Automation is the best answer to controlling cloud costs, especially if you work with modern solutions like containers and Kubernetes.
But automated optimization solutions come in different forms: they focus on automating different cost-saving tactics and give you various levels of visibility and control.
Here’s a comparison of two automation solutions available to teams working with Kubernetes: CAST AI and Spot by NetApp. Keep on reading to find out which one supports your use cases better.
CAST AI – Complete Kubernetes automation platform for cost reduction, monitoring, and security insights
Spot by NetApp – automated spot instance and reserved capacity management with limited security analysis
CAST AI is a comprehensive cloud automation platform for managing and monitoring Kubernetes costs and security. Companies across different industries use it to save an average of 65% on their cloud bills.
Cost automation features include spot instance management, autoscaling up & down, rightsizing, and more to ensure predictable cloud operations. Moreover, CAST AI’s support matches the speed and quality of the most demanding implementations.
Spot by NetApp (formerly Spot.io) started as a cloud management solution focused on optimizing cloud costs with spot instance automation.
The tool helps teams get spot capacity for their workloads automatically, cutting costs and ensuring high availability. The product now also offers cloud security analysis for the three major cloud providers.
CAST AI vs. Spot by NetApp – quick feature comparison
|Feature||CAST AI 🥇||Spot.io|
|Google Cloud Platform||✅||✅|
|Cost allocation and visibility|
|Detailed cost allocation||✅||✅|
|Automated cost forecasting||✅||✅|
|Cost view across multi cloud||✅||✅|
|Advanced cost monitoring||✅||✖|
|Cost optimization and automation|
|Automated rightsizing implementation||✅||✖|
|Multi-shape cluster construction||✅||✖|
|Pod parameter-based autoscaling||✅||✅|
|Automatic bin packing||✅||✅|
|Full lifecycle automation||✅||✅|
|Capacity fallback guarantee||✅||✅|
|Free live chat support||✅||✖|
|Dedicated Slack channel||✅||✖|
Detailed feature comparison of Spot.io and CAST AI
- Cost visibility and monitoring
- Cost optimization and automation
- Spot instance automation
1. Cost visibility and monitoring
Cluster cost breakdown & forecasting
In CAST AI, you get a cost breakdown at the workload, node, cluster, namespace, and label level. Teams can analyze costs down to individual microservices and then generate a detailed forecast of their cluster costs for better planning. CAST AI uses universal metrics that work across all three major cloud service providers.
Using CAST AI’s cost monitoring feature, users can track historical data of the cluster to understand how its cost fluctuated over time, see its normalized cost per provisioned CPU, and how much they’ll have to pay at the end of the month, among others.
Spot by NetApp breaks down the infrastructure costs of clusters and offers insights into each layer. You can break down the costs into namespaces and individual workloads within every namespace, with the option to filter them according to your container labels and annotations. For each workload, Spot by NetApp displays compute and storage costs.
Users can use this data to analyze the application costs, perform chargebacks without extensive resource tagging, and estimate future cloud spend.
Cost view across multi cloud
Many companies use more than one cloud service today. Allocating costs in a multi cloud context is challenging, but CAST AI equips teams with insights addressing this issue. It ensures visibility into cluster costs across AWS, GCP, and Azure and this is further supported by integrations to Prometheus and Grafana.
Spot by NetApp supports multi cloud visibility across its solutions for containerized applications. As a result, you view your cloud costs across all cloud accounts.
2. Cost optimization and automation
CAST AI uses AI to choose the best instance types and sizes that fully meet your application’s requirements but still help you cut cloud costs. Whenever a cluster needs extra nodes, the AI-driven instance selection algorithm selects the instances that ensure maximum performance at a minimum cost. Your team doesn’t have to lift a finger because it all happens automatically.
The platform also comes with multi-shape cluster construction so you no longer have to care about maintaining node pools. With CAST AI, you always get an optimized mix of different instance types adapted to your application’s requirements.
Spot by NetApp offers recommendation mechanisms helping you place pods and tasks on the VMs that fit their resource requirements and other scheduling constraints. The tool monitors workload utilization in real-time to deliver recommendations for adjusting the resource requirements of workloads when they start consuming significantly more or fewer resources than requested.
The solution provides recommendations per container and summarizes them for the entire workload to enable easier visualization at a high level and faster implementation.
CAST AI’s autoscaler uses business metrics to generate the optimal number of required pods. The feature then scales the replica count of your pods up and down, scaling to zero and removing all pods if there’s no work to be done. CAST AI also ensures that the number of nodes in use matches the application’s requirements at all times, scaling nodes up and down automatically.
Spot by NetApp continuously checks for unschedulable pods and if it finds one, it scales up to make sure that all pods have a place to run. The solution also removes nodes automatically when all pods running on the node can be moved to other nodes in the cluster. Spot prioritizes downscaling the least utilized nodes.
Automatic bin packing
Kubernetes can be challenging cost-wise because it distributes applications within the provisioned resources evenly without paying attention to how cost-effective this setup is.
CAST AI changes the default pod scheduling behavior and uses automated bin-packing to achieve maximum savings in line with your settings. Fewer nodes translate into lower costs.
Spot by NetApp uses bin-packing algorithms as well. When the tool identifies an instance where workloads may be distributed across the cluster, it triggers a scale-down to drain and terminate the instance.
3. Spot instance automation
Spot Instances can bring dramatic savings of up to 90% off the on-demand pricing. But there’s a catch – the cloud provider can pull the plug anytime. That’s why automation is so critical for effective spot instance use.
CAST AI ensures full automation of selecting and provisioning spot instances available in the marketplace and handling interruptions. This means that teams don’t have to worry about their workloads not finding a place to run.
The platform always looks for better instance alternatives and provisions new instances to guarantee high availability. In case of no spot availability, the fallback feature moves your workload temporarily to an on-demand instance to ensure its continuous operation. CAST AI further optimizes your containers on spot instances with bin packing.
Spot by NetApp allows users to run clusters on spot instances without the need to provision or scale instances. It continuously analyzes your usage and provides autoscaling groups that optimize resources to ensure availability and meet workload demands using the lowest-cost compute options. The tool also takes care of bin packing containers on spot instances to optimize their use even further.
Note: Spot instance automation is the main cost optimization method used by Spot by NetApp, the tool is therefore likely to suggest running workloads on more spot instances. CAST AI, on the other hand, analyzes workload requirements to check if running it on spot instances is the best choice in a given case and if not, picks more suitable optimization methods, from automated instance rightsizing to autoscaling.
Since both automation solutions work directly with your cloud infrastructure, their security is essential.
Created by cybersecurity experts, CAST AI offers a number of security features such as encryption at rest/in transit, secrets management, network security, logging, visibility, and more. The platform also comes with automatic patching and upgrades to VMs and Kubernetes to eliminate the chance of errors in your clusters.
The platform doesn’t access any environment variables considered sensitive such as secrets, passwords, tokens, etc. CAST AI is both ISO 27001 and SOC 2 certified.
Moreover, CAST AI now provides free access to container security insights. Released in 2022, the feature gathers key security insights in one place, enabling you to run vulnerability checks and prioritize issues to ultimately improve your K8s configuration.
Spot by NetApp encrypts account data within the browser and re-encrypts it with a secure algorithm when it reaches its servers. The tool doesn’t store any private keys, passwords, or authentication tokens – authentication is based on the IAM Cross Account Role & External ID.
Spot by NetApp also includes a security module, which identifies the most critical vulnerabilities based on potential attack surface and supports prioritized remediation.
CAST AI offers a free Savings report you can run to check whether you could save up on your cloud bill. The read-only agent analyzes your setup and generates actionable recommendations.
You can implement them manually or turn on automated cost optimization features – in that case, you can choose between three premium plans: Growth, Growth PRO, and Enterprise. In each plan, the platform offers guaranteed savings of at least 50%.
CAST AI charges you for the actual number of CPUs in management, not the percent of savings it helps you generate, as is the case with Spot by NetApp.
Spot by NetApp offers a free trial after which users can choose from two different pricing models: Pay-as-you-Go without the annual commitment or Subscription that comes with a commitment but opens the door to more discounts and priority support. In both cases, the % of savings isn’t publicly available and needs to be negotiated with sales.
CAST AI provides users with different forms of support. Free account users can consult product documentation and ask questions in a live chat or community Slack. Premium plans come with more customized forms of support, from a priority live chat support to 24/7 dedicated Slack channel support.
What many CAST AI clients appreciate is the quality and speed of support they receive and how well it compliments their product development efforts. They also appreciate the fact that the product is being continuously developed to match their emerging needs.
The improvements of CAST AI over the last couple of months were amazing. The support team from CAST AI are really helpful and friendly. They’re always available and ready to improve their product. Personally, I’d recommend CAST AI to anyone – it’s one of the good vendors with which we interacted in terms of the relationship or support. The platform was able to show its value in under one month.— Jenson C S, Engineering Manager at ShareChat
Spot by NetApp also offers several levels of support. Free account users can consult product documentation and write email to support in standard business hours (9-5 on weekdays). Premium plans – pay-as-you-go and annual – provide users with email and live chat options in the console.
However, more demanding or complex implementations in need of adjusting the product may not be properly served right now.
Both Spot by NetApp and CAST AI are great automation solutions for cloud cost optimization, monitoring, and getting container security insights.
Spot instance automation is a smart method for reducing the cloud bill, but it’s just part of the picture.
By rightsizing other instances – for example, the ones purchased within an AWS Savings Plan – you can drive your cloud bill down even more, as shown in this example. While Spot by NetApp offers recommendations, CAST AI does the job for you via automated instance selection.
The broad range of optimization features combined with automated cost forecasting,monitoring, and dedicated support brings CAST AI to the top of cloud cost optimization platforms.
P.S. If you prefer a hands-on approach, you can always run the free Cluster Savings report to see what the platform could save you automatically.
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Talking in terms of savings on AWS bill, what would be the difference between spot and cast? How much on top you could save?