automation for Kubernetes
A unicorn that achieved a $2.5 billion valuation in 2022, Games24x7 is an India-based multi-game platform that serves games to over 100 million users. Technology leaders at Games24x7 have implemented a number of FinOps best practices to spread cost awareness among their engineering teams and keep cloud costs in check:
Cost was the main driver of our decision to try Cast AI. The team worked closely with us and helped us understand what the potential cost savings are. We started seeing benefits in cost reduction and ease of operations right after integrating Cast into our FinOps stack.
I don’t have to think about what kind of cost optimization I need to do, what kind of security best practices or other controls we need to have in place, and things like that. Cast is super easy and helpful that way.”
Sanjay Kumar Singh, Head of DevSecOps at Games24x7
Automation made the entire process of managing Kubernetes costs and resources easier, helping Games24x7 focus on fine-tuning its FinOps model.
This makes Cast AI best for FinOps teams
Cast AI provides FinOps teams with the visibility, control, and automation they need to maintain predictable and efficient cloud costs. Instead of manually tracking spend or juggling complex billing data, teams get a single platform that continuously optimizes resources, rightsizes workloads, and ensures every dollar of committed spend is fully utilized.
With real-time cost insights and automated savings actions, FinOps professionals can transition from reactive reporting to proactive cloud cost optimization, turning cloud financial management into a strategic advantage.
Kubernetes cost optimization
Monitor resource spending, automate resource allocation, and scale instantly with zero downtime.
