Scaling cloud resources is so easy that many teams lose control over their cloud spend. An architecture oversight can quickly snowball into a massive bill at the end of the month. That’s why Kubernetes cost management and optimization tools like Kubecost or CAST AI are a must.
Such tools can provide detailed visibility and exhaustive reporting but, ideally, also deploy automated optimization to handle the fast-changing requirements of Kubernetes and generate considerable savings.
So here’s a comparison of two cloud-native cost management solutions – Kubecost and CAST AI.
|CAST AI – cloud cost analysis, monitoring & automated optimization|
Created by industry veterans, CAST AI is a complete Kubernetes cost management platform with powerful automation features for optimizing Kubernetes workloads. CAST AI users save on average from 50% to even 90% on their cloud bills.
|Kubecost – advanced cloud cost analysis and reporting|
Kubecost started as an open-source tool that provided developers with more visibility into their Kubernetes costs. Today, Kubecost is a robust cost reporting solution providing insights into cost allocation, cost monitoring, and alerts – supporting teams looking to gain visibility.
Kubecost vs. CAST AI – quick feature comparison
|Feature||CAST AI 🥇||Kubecost|
|Google Cloud Platform||✅||✅|
|Cost allocation and visibility|
|Detailed cost allocation||✅||✅|
|Automated cost forecasting||✅||✅|
|Cost view across multi-cloud||✅||✅|
|Cost optimization and automation|
|5-minute first optimization||✅||✖|
|Multi-shape cluster construction||✅||✖|
|Pod parameter-based autoscaling||✅||✖|
|Automatic bin packing||✅||✖|
|Full lifecycle automation||✅||✖|
|Capacity fallback guarantee||✅||✖|
Detailed feature comparison of Kubecost and CAST AI
- Installation and setup
- Cost allocation
- Cost monitoring
- Cost optimization and automation
1. Installation and setup
To start saving on your cloud bill with CAST AI, you need to create an account and connect an existing Kubernetes cluster or create a new one inside the tool. Teams often connect their clusters in read-only mode to get a free detailed report of estimated monthly savings – and then take action by turning automated optimization on. It takes only 15 minutes to get the cost analysis and optimize costs automatically.
Supported platforms: At the moment, CAST AI supports services from AWS, Google Cloud Platform, and Microsoft Azure.
To install and operate Kubecost, teams can use the Kubecost helm chart. This installation method brings you all the components for getting started, offering access to Kubecost features and an opportunity to scale to large clusters. Teams can also enjoy much flexibility in configuring Kubecost and its dependencies. Kubecost offers three other installation options, but they require effort and come with less flexibility.
Supported platforms: Currently, Kubecost supports cloud services from AWS, Google Cloud Platform, and Microsoft Azure.
2. Cost allocation
Detailed cost breakdown
CAST AI offers a cost breakdown and forecasting feature at the level of projects, clusters, namespaces, and deployments. You can analyze costs to individual microservices and generate a detailed forecast of cluster costs.
The CAST AI Cost report allows users to track historical cluster data to understand how it fluctuated over time, the normalized cost per provisioned CPU, and the height of their bill at the end of the month, among others. Moreover, CAST AI uses Grafana and Kibana metrics which work across different cloud service providers.
Kubecost provides flexible and customizable cost breakdown features as well. You can divide costs by namespace, deployment, service, and more indicators across all three major cloud service providers. Like in CAST AI, this comprehensive resource allocation points the way to generating more accurate showbacks and chargebacks, streamlining ongoing cost monitoring processes.
Allocation by organizational concepts
Focusing on automated optimization, CAST AI offers cost allocation per cluster and per node.
Kubecost users can allocate costs to concepts such as teams, individual applications, products, projects, departments, or environments.
Cost view across multi cloud
Many companies are using the services of more than one cloud provider. Allocating costs across clouds is tricky, but CAST AI rises to this challenge. It supports teams with universal metrics for any cloud provider.
Kubecost displays the costs across multiple clusters and multi-cloud environments in a single view or through a single API endpoint.
3. Cost monitoring
Cost allocation is the first step to understanding your cloud bill. Next, you need to closely monitor how your resource use translates to costs in real time.
CAST AI displays the most significant cost driver – compute costs – in the Savings estimator and shows potential savings associated with deployments on Spot Instances. It also offers ongoing cloud cost reporting that explores CPU costs in detail.
Kubecost allows teams to link real-time in-cluster costs (CPU, memory, storage, network, etc.) with out-of-cluster expenses from the cloud services across AWS, GCP, and Azure – for example, tagged RDS instances, BigQuery warehouses, or S3 buckets. Users get context-aware, cluster-level reports to reach an optimal balance between cost and performance matching their service requirements.
4. Cost optimization and automation
Once you allocate costs and monitor them on a regular basis, it’s time to take action and start optimizing your spend. Kubecost and CAST AI support teams on this mission differently.
CAST AI: Fully automated cost optimization that beats savings plans
- Pod autoscaling – this feature uses business metrics to determine the number of required pod instances. It scales the replica count of your pods up and down – and removes pods if they’re no longer needed.
- AI-driven instance selection – if your cluster needs extra nodes, CAST AI chooses the optimal instances that meet your requirements and come at the best price.
- Multi-shape cluster construction – CAST AI delivers an optimized mix of different instance types adapted to your application’s needs.
- Automated pod scaling parameters – to help teams avoid overprovisioning, CAST AI sets these parameters automatically and maximizes cost savings.
- Automatic bin packing – since Kubernetes distributes applications within a cluster evenly, it doesn’t help teams reduce their cloud spend. CAST AI solves this problem via bin packing for maximum savings.
- Spot instance automation – spot instances can bring up to 90% of on-demand pricing savings. You don’t need to worry about a provider pulling the plug on your instance – their replacement is fully automated.
- Node autoscaling – this feature ensures that your nodes match your requirements at all times, automatically scaling nodes up and down.
Savings: By turning CAST AI automated optimization on, you can save from 50 to 90% on your cloud spend.
Kubecost: Cluster-level insights and recommendations for engineers to implement
Kubecost provides detailed reports and real-time alerting functionality. Delivered via Slack or email, these alerts notify teams about budget overruns, anomalous spend patterns, and Kubernetes tenants that fall below the set efficiency levels. Users can set budgets for configurable aggregation levels – for example, team or application.
Savings: Kubecost generates insights DevOps engineers can use to save 30-50% or more.
Cloud infrastructure security is paramount to both Kubecost and CAST AI.
CAST AI offers multiple security features, such as encryption at rest/in transit, secrets management, network security, logging, visibility, and more. Moreover, it provides automatic patching and upgrades to VMs and Kubernetes, so you’re always kept up to date and eliminate the chance of error in your clusters.
The platform’s new security feature additionally checks up on, prioritizes, and reports on your K8s cluster configuration and vulnerability issues.
Kubecost doesn’t expose private data anywhere, and since users deploy Kubecost in their infrastructure, there’s no need to egress any data to a remote service. You retain and control access to sensitive cloud spend data at all times.
CAST AI delivers three powerful K8s cost and security optimization tools for free. Thanks to a free savings report, users can check how much they could save on their infrastructure. Cost monitoring allows them to keep a close eye on all expenses in real time, while the security feature identifies and prioritizes configuration issues and vulnerabilities.
Premium CAST AI plans use automation to streamline your optimization efforts. You can now choose between three plans: Growth, Growth PRO and Enterprise. In all cases, CAST AI offers guaranteed savings of 50% without the need to handle manual implementation tasks.
Kubecost offers a free plan which allows you to monitor and optimize one cluster. All premium plans are also free for the first 30 days. However, whichever paid plan you choose, you’ll need to dedicate time to implement the recommendations provided by Kubecost. This will incur extra charges and doesn’t automatically guarantee savings.
When to choose CAST AI vs. Kubecost
Both Kubecost and CAST AI offer a lot of value to teams looking to optimize their cloud bills and streamline processes related to cost monitoring, allocation, and reporting.
But if you’d like more than cost reporting and optimization recommendations, CAST AI’s automated features will be a safe bet to deliver outstanding and lasting results fast.
By combining cost monitoring with automated cost optimization, CAST AI is an end-to-end solution that keeps your cloud costs in check and generates impressive savings.
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Leave a reply
Do you have any estimate of when AKS will be supported? Would like to play around
Hey Matt, The time to check out AKS is now as we just have started supporting it, so drop by and say hi!
You mentioned, that Kubecost is an open-source tool that a team can use. Is CAST AI fully autonomous after turning automated optimization on or does it have to be supervised by a team member as well?
Great question Victor! To be short, we would recommend keeping an eye for some time on your clusters that are managed by CAST systems but in the long run, it’s as hands-free as it gets with cluster management and will not require a separate team to manage. 🙂
By the comparison chart looks that CAST AI has pretty much all futures Kubecost does, so what additional advantage would be using both of them and are you refering to free versions of Kubecost?
Well, Kubecost has its free tier for your one cluster that you can always keep an eye out without upgrading. It gives you some insights on what could be done differently, but the action has to be taken on your part. While at CAST, we offer a 14-day free trial that you can use with multiple clusters that you have and get not only insights on what could be done differently but act on it for free.
If I understand the Cost View section correctly, both services provide solutions for better multicloud management. However, with Kubecost the comparison has to be done manually, while CAST AI makes the comparison itself, showing what can be improved. Is that correct?
To make a quick adjustment to your understatement – Kubecost shows your cloud bill costs even across multi-cloud clusters, but that’s where their hand’s reach ends, while CAST will also do that but on top of that, it will be able to create/manage/delete/scale/enable spot-instances and more on your multicloud clusters. So kubecost -> Viewing, CAST -> viewing, managing.
Which of the features available for Kubecost (mentioned in Cost Monitoring section) does CAST AI provide or will in the future? I’m especially interested in features related to Azure
Hey Lewis, We are currently working on better cost visibility than compute only. That will include network traffic costs, data costs, and some more that we are yet to decide, but we are well aware of the need for more informational tools that other products might offer. Thanks for the comment thought!
There’s a mention about pricing plans for Growth and Enterprise. Could you please provide more information about the features and differences of the two?
Are there any case studies to show how node autoscalling or cluster reshaping works?