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 that work with Kubernetes: CAST AI and Spot.io. Keep on reading to find out which one supports your use cases better.

CAST AI vs. Spot.io - quick feature comparison

Feature	CAST AI 🥇	Spot.io
Supported platforms
AWS	✅	✅
Google Cloud	✅	✅
Microsoft Azure	✅	✅
Cost visibility
Detailed cluster cost breakdown	✅	✅
Automated cost forecasting	✅	✖
Cost view across multi cloud	✅	✖
Real-time alerting functionality	✖ (coming soon)	✅
Kubernetes cost optimization and automation
Rightsizing
Recommendations for instance types and sizes	✅	✅
Automated rightsizing via AI-driven instance selection	✅	✖
Multi-shape cluster construction	✅	✖
Autoscaling	✅	✅
Automated pod scaling parameters	✅	✅
Horizontal pod autoscaling	✅	✅
Node autoscaling	✅	✅
Cluster scheduling	✅	✅
Automatic bin packing	✅	✅
Spot Instance automation	✅	✅
Full multi cloud optimization	✅	✖

Detailed feature comparison of Spot.io and CAST AI

1. Cost visibility and monitoring
2. Cost optimization and automation
3. Spot instance automation
4. Full multi cloud optimization
5. Security
6. Pricing

1. Cost visibility and monitoring

Cluster cost breakdown & forecasting

In CAST AI, you get a cost breakdown at the project, cluster, namespace, and deployment 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 with all supported cloud service providers.

The platform also comes with a cost allocation feature that works per cluster and per node. A feature for ongoing cloud cost reporting is also in the near-future pipeline since the CAST AI team plans to expand cost dimensions beyond compute - such as control plane, network, egress, storage, and others.

Spot.io breaks down the infrastructure costs of clusters and offers insights into each of the layers. Costs are broken down 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.io 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 for addressing that. Its full multi cloud functionality comes with visibility via universal metrics from Grafana and Kibana that can be used for any supported cloud service providers.

Spot.io currently doesn’t support multi cloud functionality or visibility across its solutions for containerized applications.

2. Cost optimization and automation

Rightsizing

CAST AI uses AI to choose the best instance types and sizes that meet an application’s requirements but still help teams save up on cloud costs. Whenever a cluster needs extra nodes, the AI-driven instance selection algorithm selects instances that achieve 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, since choosing the same instance shape for every node in a cluster may easily lead to overprovisioning. That’s why CAST AI delivers an optimized mix of different instance types adapted to an application’s requirements.

Spot.io offers a handy Right Sizing recommendation mechanism. The tool monitors workload utilization in real-time to provide you with 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. 

Autoscaling

Horizontal pod autoscaling and node autoscaling

CAST AI automates pod scaling parameters to help teams avoid overprovisioning their infrastructures. The platform also includes a Horizontal Pod Autoscaler, which uses business metrics to generate the optimal number of required pod instances. 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.io continuously checks for unschedulable pods and if it finds one, it scales up to make sure that all the 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. To optimize costs, Spot.io prioritizes downscaling the least utilized nodes.

Cluster scheduling and termination

CAST AI automatically pauses and resumes clusters created within the platform to help teams avoid paying for resources they’re not using.

Spot.io makes sure that all that pods and nodes are terminated in a case of scale-down or instance replacement. 

Automatic bin packing 

Kubernetes presents a cost challenge to teams because it distributes applications within a cluster evenly, with no attention paid 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 to lower costs.

Spot.io 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 result in 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 makes sure that the replacement of spot instances that were interrupted is fully automated. This means that teams don’t have to worry about their workloads not finding a place to run. The platform is always on the lookout for better instance alternatives and provisions instances in a split-second to guarantee high availability.

Spot.io allows users to run their clusters on spot instances without the need to provision or scale instances. The tool also takes care of bin packing containers on spot instances to optimize their use even further. 

4. Full multi cloud optimization

As more and more companies use multiple cloud services to access best-in-class services and prevent disasters, the need to monitor, manage, and optimize costs across providers is greater than ever. 

CAST AI meets this need by offering you the option to set up multi cloud clusters using a number of multi cloud features:

• Active-Active Multi Cloud - CAST AI replicates applications and data across multiple cloud services. If one fails, others keep your workloads afloat, guaranteeing business continuity.
• Global Server Load Balancing - the platform distributes traffic evenly across all the participating clouds, always choosing the endpoints that are up and healthy. 
• Multi cloud visibility - CAST AI makes cost allocation across cloud services easier thanks to universal metrics from Grafana and Kibana.

Spot.io doesn’t support the multi cloud functionality at the moment.

5. Security

Since both automation solutions work directly with the 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. CAST AI doesn’t access any environment variables that are considered sensitive such as secrets, passwords, tokens, etc. CAST AI is ISO-certified and well underway to obtaining SOC 2 certification. 

Spot.io 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.

6. Pricing

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 automated cost optimization features - in that case, you can choose between two plans: Growth and Enterprise. CAST AI offers guaranteed savings of at least 50%.

Spot.io offers a free trial after which users can choose from two different pricing models: Pay-as-you-Go without the annual commitment and Subscription that comes with a commitment but opens the doors to more discounts and priority support.

Summary

Overall winner

CAST AI

Both Spot.io and CAST AI are great automation solutions for reducing cloud costs and facilitating processes like cost monitoring, management, and optimization.  

Using spot instances is a smart method for reducing the cloud bill, but it’s just part of the picture. 

By rightsizing other instances - for example, ones purchased within an AWS Savings Plan - you can drive your cloud bill down even more. While Spot.io offers recommendations, CAST AI does the job for you via automated instance selection.

The broad range of optimization features combined with automated cost forecasting and unique multi cloud functionality brings CAST AI to the top of cloud cost optimization platforms.

To accurately estimate the savings that you would be able to get - start with the free AI Cluster Analyzer.