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CAST AI vs PerfectScale comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

CAST AI
Ranking in Cloud Cost Management
39th
Average Rating
8.6
Reviews Sentiment
7.5
Number of Reviews
3
Ranking in other categories
No ranking in other categories
PerfectScale
Ranking in Cloud Cost Management
25th
Average Rating
9.4
Reviews Sentiment
1.7
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2026, in the Cloud Cost Management category, the mindshare of CAST AI is 1.6%, down from 1.9% compared to the previous year. The mindshare of PerfectScale is 1.4%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Cost Management Mindshare Distribution
ProductMindshare (%)
PerfectScale1.4%
CAST AI1.6%
Other97.0%
Cloud Cost Management
 

Featured Reviews

Udit Parekh - PeerSpot reviewer
DevOps Engineer at Veefin
Automation has optimized our kubernetes costs and continuously improves cluster efficiency
CAST AI has positively impacted our organization because we are now able to control our Kubernetes costs, and the automated node provisioning continuously monitors our application usage to select which node to provision, ensuring the application has sufficient compute power and improving our cluster efficiency. In terms of cost savings, we have currently reduced our costs by 30 to 40%, and it saves time while managing infrastructure because it continuously monitors and provides the nodes to the application, so we don't need to do anything ourselves. This is a fully automated process. Additionally, manual intervention has decreased significantly because this is a completely automated process.
reviewer2750058 - PeerSpot reviewer
DevOps & FinOps Engineer at a tech vendor with 501-1,000 employees
Gain visibility into Kubernetes clusters and optimize resource allocation based on historical data
I think they should focus more on Kubernetes features that allow on-the-fly resource allocation without the need to restart services. They should implement this in their autoscaler to make it more useful in scenarios that require immediate scaling up or down. They should also offer more options for visualizing graphs in different ways, such as tabular views.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"CAST AI's machine learning algorithms that listen to the data generated by the cluster in order to optimize the workloads are among the best features offered."
"Since using CAST AI, we have achieved approximately 30 to 40 percent reduction in our Kubernetes infrastructure cost."
"In terms of cost savings, we have currently reduced our costs by 30 to 40%, and it saves time while managing infrastructure because it continuously monitors and provides the nodes to the application, so we don't need to do anything ourselves."
"PerfectScale made our Kubernetes optimization effortless; it found wasted resources, lowered our cloud costs, and improved performance almost instantly."
"The cluster and workload autoscaler gives us the ability to have control over all the workloads' resources instead of managing them one by one."
"Automated resource optimization using different policies based on the environment enabled the organization to achieve infrastructure cost savings."
 

Cons

"Perhaps improving the documentation a little would allow it to reach that rating, which has benefited me regarding that specific focus."
"The limitations of CAST AI include reporting and customization options."
"To improve CAST AI, I would like to see more granular reporting, deeper cost allocation insights, and additional customization options for optimization policies."
"With their in-place optimisations, stateful set optimisation would be a great addition."
"At the beginning, the support was not very impressive."
"I think they should focus more on Kubernetes features that allow on-the-fly resource allocation without the need to restart services."
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Top Industries

By visitors reading reviews
No data available
Insurance Company
32%
Construction Company
27%
Healthcare Company
7%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for CAST AI?
In terms of setup cost, licensing, and pricing, I find the experience good. It's enterprise-grade, and the pricing is usage-based with no heavy upfront setup cost, which makes the onboarding straig...
What needs improvement with CAST AI?
The documentation of CAST AI can definitely be improved for first-time users. When we are onboarding a new user, the team needs some time to tune the policies and build confidence in automation bec...
What is your primary use case for CAST AI?
Our main use case for CAST AI is that we use it as a cloud provider and for Kubernetes clusters. We are using secure access roles and all those requirements for right-sizing the containers' workloa...
What needs improvement with PerfectScale?
With their in-place optimisations, stateful set optimisation would be a great addition.
 

Comparisons

 

Overview

Find out what your peers are saying about CAST AI vs. PerfectScale and other solutions. Updated: June 2026.
900,644 professionals have used our research since 2012.