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. Wondering whether you need something more than just cost reporting and analysis? Here’s a comparison of features delivered by two modern cloud-native solutions, Kubecost and CAST AI. CAST AI - Analysis & Automation Kubecost - Analysis 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 ad tech are using CAST AI to save from 50% to even 90% on their cloud bills. 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 Supported platforms AWS ✅ ✅ Google Cloud Platform ✅ ✅ Microsoft Azure ✅ (coming soon) ✅ Cost optimization and automation Detailed insights on cluster cost optimization ✅ ✅ Recommendations for optimizing cloud costs ✅ ✅ Real-time alerting functionality ✅ (coming soon) ✅ Horizontal pod autoscaling ✅ ✖ AI-driven instance selection ✅ ✖ Multi-shape cluster construction ✅ ✖ Automated pod scaling parameters ✅ ✖ Automatic bin packing ✅ ✖ Spot Instance automation ✅ ✖ Node autoscaling ✅ ✖ Cluster scheduling ✅ ✖ Cost allocation Detailed cost breakdown ✅ ✅ Allocation by organizational concepts ✖ ✅ Cost view across multi-cloud ✅ ✅ Live customer support ✅ ✖ Full multi-cloud optimization ✅ ✖ Detailed feature comparison of Kubecost and CAST AI 1. Installation and setup CAST AI 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 choose to 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. connect their clusters At the moment, CAST AI supports services from AWS and Google Cloud Platform, with Azure support coming in Q4 2021. Supported platforms: Kubecost 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 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. Moreover, CAST AI delivers insights using universal metrics for any cloud service provider from Grafana and Kibana. CAST AI 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 leads to generating more accurate showbacks and chargebacks, streamlining the ongoing cost monitoring. Kubecost Allocation by organizational concepts Focusing on automated optimization, offers cost allocation per cluster and per node. CAST AI users can allocate costs to concepts such as teams, individual applications, products, projects, departments, or environments. Kubecost Cost view across multi-cloud Many companies are using the services of more than one cloud provider. Allocating costs across clouds is tricky, but rises to this challenge. It supports teams with a unique , providing universal metrics for any cloud provider. CAST AI full multi-cloud functionality and visibility 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. Kubecost 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. ​ displays the biggest cost driver - compute costs - in the Savings estimator and shows potential savings associated with deployments on Spot Instances. It has a planned feature in the pipeline for , including other dimensions such as control plane, network, egress, storage, and others. CAST AI ongoing cloud cost reporting allows teams can link (CPU, memory, storage, network, etc.) with 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 find an optimal balance between cost and performance matching their service requirements. Kubecost real-time in-cluster costs out-of-cluster expenses 4. Cost optimization and automation Once you allocate costs and monitor them regularly, it’s time to take action and start optimizing your spending. Kubecost and CAST AI support teams on this mission differently. CAST AI: Fully automated cost optimization that beats savings plans - 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 there’s no work to be done. Horizontal pod autoscaling - if your cluster needs extra nodes, CAST AI chooses the best instance types that meet your requirements but still help to save up. AI-driven instance selection - CAST AI delivers an optimized mix of different instance types adapted to your application’s needs. Multi-shape cluster construction - to help teams avoid overprovisioning, CAST AI  sets these parameters automatically and maximizes cost savings. Automated pod scaling parameters - 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. Automatic bin packing - 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. Spot Instance automation - this feature makes sure that your nodes match your requirements at all times, scaling nodes up and down automatically. Node auto-scaling - automatically pause and resume clusters to avoid paying for resources your teams aren’t using. Cluster scheduling By turning CAST AI automated optimization on, you can save from 50 to 90% on your cloud spend. Savings: Kubecost: Cluster-level insights and recommendations for engineers to implement Kubecost provides detailed reports and 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. real-time alerting functionality. Savings: Kubecost generates insights DevOps engineers can use to save 30-50% or more. 5. Security Since both Kubecost and CAST AI work with your cloud infrastructure, their security is paramount. offers many 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. CAST AI 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. Kubecost 6. Pricing comes with a free Cost 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 CAST AI guaranteed savings of 50%. All 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. Kubecost Summary: When to choose Kubecost vs. CAST AI Both Kubecost and CAST AT 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. Bringing Kubecost and CAST AI together will give you an end-to-end solution that will keep your cloud costs in check and generate some pretty impressive savings. BTW. You can use KubeCost together with CAST AI.

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Kubecost vs. CAST AI: A Perfect Match for End-to-End Cloud Cost Optimization

Kubecost vs. CAST AI: A Perfect Match for End-to-End Cloud Cost Optimization

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