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CAST AI vs IBM Kubecost 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
8
Ranking in other categories
No ranking in other categories
IBM Kubecost
Ranking in Cloud Cost Management
22nd
Average Rating
9.4
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 IBM Kubecost is 2.8%, 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%
IBM Kubecost2.8%
Other95.6%
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.
DIRK UYTTERHOEVEN - PeerSpot reviewer
Senior Enterprise Architect at DV Consulting
Identifies and eliminates overprovisioning of expensive resources like storage, highly scalable and offers performance
I like the overall product because I can select what monitoring should be enabled and whatnot. In our case, we really focus on performance because it's clear that the price is related to most performance setups. So the more performance, the more expensive. So we look into the performance that the customer needs, and then based upon that feedback from the remote control, we change the parameters. And even the end user will not notice it is not using it, so we just make money without any impact on the end users.

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."
"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."
"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."
"We have achieved approximately 30% to 40% reduction in Kubernetes infrastructure cost with CAST AI, and we also reduced manual cluster management activities significantly, especially around node scaling and capacity planning."
"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."
"The price is reasonable, considering the value it delivers."
"It offers a detailed examination of your cluster, including the types of instances utilized, allocated CPU and RAM, and resource distribution for specific applications."
"I mostly like the dashboards."
 

Cons

"The limitations of CAST AI include reporting and customization options."
"I would like to see CAST AI improved with deeper and more intelligent answers and solutions, along with additional optimization and customization options."
"I would like to see more granular reporting, deeper cost allocation insight, and additional customization options for optimization policies in CAST AI."
"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."
"CAST AI could be improved by adding some AI agent capabilities."
"CAST AI can be improved in that automation policies require careful tuning."
"To improve CAST AI, I would like to see more granular reporting, deeper cost allocation insights, and additional customization options for optimization policies."
"Perhaps improving the documentation a little would allow it to reach that rating, which has benefited me regarding that specific focus."
"Faster monitoring could potentially improve overall stability in the production environment."
"The integration with other solutions could be improved."
"There is a significant potential for enhancing it through the incorporation of advanced technologies like AI and generative AI."
 

Pricing and Cost Advice

Information not available
"The cost of the tool may seem nominal compared to the potential savings in infrastructure expenses."
"The real savings come from using Kubecost features like autoscaling and serverless functions to optimize your resource usage. If you treat it like a data center migration without fine-tuning, it might cost more."
"The cost is cheap. Kubecost has an open-source core."
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Top Industries

By visitors reading reviews
Energy/Utilities Company
15%
Financial Services Firm
12%
Educational Organization
11%
Media Company
9%
Financial Services Firm
17%
Insurance Company
13%
Manufacturing Company
11%
Construction Company
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise1
Large Enterprise2
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for CAST AI?
In terms of pricing, I believe the pricing is reasonable because of the amount of savings and operational efficiency it delivers, making it easier to justify the investment. Organizations with larg...
What needs improvement with CAST AI?
The limitations of CAST AI include reporting and customization options. I think they can improve in these areas, especially when some advanced settings require a learning curve, particularly for te...
What is your primary use case for CAST AI?
Our main use case for CAST AI is Kubernetes cost optimization, automated node provisioning, and improving cluster efficiency. I can provide a specific example of how we use CAST AI for Kubernetes c...
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Comparisons

 

Also Known As

No data available
Kubecost - Amazon EKS cost monitoring
 

Overview

Find out what your peers are saying about CAST AI vs. IBM Kubecost and other solutions. Updated: June 2026.
902,988 professionals have used our research since 2012.