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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
19th
Average Rating
8.6
Reviews Sentiment
7.5
Number of Reviews
7
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 July 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 (%)
CAST AI1.6%
PerfectScale1.4%
Other97.0%
Cloud Cost Management
 

Featured Reviews

DeepakReddy - PeerSpot reviewer
Sr.Devops engineer at Scaler
Automated cost controls have cut cloud waste and free our team to focus on new projects
CAST AI can be improved in that automation policies require careful tuning. Sometimes it can be confusing for non-technical people or managers who are not familiar with technical details. However, it is good for technical people who are already into DevOps or cloud engineering. Spot strategies may need adjustment for sensitive workloads. The reporting and UI part can be somewhat better. Technical support can also be improved. Documentation is somewhat unclear sometimes, but not everywhere. There are many pros here, including easy onboarding, simple deployment, and excellent Kubernetes visibility, strong spot instance automation, and automated right-sizing. These features are very good for our organization because they reduce a lot of cost and reduce a lot of manual effort. However, some things can be improved, such as automation policies that require careful tuning and may need somewhat more help. Spot strategies can be improved, and some UI and documentation can also be improved.
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."
"CAST AI has reduced approximately 40% of our AWS bills and AWS cloud bills, really impacting our organization positively and enabling us to use our cloud better."
"Overall, CAST AI has been a valuable addition to our Kubernetes platform operations."
"In the last Q2 result, because of using CAST AI, we have reduced our manpower, money, and cost by 20 to 30%, which indicates substantial funding reduction."
"Since adopting CAST AI, we achieved approximately 30-40% reduction in Kubernetes infrastructure costs."
"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."
"Since using CAST AI, we have achieved approximately 30 to 40 percent reduction in our Kubernetes infrastructure cost."
"CAST AI monitors the workloads in the cluster and optimizes the number of nodes needed, their CPU and their memory so that we pay as little as possible."
"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."
"PerfectScale made our Kubernetes optimization effortless; it found wasted resources, lowered our cloud costs, and improved performance almost instantly."
 

Cons

"I would like to see CAST AI improved with deeper and more intelligent answers and solutions, along with additional optimization and customization options."
"Perhaps improving the documentation a little would allow it to reach that rating, which has benefited me regarding that specific focus."
"CAST AI could be improved by adding some AI agent capabilities."
"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."
"I would like to see more granular reporting, deeper cost allocation insights, and additional customization options for optimization policies in CAST AI."
"CAST AI can be improved in that automation policies require careful tuning."
"The reason I did not give it a perfect score is that I would still prefer to see more advanced cost reporting and workload-level analytics."
"I think they should focus more on Kubernetes features that allow on-the-fly resource allocation without the need to restart services."
"At the beginning, the support was not very impressive."
"With their in-place optimisations, stateful set optimisation would be a great addition."
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Top Industries

By visitors reading reviews
Energy/Utilities Company
15%
Outsourcing Company
11%
Educational Organization
10%
Financial Services Firm
10%
Insurance Company
32%
Construction Company
27%
Healthcare Company
7%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business10
Midsize Enterprise2
Large Enterprise3
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for CAST AI?
CAST AI provides 80% to 85% accuracy. Because it is an AI system, sometimes it can make mistakes, but providing 80% to 85% accuracy is a pretty good number for any normal tool. It is good.
What needs improvement with CAST AI?
I would like to see CAST AI improved with deeper and more intelligent answers and solutions, along with additional optimization and customization options. The customization option in particular cou...
What is your primary use case for CAST AI?
My primary use case for CAST AI is Kubernetes cost efficiency, and since I handle the cloud system as well, CAST AI has been instrumental in helping me. We mainly use it to automate processes in AW...
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.
904,836 professionals have used our research since 2012.