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CAST AI vs Finout 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
Finout
Ranking in Cloud Cost Management
14th
Average Rating
8.4
Reviews Sentiment
7.0
Number of Reviews
5
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 Finout is 1.2%, down from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Cost Management Mindshare Distribution
ProductMindshare (%)
Finout1.2%
CAST AI1.6%
Other97.2%
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.
HarshShah2 - PeerSpot reviewer
DevOps Engineer at Veefin Solutions Ltd.
Centralized cost governance has enabled accurate Kubernetes and SaaS spend allocation
The best features in my experience with Finout are the MegaBill, combining multiple cloud providers and SaaS tools into the consolidated dashboard. Virtual Tagging, Kubernetes cost allocation, cost anomaly detection, and custom dashboards are all valuable features. Virtual Tagging paired with the MegaBill concept completely resolved our historic tagging gaps. If a legacy resource lacks physical AWS tags, we can virtually tag it in seconds inside Finout to fix our cost attribution immediately. The governance capabilities of Finout are very solid. Virtual tagging rules allow us to enforce strict cost boundaries. From a security standpoint, it connects using secure, read-only IAM roles and digests AWS cost and usage reports, meaning it does not pose an operational risk to our live application environments.

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 adopting CAST AI, we achieved approximately 30-40% reduction in Kubernetes infrastructure costs."
"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."
"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."
"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."
"In terms of money saved, organizations could very easily save anywhere from ten to thirty percent of their cloud costs."
"We achieved approximately thirty to forty percent reduction in total cloud and SaaS waste by identifying orphan resources and optimizing underutilized third-party licenses."
"We have achieved a twenty to thirty percent reduction in total cloud and SaaS waste by identifying orphaned resources and optimizing underlying third-party licenses, and we also saved hours of engineering time previously spent manually building cost allocation spreadsheets every month."
"Finout paid for itself within the first quarter by exposing several forgotten high-compute database instances and misconfigured data pipelines that were draining budget needlessly."
"We have achieved a twenty to thirty percent reduction in total cloud and SaaS waste by identifying orphan resources and optimizing underutilized third-party licenses."
 

Cons

"Perhaps improving the documentation a little would allow it to reach that rating, which has benefited me regarding that specific focus."
"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 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."
"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 insights, and additional customization options for optimization policies in CAST AI."
"While the visibility features are top-notch, I would like to see more actionable, automated cost optimization recommendations, and I would also love to see more advanced cost forecasting models."
"Finout's user interface is dense with data, which is great for power users, but simplifying the dashboard creation wizard for non-technical team managers would speed up adoption across the company."
"While its visibility features are top-tier, I would appreciate seeing more actionable, automated remediations and guardrails similar to tools that can actively scale down infrastructure automatically."
"The right sizing feature is okay. It could be used, but I feel its functionality isn't as strong as the cloud-native solutions, so GCP, AWS, and Azure."
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Top Industries

By visitors reading reviews
Energy/Utilities Company
15%
Financial Services Firm
12%
Educational Organization
11%
Media Company
9%
No data available
 

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...
What needs improvement with Finout?
The right sizing feature is okay. It could be used, but I feel its functionality isn't as strong as the cloud-native solutions, so GCP, AWS, and Azure. They all have better right sizing capabilitie...
What is your primary use case for Finout?
Our use case is integrating cloud costs from a multi-cloud estate to have one pane of glass for cost visibility. We use it for reports, but there's also other functionality that we've liked using, ...
What advice do you have for others considering Finout?
I would recommend them to use it. It's a good tool. The company is still quite new and young, but they're rapidly developing, and their support is great. The Finout team seemed like they could be f...
 

Comparisons

 

Overview

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