CAST AI vs. CloudCheckr: Which one is a better choice for your team?

Laurent Gil
· 7 min read
CAST AI vs. CloudCheckr

Keeping cloud costs in check is a challenge for many teams. Luckily, they can now benefit from various cost management and optimization solutions that often come with handy automation, which requires no extra work from engineers and guarantees savings. 

Here’s a comparison of two cost optimization solutions for teams that work with the three major cloud service providers: CAST AI vs. CloudCheckr. Keep on reading to find out which one can support your teams better.

CAST AI – automated cost optimization for Kubernetes 

CloudCheckr – cloud cost management and visibility

Created by cybersecurity experts, CAST AI is an ISO 27001-certified, comprehensive cloud automation platform for optimizing Kubernetes environments. Companies across e-commerce and adtech are using CAST AI to save from 50% to even 90% on their cloud bills. 

CloudCheckr is a cloud management tool that focuses on reporting and generating recommendations for optimizing cloud costs. It started as a cloud security platform and later expanded into cost management, spanning over cost tracking, optimization, and resource inventory. 

CAST AI vs. CloudCheckr – quick feature comparison

Feature

CAST AI 🥇

CloudCheckr

Supported platforms

   

AWS

Google Cloud

Microsoft Azure

✅ (coming soon)

Cost allocation and visibility

   

Detailed cost allocation

Cost reporting

Real-time alerts

Cost view across multi cloud

✅ (limited)

Cost optimization and automation

   

Automated rightsizing

Horizontal pod autoscaling and node autoscaling

Node autoscaling

Cluster scheduling and termination

Automatic bin packing 

Spot instance automation

Full multi cloud optimization

Detailed feature comparison of CloudCheckr and CAST AI

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

1. Cost allocation and visibility

Cost allocation and reporting 

In CAST AI, costs are broken down at the project, cluster, namespace, and deployment levels. Teams can track expenses down to individual microservices and then create a comprehensive estimate of their cluster costs to help with planning. CAST AI employs universal metrics that are compatible with any cloud service provider.

The cost allocation functionality in CAST AI operates per cluster and per node. The platform is planning to extend the reported cost dimensions to control plane, network, egress, storage, and others. The Capability for ongoing cloud cost reporting is also in the works.

CloudCheckr offers a detailed view of cloud cost allocation data in its Cost Changes Report. Teams can get instant visibility into their expenses across the supported cloud service providers. The Cost Summary Report, on the other hand, displays cloud costs over time in the monthly format, allowing teams to interact with the data and improve the accuracy of their billing.

Real-time alerting

The CAST AI team is currently working on the real-time alerting functionalities that notify users when their cloud spend passes the set threshold to eliminate the risk of a service bill spiraling out of control.

CloudCheckr includes an alerting feature paired with cloud governance to give teams more control over their costs and help them avoid any surprises.

Cost view across multi cloud

Since many companies use more than one cloud platform today, multi cloud support is necessary for supporting any cost optimization effort. 

Allocating expenses for multi cloud environments is difficult, but CAST AI makes it much easier thanks to its extended multi-cloud capabilities. The platform solves cross-cloud visibility by using Grafana and Kibana universal metrics that work with any cloud service provider.

CloudCheckr comes with a report that shows cloud spend across various cloud services and providers, but – compared to CAST AI – its multi cloud functionality is more limited.

2. Cost optimization and automation

CAST AI – fully automated cost optimization

Automated rightsizing with AI-driven instance selection 

CAST AI uses artificial intelligence to select the optimal instance types and sizes and match an application’s needs while also reducing cloud expenses. When a cluster requires more nodes, the platform’s automation engine chooses instances that provide the best performance at the lowest cost. Teams don’t have to do anything extra because everything is automated.

Given that picking the same instance shape for every node in a cluster can easily lead to overprovisioning, the platform includes multi-shape cluster creation. As a result, CAST AI provides an optimal combination of different instance types in line with the application’s needs.

Horizontal pod autoscaling and node autoscaling

To help teams avoid overprovisioning their infrastructures, CAST AI automates pod scaling parameters. The Horizontal Pod Autoscaler uses business metrics to generate the ideal number of the required pod instances. 

If there is no work to be done, the feature scales the replica count of your pods up and down, eventually scaling to zero and eliminating all pods. CAST AI also guarantees that the number of nodes in use always suits the application’s requirements, autonomously scaling nodes up and down.

Cluster scheduling and termination

To help teams avoid paying for resources they don’t utilize, CAST AI automatically stops and resumes clusters created within the platform.

Automatic bin packing 

Teams face a cost challenge with Kubernetes since it distributes applications equally across a cluster with no regard for how cost-effective this design is.

CAST AI modifies the default pod scheduling behavior and uses automatic bin-packing to maximize savings according to set preferences. Fewer nodes bring greater cost savings.

CloudCheckr – recommendations and limited automation

Detailed reports and recommendations

CloudCheckr uses predictive analytics to generate resource purchasing recommendations for its users. The platform identifies wasted resources and provides resource re-sizing recommendations to reduce costs.

To accomplish that, CloudCheckr employs 600+ Best Practices checks that check for idle resources, unused instances, and mismatches in EC2 Reserved Instances, and more. The engine generates recommendations only for rightsizing and snapshot cleanups. 

Another interesting reporting feature is the Savings Plan Recommendations report that helps teams to check which services they deployed could be covered by their Savings Plans create/customize purchase recommendations.

The platform’s approach to cost optimization relies on policy-based management and focuses on reporting problems rather than offering automated solutions. Still, CloudCheckr comes with a few helpful automation features.

Automation in CloudCheckr:

  • The platform automatically reallocates, resizes, and modifies Reserved Instances. It keeps historical data for tracking RI inventory throughout the entire lifecycle and helps teams make future purchases.
  • CloudCheckr automatically starts and stops EC2 instances so they run only when needed. 
  • The platform automatically enforces tag-or-terminate policies for better infrastructure control.

3. Spot instance automation

Spot Instances can generate savings of up to 90% off the On-Demand pricing. However, there is a catch: the cloud provider can terminate the insurance at any time. That’s why successful use of Spot Instances depends on automation.

CAST AI ensures that the replacement of interrupted spot instances is fully automated. As a result, teams don’t have to worry about their applications running out of space. The platform always searches for optimal instance options and spins up instances in a fraction of a second to ensure high availability.

CloudCheckr doesn’t automate spot instance selection and replacement at the moment.

4. Full multi cloud optimization

As more businesses turn to multiple cloud services to access best-in-class services and avoid disasters, the need to track, manage, and optimize costs across providers is more important than ever.

CAST AI meets this requirement with a host of multi cloud features:

  • Active-Active Multi Cloud – it replicates applications and data across multiple cloud services, so if one of them fails, others keep applications working and guarantee business continuity.
  • Global Server Load Balancing – CAST AI distributes traffic evenly across all the clouds in use, always picking endpoints that are up and healthy. 
  • Multi cloud visibility – the platform simplified cost allocation across cloud services by using universal metrics from Grafana and Kibana.

CloudCheckr currently doesn’t support multi cloud functionality and only offers cost visibility for AWS, Microsoft Azure, and Google Cloud.

5. Pricing

CAST AI users can start with the free Cluster Analyzer to check whether they can save on their cloud services. The read-only agent examines the configuration and provides actionable recommendations. You can apply them manually or use automated cost optimization features – in which case, you’ll have to select one of two plans: Growth and Enterprise. CAST AI guarantees at least a 50% reduction in costs.

CloudCheckr doesn’t provide pricing information on its website. However, according to AWS Marketplace (as of 09/2021), it charges 3% of cloud spend and $300 monthly minimum.

Summary – CAST AI vs. CloudCheckr

Overall winnerCAST AI

Both CloudCheckr and CAST AI are great cost optimization platforms that facilitate cost allocation, monitoring, management, and optimization.  

But while CloudCheckr offers recommendations and some automation features, CAST AI comes with a rich array of automated solutions that guarantee cost savings.

Combined with unique multi cloud functionality and cloud native architecture, CAST AI’s comprehensive automation features position the platform at the top of cloud cost optimization platforms.

P.S. If you prefer a hands-on approach, you can always run the free CAST AI Cost Analyzer to see what the platform could save you automatically.

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