Scaling cloud resources is easy – so easy, in fact, that many teams end up losing control over their cloud spend. A missed bug or architecture oversight can easily snowball into a huge bill at the end of the month.
That’s why teams need a cloud cost monitoring and optimization toolkit that provides detailed visibility, exhaustive reporting, and – in an ideal scenario – automated optimization capable of handling the fast-changing requirements of Kubernetes to generate some serious cost savings.
Here’s a comparison of features delivered by two modern cloud-native solutions, Kubecost and CAST AI.
|CAST AI – Analysis & Automation
Created by industry veterans, CAST AI is a full-service cloud automation platform providing powerful automation features for optimizing Kubernetes workloads. Companies across industries such as e-commerce and adtech are using CAST AI to save from 50% to even 90% on their cloud bills.
|Kubecost – Analysis
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 that teams can use to get insights into costs allocation, cost monitoring, and alerts – key tools for 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 then either connect an existing Kubernetes cluster or create a new one inside the tool. Teams often choose to 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 and Google Cloud Platform, with Azure support coming in Q4 2021.
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 a lot of flexibility for 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 down to individual microservices and generate a detailed forecast of cluster costs.
The CAST AI Cost report allows users to track historical cost data of the cluster to understand how it fluctuated over the time period, what the normalized cost per provisioned CPU was, and how much they’ll have to pay at the end of the month, among others. Moreover, CAST AI delivers insights using universal metrics for any cloud service provider from Grafana and Kibana.
Kubecost provides flexible and customizable cost breakdown features as well. You can divide costs by namespace, deployment, service, and more indicators across all the three major cloud service providers. Like in CAST AI, this comprehensive resource allocation points the way to generating more accurate showbacks and chargebacks, streamlining the ongoing cost monitoring.
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 a unique full multi cloud functionality and visibility, providing 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. However, Kubecost doesn’t help you manage multi cloud infrastructure – while CAST AI offers a full multi cloud solution with cost optimization.
3. Cost monitoring
Cost allocation is the first step to understanding where your cloud bill comes from. Next, you need to keep a close eye on how your resource use translates to costs in real time.
CAST AI displays the biggest 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 can 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 in 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 come up with the number of required pod instances. It scales the replica count of your pods up and down – and removes pods if there’s no work to be done.
- AI-driven instance selection – if your cluster needs extra nodes, CAST AI chooses the best instance types that meet your requirements but still help to save up.
- Multi-shape cluster construction – CAST AI delivers an optimized mix of different instance types that are 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 really help teams reduce their cloud spend. CAST AI solves this problem via bin packing for maximum savings.
- Spot Instance automation – Spot Instances can bring dramatic savings of up to 90% off the On-Demand pricing. You don’t need to worry about a provider pulling the plug on your instance – their replacement is fully automated.
- Node autoscaling – this feature makes sure that your nodes match your requirements at all times, scaling nodes up and down automatically.
- Cluster scheduling – automatically pause and resume clusters to avoid paying for resources your teams aren’t using.
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.
Since both Kubecost and CAST AI work with your cloud infrastructure, their security is paramount.
CAST AI offers a bunch of 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 errors in your clusters.
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 comes with a free savings report users can run anytime they want to check whether they could save up on their infrastructure. The report generates actionable recommendations. And if you want to add automated optimization into the mix, you can choose between two plans: Growth and Enterprise. In all cases, CAST AI offers guaranteed savings of 50%.
All Kubecost plans are free of charge for the first 30 days. Kubecost also offers a free plan where you can monitor and optimize one cluster. To make the most of your paid plan, 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 are great picks that offer lots of value to Kubernetes teams looking to optimize their cloud bills and streamline processes related to cost monitoring, allocation, and reporting.
But if you’d like more than reporting, the automated optimization features of CAST AI are at the top among cloud cost optimization platforms. By combining cost reporting with automated cost optimization, CAST AI gives you an end-to-end solution that keeps your cloud costs in check and generates some pretty impressive savings.
Learn how much you can save
Run a free CAST AI savings repor